literature review for transportation

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Preferences in regional public transport: a literature review

• Joel Hansson   ORCID: orcid.org/0000-0001-6478-0091 1 , 2 ,
• Fredrik Pettersson   ORCID: orcid.org/0000-0002-4991-5100 1 , 2 ,
• Helena Svensson   ORCID: orcid.org/0000-0003-3373-0037 1 , 2 &
• Anders Wretstrand   ORCID: orcid.org/0000-0002-7394-4534 1 , 2  

European Transport Research Review volume  11 , Article number:  38 ( 2019 ) Cite this article

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The purpose of this article is to analyse quality attributes of regional public transport and their influence on modal choice, demand, and customer satisfaction through a literature review. The review is based on a working definition of regional public transport with boundaries toward local as well as interregional public transport: Regional public transport (i) targets passengers travelling between separate urban areas or to rural areas and (ii) a majority of the trips are made on a regular basis. Our results suggest that preferences of regional travellers mainly conform to the preferences of local travellers, but some important differences have been revealed. Most notably, on-board comfort is a higher priority for regional travellers and is increasingly important with longer travel times. Network coverage and coordination are also more prominent features of regional public transport, presumably due to the more dispersed nature of regional public transport networks. These differences, and the fact that the prerequisites for regional public transport are in general substantially different compared to local and interregional public transport, support continued use of this categorisation in public transport research. We also conclude that there is a requirement for more knowledge about the specifics of regional public transport, as public transport research, thus far, has been largely focused on local travel. Research areas of particular interest are on-board comfort, operational aspects, travel time improvements, how the environmental impact of public transport services affects modal choice, and the influence of trip length on passenger preferences.

1 Introduction

Settlements in rural areas around cities continue to expand across the developed world [ 1 , 2 ]. Commuters living in these areas predominantly use private cars to travel to the cities, adding to congestion, parking and environmental problems and putting the city centre’s transport network under continuous strain [ 3 ].

Also, for issues regarding greenhouse gas emissions and energy consumption in the transport sector, addressing regional travel is of key importance. In general, short trips are more frequent than longer trips, but the total mileage is dominated by medium and long-distance trips. Moreover, this dominance is likely to increase in the coming decades. According to forecasts produced by the International Transport Forum, non-urban travel demand will grow faster than urban travel demand in terms of passenger kilometres [ 4 ]. In their baseline scenario, CO 2 emissions from the non-urban passenger land transport sector is expected to double between 2015 and 2050. To be able to prevent this, incentives for modal shift as well as improved rail and bus services are needed alongside fuel efficiency improvements and increased use of alternative fuels [ 5 ].

The recent transport policies and policy-related transport research trends, focusing on replacing (passive) car travel with more active modes like walking, cycling and public transport (e.g. [ 6 ]) may suggest that there will be increased need for better regional public transport services. Active living and travel policies target older people [ 7 ], children and adolescents [ 8 ] as well as the average working population and workplace interventions [ 9 ]. Sedentary lifestyles thus affect all generations, and regional public transport services with walking and cycling as first and last mile-solutions, may play an ever so important role in the future.

For many regional trips, walking and cycling are not viable options, meaning that public transport is often the only alternative to the car for such trips. People in rural areas are at a risk of being excluded from participation in normal relationships and activities if they do not have a driver’s licence or access to a car [ 10 ]. The quality of regional public transport services impact the independent mobility of this group, particularly children and adolescents [ 11 ].

Despite this, public transport research is mainly focused on local travel. In a bibliometric analysis, Heilig & Voss [ 12 ] give insight into the field from a meta-perspective. Their keyword analysis indicates that there are few publications that exclusively concern regional public transport, at least compared to research into local public transport. Five of the 59 most used keywords contain urban (urban planning, urban traffic, urban areas, urban development, and urban area). None of the listed keywords relate directly to regional public transport: keywords containing terms such as regional , rural or interurban are absent.

The underrepresentation of regional public transport studies is also acknowledged by de Oña & de Oña [ 13 ] specifically for studies on public transport service quality. While there are numerous publications about service quality analyses in the urban transport sector, as well as for air transport, “the analysis of quality is still found to be waking up in the world of interurban land transport services” ([ 14 ], p. 9).

Previous literature reviews have pointed out quality attributes commonly found to be important in local public transport. For customer satisfaction, four quality attributes are of particular importance: frequency, travel time, safety, and punctuality [ 14 ]. In addition, costs, staff behaviour, on-board cleanliness and comfort are also commonly discussed as important factors that influence customer satisfaction and loyalty [ 15 ]. In terms of attracting car users, though, the factors of importance essentially depend on the context and characteristics of the target group [ 16 ]. Modal choice is also considerably affected by factors outside the public transport system, e.g. parking availability in the urban area in question [ 17 ].

Public transport mode may also affect the passengers’ preferences. For local public transport, rail modes display a more complex pattern of priority areas, compared to bus services, when comparing studies in different settings. For bus services, many studies include similar aspects in the conclusions regarding important quality attributes (see [ 14 ]). Bus services are overrepresented in studies into customer satisfaction in local public transport [ 15 ], but when comparing the modes, passengers generally prefer rail modes, such as metro and light rail [ 18 ]. However, through implementation of high-quality bus concepts such as Bus Rapid Transit, bus services in urban areas can attract as many passengers as light rail and metro, and lead to significant modal shifts similar to those found when implementing rail-based systems [ 19 ].

The general characteristics of local travel are different from regional travel (longer distances, often lower service frequencies, fewer stops, etc.), and similarly there are important differences between the regional and interregional levels. These characteristics impact the preferences of potential and existing passengers, e.g. expressed on an urban–interurban scale [ 20 ] or a short distance–long distance scale [ 21 ].

The aim of this paper is to review research into public transport for regional travel, focusing on important factors for increasing the modal share of public transport in relation to the private car. Because few service quality studies deal explicitly with the effects on modal split, studies concerning demand and customer satisfaction are also included in the review. Customer satisfaction is correlated with demand and modal choice [ 22 ], but it is also acknowledged that these are separate concepts, to some extent affected differently by different policies [ 23 ]. Furthermore, customer satisfaction is not solely based on the actual conditions of the transport system [ 24 , 25 ]. The perception of the public transport system is moderated by customer characteristics, situational conditions such as regional or urban setting, and passenger expectations [ 26 ]. However, this review includes studies from different contexts in different parts of the world, from suburban rail networks around large cities to bus services in rural settings. We will thereby be able to study patterns beyond the situational conditions, and demonstrate the service attributes that are most commonly found to significantly impact customer satisfaction, demand, and modal choice.

From the review, we will attempt to answer the following research questions:

What similarities and differences between regional and local public transport are evident with regards to important quality attributes?

Are there any quality attributes whose importance depends on travel time or distance? This question relates to the differences between local and regional public transport but has a broader perspective, aiming to also reveal differences between diverse types of regional travel.

Are there any evident differences between bus and rail services in terms of important quality attributes?

Do the results of studies into public transport demand and modal choice conform to the results of customer satisfaction studies?

The focus of this review is on conventional bus and rail services (along fixed routes and with fixed schedules) serving regional travellers. It is acknowledged that regional public transport has a very diverse service portfolio [ 3 ], ranging from demand-responsive services to high-capacity regional rail systems. However, the vast majority of regional passengers use conventional bus and rail services [ 27 ].

2 Definitions

2.1 service quality attributes.

The reviewed studies have different objectives and use different data sources. Consequently, each study assesses different variables. As a framework for structuring the analysis, we have used the categorisation presented in the EU standard EN 13816:2002 [ 28 ]. This standard provides an extensive list of service quality attributes, grouped into eight areas:

Availability. Extent of the service offered in terms of geography, transport modes, operating hours, and frequency.

Accessibility. Access to the public transport system including interface with other transport systems.

Information. To assist the planning and execution of journeys, under normal conditions as well as under abnormal conditions such as delays.

Time. Length of trip time. This area also includes adherence to schedules in the form of punctuality or regularity.

Customer care. Customer interface, staff behaviour and attitudes, and ticketing options.

Comfort. Service elements that make journeys relaxing, enjoyable, or productive, e.g. through station facilities, seating and personal space, ride comfort, vehicle condition, atmosphere, and complementary services such as on-board Wi-Fi.

Safety. Sense of personal protection from crime and accidents.

Environmental impact. Environmental impact resulting from the provision of the public transport service.

In order to include in the analysis all internal factors over which public transport managers exercise a certain level of control [ 29 ], cost has been added to the framework. Cost or fare level is also a commonly discussed policy attribute.

2.2 Local, regional and interregional public transport

This paper explores the differences between various geographic scales: local, regional and interregional. However, the boundaries between these geographic scales are unclear and need to be defined in order to conduct the review.

A previous attempt to define regional travel – and hence local and interregional travel – has been made by the UITP [ 3 ]. It notes that a single definition is “difficult to establish due to the great diversity that exists within regional transport” (p. 1), and as a result, its definition is somewhat ambiguous. Interestingly, the notion of captive riders is included, possibly indicating the challenge in designing attractive regional public transport services: “Regional public transport covers all collective passenger transport services excluding most public transport within cities and urban centres. In general, regional transport services bring captive riders from lower-density and suburban areas to larger city centres and serve small- and medium-sized cities” ([ 3 ], p. 1).

A number of other previous publications touch upon definitions of terms related to regional public transport. White [ 27 ] adopts the definition of rural transport used in the National Travel Survey in the UK. It concludes that rural areas comprise settlements below 10,000 people or are open countryside. However, White notes that for transport planning purposes, small towns are often also served by rural networks, providing interurban links to larger regional centres of employment, shopping, etc. Village-to-town and town-to-town movements are in many cases served by the same routes.

Exurban is another related term, used by Petersen [ 30 ], who writes about public transport for exurban settlements in Australia. He presents two alternative definitions of exurban: “Beyond the suburbs, the Australian exurban region is defined by [ …] the region surrounding an urban area, bounded on the outer by how far commuters are willing to travel, and on the inner by contiguous urban or suburban development” (pp. 24–25). Alternatively, exurban areas can be defined as “the mainly small town and rural regions within 150 kilometres radius from the state’s capital and largest city” ([ 30 ], p. 25).

A systemisation of bus services in three categories has been made by Godlund [ 31 ], as a tool to describe the development of bus services in Sweden from the early years of the twentieth century to the 1950s. The network is categorised into α services , β services and γ services . The first category, α services , include urban and suburban lines, defined by a maximum distance of 6 km from an urban area. β services include rural to urban services, interurban services with intermediate rural stops, as well as purely rural services. Finally, γ services are interurban express lines.

Interurban bus services have been explored in a number of case studies by Luke, Steer & White [ 32 ], describing the current state and future development of such services in the UK. For this purpose, they adopt a working definition of interurban bus : “two or more urban areas (typically towns, but might be cities) are linked by a bus service with intermediate stops typically to serve villages en route” (p. 1).

To conclude, existing definitions of regional public transport and related terms such as rural, exurban and interurban are somewhat vague and use various sets of metrics. Besides demographics, the definitions are based on elements such as travel distances or stopping patterns of the public transport services.

In order to sort the terms, we conducted a survey among public transport professionals about their perception of the concept of regional travel (see [ 33 ]). The results emphasise the importance of maintaining a distinction between functionality and technology, meaning that factors such as vehicle type or speed should not be included in the definition. Instead, the survey indicates a clear preference for either an administrative or a functional definition, depending on the purpose.

In the administrative version, regional travel is defined by administrative boundaries, i.e. on the outer boundary by county limits, or an agglomeration of counties, and on the inner boundary by city, town or municipality limits. The functional definition is, instead, based on the notions of urban areas and regular travel . Local travel is within an urban area and consequently some portion of a regional trip takes place outside an urban area. For the outer boundary, the functional definition focuses on travel patterns: regional trips are made on a regular basis, daily to weekly in general. This frequency range is based on the survey responses, expressing a need to include also other trip purposes than commuting in the definition of regional travel, such as travel for daily or weekly leisure activities and shopping needs [ 33 ].

For practical reasons, trips made on a regular basis can be roughly interpreted as trips within a certain distance or within a certain travel time. However, it is hard to draw a general conclusion about the quantification of such distance or time limits, as they are likely to depend on the national or regional context [ 33 ].

For both the (outer) regional-interregional and the (inner) local-regional boundaries, the survey respondents’ preferences for the administrative and the functional definitions are roughly equal. One approach could then be to combine these criteria using logical conjunction or disjunction (AND/OR).

In most cases there is a large overlap between the two criteria. As for the regional-interregional boundary, most of the trips made on a regular basis are almost certainly within an administrative unit such as a county or an agglomeration of counties, and vice versa. As for the local-regional boundary, most trips to and from an urban area will cross an administrative boundary such as city, town or municipality limits.

To be able to determine how to combine the criteria, we need to examine what lies outside the overlaps. As for the regional-interregional boundary, the administrative definition will include trips that are not made on a regular basis, especially if the administrative region is large. At the other end of the Venn diagram (see Fig.  1 ), trips made on a regular basis but crossing a regional (or national) administrative boundary, even between towns just a few kilometres apart, are excluded in the strictly administrative definition.

Venn diagram for the outer boundary of regional travel. Regular travel means that trips are made daily to weekly in general

Since this project focuses on passenger preferences and factors that influence ridership on regional public transport services, it is reasonable to exclude long-distance travel within large administrative regions from the definition of regional travel. However, regular travel that crosses administrative boundaries shares key characteristics with other regular travel, and should therefore be included. This means that a combination of administrative and functional criteria is superfluous. The criterion of regular travel is sufficient in itself, for our purpose, and will, moreover, include a majority of the trips that are regional according to the administrative criterion.

The line of reasoning is similar for the local-regional boundary. Trips that cross an administrative boundary but that are carried out within an urban area are local in terms of passenger preferences. At the other end of the Venn diagram (see Fig.  2 ), trips which, to some extent, pass outside urban areas but are within a local administrative entity have much in common with other interurban or urban-to-rural trips. Analogous to the outer boundary, this means that the functional criterion is sufficient for our purpose and will also in this case include a majority of the trips that are regional according to the administrative criterion.

Venn diagram for the inner boundary of regional travel

Thus, for the purpose of exploring passenger preferences, the following working definitions have been adopted in this review:

Local public transport carries passengers within an urban area. The definition is based on urban areas instead of density or urban centres, implying that travel between different parts of a conurbation (belonging to the same urban area) is local rather than regional.

Regional public transport targets passengers travelling between separate urban areas or to rural areas, and a majority of the trips are made on a regular basis (daily to weekly, in general). This means that regional travel is not defined as being within a certain geographic area, but is instead based on travel patterns in each case.

Interregional public transport targets passengers travelling between regions as defined above. Thus, the majority of the trips are made less frequently than weekly.

The definitions focus on travel patterns rather than individual trips. On a regional public transport service, the majority of passengers travel between separate urban areas or to rural areas, and the majority of these trips are made on a regular basis. This implies that most passengers on regional services are frequent travellers, but not necessarily all of them.

This study uses the PRISMA method [ 34 ] to identify and systematically analyse relevant literature on important service quality attributes in regional public transport. The PRISMA method is chosen due to its structured, iterative process for identifying a comprehensive set of studies that meet the specified aim of this study [ 34 ].

The process includes three phases (Fig. 3 ): First, a set of literature is gathered through an extensive search in Scopus and TRID – Transport Research International Documentation. In the second phase, the identified literature is assessed using a set of inclusion and exclusion criteria. Titles, abstracts, and full-text articles, respectively, are assessed, narrowing down the literature list in each step. Before the full-text assessment, literature found via citation searches is added to complement the literature identified in the database searches. The third and final phase of the process is a qualitative synthesis of the selected literature.

Process for identification of relevant literature (adapted from [ 34 ])

In order to identify relevant search terms, the research subject was broken down into three main concepts: ridership , regional , and public transport . For each of these concepts, synonyms, broader terms, narrower terms, and related terms were identified through a combination of citation searches and using the Transportation Research Thesaurus [ 35 ]. The resulting search terms, listed in Table  1 , were combined using building block searches. In addition, the searches were limited to literature written in English and published before July 2018 when the search was conducted.

The titles, abstracts, and finally the full-text literature have been assessed using a number of inclusion and exclusion criteria. Besides the language and publication date limitations mentioned above, we have used three relevance criteria to narrow down the results. Firstly, the study case must be in line with our definition of regional public transport. In studies covering more than just regional public transport, for example, both local and regional public transport in a metropolitan area, results regarding regional travel have to be explicitly reported. Secondly, the study must cover one or multiple service quality attributes and their influence on modal split, ridership, or customer satisfaction. Thirdly, we only include studies about conventional modes of regional public transport, i.e. train and bus services along fixed routes and with fixed timetables. Paratransit and demand-responsive services, as well as air services, have been excluded.

Thirty-seven studies were selected for review (references [ 20 , 24 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 ]). The selection is centred upon recent publications: 31 of the studies have been published during the last 10 years and 23 of them as recently as during the last five-year period. This pattern was also evident in the initial search results, indicating an increasing trend for research into service quality in regional public transport.

Studies from different geographic contexts are included in the review, from the suburbs and rural areas around Mumbai [ 36 , 37 , 38 ] and Shanghai [ 39 ] to small-town regions in Texas [ 40 ] and southern Italy [ 24 ]. These differences in city and region size mean different realities in the public transport systems. For instance, crowding is a common issue around large cities (see [ 38 , 41 ]), while other types of comfort-related attributes are more important in less densely populated areas (see [ 20 , 40 , 42 , 43 ]). Geographic context is indicated in the review when relevant.

Fourteen of the selected studies comprise evaluations of multiple quality attributes, enabling suggestions about the relative importance of these attributes. These studies are outlined in section The relative importance of quality attributes , providing an overview of important quality attributes in regional public transport. The focus of this section lies on a quantitative assessment of these 14 studies. The remainder of the studies that were identified in the search process are focused on specific attributes, providing more in-depth information about various aspects of regional public transport networks. Results from a qualitative assessment of these studies, and of the 14 “overview studies”, can be found in sections Cost to Comfort . The structure of this part of the review reflects the content of the selected studies, sorted into some of the categories used in section The relative importance of quality attributes . In section Research gaps we return to the full framework of attribute categories (see section Service quality attributes ) in order to identify gaps in the literature.

4.1 The relative importance of quality attributes

Results from the 14 studies comprising evaluations of multiple quality attributes are summarised in Table  2 . In the table the results are visualised through quality attribute categories (see section Service quality attributes ) that are discussed by the authors as being important or found to have major impacts in the analyses. In most cases, each category included comprises several more specific quality attributes. For instance, the category “Availability” may for a specific study include attributes concerning frequency as well as attributes about network coverage. To be able to separate these attributes, a more fine-grained analysis have been carried out. For reasons of clarity, however, the results are presented on category level in Table 2 . It should also be noted that the notions for similar quality attributes vary between the studies, and in those cases we have made interpretations according to the framework described in section Service quality attributes .

For each publication, public transport modes included in the study are specified, as well as the output variable: modal choice, demand, or customer satisfaction. We have also made rough estimations of average travel distances in the samples, as they are usually not explicitly reported in the studies, and assigned each study to one of the three distance categories: short (less than 25 km, 6 studies), medium (25–50 km, 3 studies), and long (more than 50 km, 4 studies). The three distance categories have been chosen based on the availability of results from the investigated studies. One of the studies covers multiple geographical scales. Thus, it has not been assigned to a distance category.

In addition, the type of data used in each study is also specified in Table 2 . The most commonly used data sources in the selection of studies comprise different types of stated preference surveys and customer satisfaction surveys, often in combination. It should be noted that the notion of stated preference is used here as a wide concept, ranging from directly stated importance of different attributes to discrete choice experiments. Revealed preference data is only used in three of the studies, through ridership data or a survey.

Cost, availability, time, and comfort are the most commonly explored categories, covered in almost all of the selected studies. Most of the other categories are also quite well represented, as they are included in at least half of the studies. The exception is environmental impact, which is included in only two of the studies.

Three of the categories are mentioned in the discussion of the most important variables in most of the studies in which they have been included:

Availability, whereof frequency of service appears to be of particular importance. Frequency is the most common individual attribute in the discussions about important attributes. Another availability attribute commonly discussed is network coverage (and walking distance).

Time, whereof reliability and punctuality are the most common attributes mentioned in the discussions. Travel time also appears in the discussion about important attributes in more than half of the studies in which it has been included.

Comfort, which is typically represented by different attributes related to on-board comfort, such as crowding, cleanliness, ventilation, vehicle condition, etc. A number of studies also find station facilities to be of importance.

A couple of the studies include comparisons of local and regional travel. Majumdar & Lentz [ 40 ] suggest that the results in rural areas do not differ very much from the results in urban areas. In contrast, Stern [ 51 ] concludes that the urban and rural population ascribes different preferences to service attributes. Román, Martín, & Espino [ 20 ] find that interurban passengers place more emphasis on comfort, and less on frequency, than urban passengers. In fact, comfort is found to be the most important attribute for interurban passengers, and frequency is a top priority for urban passengers.

A similar pattern is evident in the comparison between short, medium, and long regional trips in Table 2 . All studies of medium and long regional trips include some aspect of on-board comfort in the discussion of important attributes. However, in the studies of short regional trips these attributes are absent in the discussion (a couple of these studies instead find station facilities important, which is another aspect of the comfort category). Additional support for this finding is expressed by Bouscasse, Joly, & Peyhardi [ 36 ] through a positive crossed effect between travel time and comfort, which means that the value of comfort increases as travel time increases.

In addition, the results indicate that cost is a more important attribute for medium and long regional trips than for short regional trips.

The opposite could be said for information, which is found to be important in all but one of the studies of short regional trips (where included), but in none of the studies of medium and long regional trips. A similar tendency is also found for frequency and reliability, although not as pronounced.

In terms of differences between bus and rail services regarding the relative importance of quality attributes, no conclusions can be drawn from the studies in Table 2 due to the low number of such studies that focus entirely on rail services.

The results of studies into modal choice and demand largely conform to the results of the customer satisfaction studies. A couple of differences can be discerned: more emphasis is placed on frequency and reliability in the customer satisfaction studies. However, these differences are not convincingly transparent and there is also an overrepresentation of customer satisfaction studies regarding short regional trips and bus users. Hence, it is hard to determine whether the differences relate to the type of study, travel distances, public transport mode, or to all of those.

Firstly, it is important to note that cost cannot be seen as an isolated entity, it is rather the relationship between quality and price that affects users the most [ 50 ].

In terms of price elasticity, the demand for regional public transport services is inelastic, i.e. typically between -1 and 0 [ 37 , 45 , 52 ], and the demand adjusts very quickly after any change in fare levels [ 37 ]. Price elasticity differences between bus and rail services are found to be minimal [ 36 , 52 ] and no significant difference has been detected between elasticities of demand for day and season tickets [ 52 ]. Furthermore, Stark [ 52 ] found that price elasticities may vary with distance – absolute elasticity values fell with increasing distance up to around 20 km and then rose again. However, it should be noted that the number of cases in this study was too low for a more general conclusion to be justified.

Integrated local–regional fare systems are crucial in order for users to access a coherent public transport network at a reasonable cost. Rashedi, Mahmoud, Hasnine, & Habib [ 43 ] revealed that eliminating additional costs for regional transit users when using local transit for access or egress is an effective strategy for improving the modal share of public transport for regional commuting trips.

4.3 Availability and accessibility

4.3.1 planning and organising regional public transport networks.

The coordination of public transport systems, not only through integrated fares but also through integrated ticketing and coordinated transport planning, marketing, and customer information, is a foundation for providing an attractive alternative to the car. Buehler, Pucher, & Dümmler [ 53 ] suggest that such coordination, in the form of so-called Verkehrsverbund, is a part of the explanation of why the modal share of private cars has fallen since 1990 in many German, Austrian, and Swiss metropolitan areas. In all six of their case studies, they argue that the integrated public transport associations have increased the quality and quantity of services, attracted more passengers, and reduced the proportion of costs covered by subsidies.

Coordination is also a key element for making public transport attractive and cost-effective outside the most densely-populated areas. Despite low frequencies, it is possible to create an attractive public transport network through rigorous coordination and central network planning [ 54 ]. Integrated timed-transfer systems with pulse timetables can operate with more than adequate levels of cost recovery and vehicle occupancy even in rural regions with very low population densities [ 55 ]. Demand-responsive services may enable larger areas to be covered, to meet planning objectives of ensuring a minimum of level of service. However, conventional interurban services between towns can provide a more cost-effective way of serving rural areas in which smaller settlements are concentrated along corridors [ 55 , 56 ].

4.3.2 Regional public transport modes: bus versus rail

The coordination of public transport networks also includes the coordination of modes. Brown & Thompson [ 57 ] argue that the combination of a rail service backbone and a multi-destination service strategy – feeder services to rail stations rather than parallel direct services – is a prosperous approach. Easy and well-timed transfers are fundamental to making this strategy successful [ 54 , 55 , 57 ].

A couple of studies indicate that trains are preferred to buses in general, or that bus or coach services are considered to complement rail services [ 58 , 59 ]. There are exceptions in which coaches can achieve comparable trip times to rail [ 59 ] though the perception of the modes is not only based on performance and cost criteria. There are also other more subjective criteria related to travel emotions or sensory aspects that might explain the differences in perception [ 58 ].

Bazley, Vink, & Blankenship [ 60 ] describe the concept of “Rural Bus Rapid Transit” as an alternative to rail in rural settings. Such a concept was inaugurated in 2013 between Aspen and Glenwood Springs, USA, covering a distance of approximately 70 km with 13 bus stations. The concept comprises high standards of operations as well as vehicles and station facilities. A few months after introduction, ridership had increased by 22% and, according to a customer satisfaction survey, this is mainly due to station locations, frequency, bus comfort and safety.

4.3.3 Access and egress

Integration of local and regional public transport networks is described above as an important factor for the availability of the system. In the design of the system, this needs to be balanced with other modes of access and egress to regional public transport stations. For instance, Akbari, Mahmoud, Shalaby, & Habib [ 61 ] suggest that the number of bus stops should be limited within the walking catchment area of a station, for the benefit of walking access. They define the walking catchment area as an 800-m radius around the station.

Four hundred or eight hundred meters, roughly a 5- or 10-min walk, are conventionally assumed distances for walking catchment areas, but there are studies which demonstrate that many passengers are willing to walk substantially further. For instance, data from regional buses around Amsterdam show that the 90-percentile of the walking access distance is somewhere in the range of 1,200–1,500 m [ 62 ]. Walking distances to suburban rail stations in Perth, Australia, are even longer, with significant numbers of passengers walking as far as 2–3 km to the stations [ 63 ]. However, it should be noted that characteristics of the built environment significantly affect trip production by walk access [ 61 ].

Higher speed and frequency of public transport services increase the catchment area, although this effect is not as apparent for walking as it is for cycling. The 90-percentile of the catchment radius for high-quality bus services around Amsterdam (“R-Net”) is almost double the catchment radius for conventional services (“Comfortnet”), 3,000 m vs 1,500 m. For walking access, the differences are not as evident. Also, the bicycle is a more important access mode for the high-quality system than for conventional bus services. The mode share of the bicycle is significantly higher for access to “R-Net” than for access to “Comfortnet” [ 62 ].

For car access there are also indications of a positive relationship between quality of service and catchment area. Higher frequencies mean that people are willing to drive longer distances [ 64 ]. Additionally, driving distances to stations are affected by overall trip lengths, i.e. longer public transport trips mean longer average driving distances to stations [ 64 ].

In general, the importance of car parking at stations, or park-and-ride facilities, increases with distance from the destination [ 65 , 66 ]. Accessible and inexpensive car parking at stations can encourage motorists to shift to public transport modes for part of their trip [ 65 ] and make daily travel easier for many commuters. However, offering passengers free parking at stations also has negative consequences and, in most cases, free park-and-ride cannot be assumed as a measure of reducing traffic volumes or greenhouse gas emissions from traffic [ 66 ]. Traffic volumes may even increase if a park-and-ride facility occupies a site which has an alternative use that could contribute to less transport demand and traffic, or if the park-and-ride facility means car journeys replace walking, cycling or using public transport to access the station [ 66 ].

Monetising parking at stations will stop some regional travellers from using public transport. Nevertheless, it is a way of managing parking demand and improving access times for park-and-ride users [ 43 ]. Reasonable daily parking charges (compared to the cost of driving to considerably more expensive parking facilities at the destination) can also provide sufficient capital to build and operate new park-and-ride capacity without subsidies from other revenue sources [ 67 ].

4.4.1 Reliability

In many of the reviewed studies reliability is equalled with punctuality, even though reliability often is understood as a wider concept. The EU standard EN 13816:2002 [ 28 ] (see section Service quality attributes ) separates punctuality – on-time performance – from regularity – maintaining headways – but this differentiation is absent in the reviewed studies. However, there are some cases where the concept of reliability is diversified in other ways. Reliability of connections is sometimes separated from general punctuality, and it is shown that reducing the probability of delays is particularly important for passengers with transfers [ 46 , 59 ]. Another aspect is the occurrence of cancelled runs, which self-evidently may come out as an important facet of reliability in studies where it is separated from on-time performance [ 24 ]. In a broader sense, reliability can also be affiliated with concepts such as permanence and simplicity, implying a general preference for carefully planned, fixed public transport routes combined in a stable network [ 55 ].

Still, punctuality is the most common measure of reliability, and it is shown that delays significantly impact the level of passenger satisfaction [ 68 ]. Higher probability of delays also decreases the probability of choosing public transport modes [ 39 , 43 ]. The importance of the issue increases with low frequencies [ 59 ]. Also, evidence from the suburban railway network in Paris suggests that passengers travelling for other purposes than commuting value punctuality even more than commuters [ 69 ]. A related issue is communicating information about service disruptions, which is highly valued among all passengers [ 41 , 69 ]. Specifically, explicit information about the expected duration of delays is particularly appreciated [ 69 ].

4.5 Comfort

The notion of comfort covers a wide range of quality attributes, for example, station facilities, crowding, noise, ride comfort, etc. In general, on-board facilities have a higher impact on perceived service quality in regional public transport than station facilities [ 68 ]. Complementary services such as on-board Wi-Fi may also significantly impact modal choice in favour of public transport [ 70 ].

Depending on passenger flows, crowding is an important factor to take into account when designing regional public transport services. In rural areas, passenger flows are generally relatively low. Thus, frequencies are largely determined by other aspects, i.e. balancing operational costs and waiting times [ 43 ]. In more densely populated areas, however, crowding is a critical factor. As the level of crowding increases, the total perceived in-vehicle travel time increases [ 38 ]. Females tend to perceive a higher reduction in utility due to crowding than males. Similar preferences are observed for higher income groups [ 38 ]. However, the effects of crowding are not inherently or unavoidably negative. A crowd can be a source of entertainment, fun, and friendship [ 36 ]. Thus, it is important to understand the specifics of the crowd, such as the density, passenger perceptions and culture.

4.6 Research gaps

Returning to the framework of quality attribute categories (see section Service quality attributes ), cost, availability, time, and comfort are the most commonly explored categories, covered by more than half of the reviewed studies (Table 3 ).

Availability is the most widely covered category, with a focus on modes (bus vs rail) and network (coordination, transfers, access and egress). However, elaborations of the operational aspect of availability, i.e. operating hours and frequency, are missing.

Regarding the time category, the reviewed studies focus on reliability. Elaborations of travel time, for example, studies about travel time improvements in regional public transport, are absent.

The reviewed studies on comfort are mainly centred on crowding. As mentioned above (section Comfort ), there are many further aspects of comfort that are yet to be investigated.

Attributes regarding accessibility, information, customer care, security, and environmental impact are touched upon in some publications, but without any further elaboration. The least explored category, environmental impact, and its effect on modal choice, ridership or customer satisfaction, is mentioned in only three of the 37 publications.

5 Discussion

The objectives of this review were framed in four research questions, discussed in the following sections, together with some suggestions for future research. Many of the results from this review are indicative rather than conclusive, due to the little amount of previous research in the topic. The fact that the reviewed studies differ in methods used and factors controlled for also limits the possibility for solid conclusions. Still, the review provides an overview of quality studies conducted in regional public transport and demonstrates some quality attributes that are recurringly found to be important in these studies.

It should be noted that there is a variation in the reviewed studies regarding how quality attributes are defined and used. This complicates the assessment, but through transformation to the framework presented in section Service quality attributes we have been able to compare studies and reveal some overall patterns. The discussion and conclusions are based on this framework, but the original studies might use different notations.

5.1 Comparison of regional and local public transport

This review has shown that frequency, comfort, reliability, travel time, and network coverage are particularly important quality attributes in many studies into regional public transport. Comparing this result with similar reviews about local public transport [ 14 , 15 ], the general impression is that the preferences of regional travellers mainly conform to the preferences of local travellers. This is also suggested by Majumdar & Lentz [ 40 ]. However, our review also highlights some important differences.

Frequency is among the highest ranked attributes in both local and regional public transport, though the results also indicate that the importance of frequency is less pronounced among regional travellers. Instead, in this group, comfort is a higher priority [ 20 ].

Attributes concerning network coverage or walking distance are seemingly more frequent in discussions about important attributes in regional public transport than in local public transport. A probable explanation is the more dispersed nature of regional public transport networks, together with the challenge of designing coherent networks in which frequencies are typically low. A proven remedy, however, is easy and well-timed transfers, achieved through rigorous coordination and central network planning [ 54 , 55 , 57 ]. In addition, the conditions for access and egress to and from stops and stations need attention, including beyond the conventionally assumed catchment areas of 400 or 800 m. Many passengers are willing to walk substantially further if the appropriate infrastructure exists [ 62 , 63 ], even if these results depend on socio-demographic characteristics (such as age) and characteristics of the built environment (such as walkability). Cycling also has the potential to be an important access mode, especially for high-quality regional services, increasing the catchment area to several kilometres [ 62 ]. Moreover, park-and-ride facilities can improve access to the public transport network in cases where overall trip lengths are relatively long [ 65 , 66 ].

In contrast to comfort and network coverage, attributes concerning security and staff do not appear to be as highly prioritised in regional public transport as in local public transport.

The notions of local and regional public transport are rarely used in the literature. Urban, rural, and interurban are more common concepts and substantial parts of the review are based on studies using these notions. As regional is not synonymous with rural or interurban, all parallels need to be handled with care. However, with the definition of regional public transport that we have adopted in this review, which is based on urban areas rather than administrative boundaries, studies of rural and interurban travel, in most cases, will fall within our definition of regional. This is illustrated in the Venn diagram in Fig. 2 , in which the right-hand circle corresponds to our working definition. Furthermore, we have assessed each case study to confirm that it is in line with our definition.

5.2 The influence of trip length

Our results indicate that there are quality attributes whose importance depends on trip length. Based on the availability of results from the investigated studies, three distance categories have been defined – short (less than 25 km), medium (25–50 km), and long (more than 50 km). Passenger preferences appear to differ between short regional trips compared to medium and long regional trips. Unsurprisingly, the results reveal that these differences are essentially in line with the differences found between local and regional travel.

The most pronounced differences appear in attributes regarding on-board comfort, the importance of which quite logically appears to increase with travel distance. Comfort in terms of station facilities is reported to have less impact on perceived service quality in regional public transport [ 68 ], although the results of our review suggest that this conclusion might require modification. On-board comfort is clearly important for medium and long regional trips, but there are indications that station facilities are more important for short regional trips.

Also in line with local–regional differences, the importance of frequency and reliability appear to decrease somewhat with travel distance. A possible explanation for this is that as travel distance increases, in-vehicle travel time increases, meaning that waiting time and potential delays constitute relatively smaller portions of the total travel time. It should be noted, however, that frequency and reliability still are among the top priorities also for long regional trips.

More surprisingly, price appears to be less important for short regional trips than for local trips and for medium and long regional trips. This finding is principally based on patterns appearing when comparing results from studies carried out in different settings regarding travel distances. Only one of the studies uses a comparative method, observing a decrease in price elasticity up to around 20 km, from where it starts increasing again [ 52 ]. This result is in line with the overall pattern, but it should be noted that it is based on relatively few observations.

As there are many similarities between the local–regional comparison and the short–long trip comparison, a relevant question is whether the segmentation between local, regional, and interregional is necessary. Is segmentation based on trip lengths, or travel times, more suitable? Indeed, the relative importance of different quality attributes probably depends more on travel times and trip lengths than on the urban or regional context. However, the prerequisites for regional public transport are generally significantly different compared to local public transport, so the separation of geographical scales is still relevant. We suggest using a combination where possible, separating short from long trip lengths, or travel times, within each category (e.g. short local trips, long local trips, short regional trips, long regional trips). A similar approach is also reasonable for interregional public transport, separating it from regional public transport based on trip regularity (in line with our working definition, see section Local, regional and interregional public transport ) and, where possible, also keeping track of the influence of trip length or travel time.

5.3 Bus versus rail

We have not been able to find any differences between bus and rail services in terms of how quality attributes are prioritised. Comparisons are aggravated by the fact that passenger satisfaction levels are moderated by their expectations [ 26 ] and expectations differ between the modes based on subjective criteria related to travel emotions or sensory aspects [ 58 ]. Nevertheless, there are indications of a general preference for trains over buses [ 58 , 59 ]. Thus, the development and evaluation of rail-inspired bus concepts such as “Rural Bus Rapid Transit” [ 60 ] or “R-Net” [ 62 ] could be an interesting and potentially prosperous way forward for regional bus and coach services.

Compared to local public transport (outlined in the introduction), there are similarities in terms of how bus services are overrepresented in studies into customer satisfaction. Also, rail is generally favoured compared to buses in both local and regional public transport. For local public transport, this gap in perception can be bridged through high-quality bus services such as Bus Rapid Transit, which has been implemented in many cities and now becoming a well-established concept the world over [ 19 ]. Efforts for developing similar high-quality bus concepts on a regional scale are, however, rare.

5.4 Modal choice, demand, and customer satisfaction

As outlined in the introduction, customer satisfaction, demand, and modal choice are intertwined concepts, each impacting the other. In this review, we have been unable to find any substantial differences, in terms of important quality attributes, in the conclusions of customer satisfaction studies compared to studies on demand or modal choice. A couple of minor dissimilarities were indicated in the results, but it is difficult to draw robust conclusions in this regard. This is due to a risk of spuriousness between type of study and other factors such as trip length and public transport mode.

Analogous to studies into local public transport, customer satisfaction studies predominantly focus on bus services. A possible explanation is that bus generally is the least favoured mode, yet constituting significant parts of the public transport networks in many cities and regions, implying that many transport authorities would benefit from increasing customer satisfaction among bus users [ 15 ].

5.5 Directions for future research

As outlined in the introduction, public transport research has largely been focused on local travel and there are relatively few publications that exclusively concern regional public transport [ 12 , 13 , 14 ]. There is a need for more knowledge about the specifics of regional public transport and, based on our results regarding important quality attributes, combined with identified gaps, we can offer some suggestions about the direction of future research:

On-board comfort is a top priority for many regional travellers, but this is a multifaceted attribute and more research is needed into the impact of different aspects of on-board comfort, e.g. seating, ride comfort, and complementary facilities.

Frequency is also acknowledged to be an attribute of great importance, but the reviewed studies have not revealed many details of operational aspects such as peak and off-peak frequencies or operating hours.

Surprisingly little attention has been paid to travel time improvements, regarding in-vehicle travel time in particular, given that in-vehicle travel time generally constitutes a relatively large proportion of the total travel time in regional public transport.

Little is known about how the environmental impact of public transport services affects modal choice for regional travel, as this attribute is rarely included in such studies.

Our review suggests that trip lengths or travel times affect passenger preferences, but the mechanisms are still largely unclear. Further studies that include the crossed effects between trip length or travel time and other service attributes are desirable.

6 Conclusions

For the purpose of exploring passenger preferences, the following working definition of regional public transport can be adopted, with boundaries towards local as well as interregional public transport. Regional public transport (i) targets passengers travelling between separate urban areas or to rural areas and (ii) a majority of the trips are made on a regular basis. The second part of the definition implies that most passengers on regional public transport services are frequent travellers, and hence, our results mainly target frequent travellers.

Quality attributes commonly reported as priorities for regional travellers are frequency, comfort, reliability, travel time, and network coverage. Some important differences with regard to local public transport are suggested. Firstly, on-board comfort is a higher priority for regional travellers, becoming increasingly important with longer travel times. Secondly, network coverage and coordination are also more prominent features in regional public transport, presumably because of the more dispersed nature of regional public transport networks. In relation to this, it has been concluded that catchment areas for walking and cycling to high-quality regional public transport services can be substantially larger than the conventionally assumed 400 or 800 m radius.

These differences, and the fact that the prerequisites for regional public transport are, in general, substantially different compared to local and interregional public transport, support continued use of this categorisation in public transport research. Where applicable, we also suggest inclusion of the impact of trip length or travel time within each category.

Trains are generally found to be preferred to buses and we therefore suggest further development and evaluation of rail-inspired bus concepts. However, we have not found any differences between bus and rail services regarding quality attribute priorities.

Our review does not indicate any substantial differences in terms of important quality attributes in the conclusions of customer satisfaction studies compared to studies into demand or modal choice.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

The authors would like to thank all respondents for their valuable input in the survey. Thanks also to the staff at the LTH library services for their constructive advice.

This study was funded by the Swedish Transport Administration through K2 – The Swedish Knowledge Centre for Public Transport.

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Hansson, J., Pettersson, F., Svensson, H. et al. Preferences in regional public transport: a literature review. Eur. Transp. Res. Rev. 11 , 38 (2019). https://doi.org/10.1186/s12544-019-0374-4

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Big data in transportation: a systematic literature analysis and topic classification

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This paper identifies trends in the application of big data in the transport sector and categorizes research work across scientific subfields. The systematic analysis considered literature published between 2012 and 2022. A total of 2671 studies were evaluated from a dataset of 3532 collected papers, and bibliometric techniques were applied to capture the evolution of research interest over the years and identify the most influential studies. The proposed unsupervised classification model defined categories and classified the relevant articles based on their particular scientific interest using representative keywords from the title, abstract, and keywords (referred to as top words). The model’s performance was verified with an accuracy of 91% using Naïve Bayesian and Convolutional Neural Networks approach. The analysis identified eight research topics, with urban transport planning and smart city applications being the dominant categories. This paper contributes to the literature by proposing a methodology for literature analysis, identifying emerging scientific areas, and highlighting potential directions for future research.

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1 Introduction

Urbanization, ongoing changes in mobility patterns, and rapid growth in freight transportation pose significant challenges to stakeholders and researchers within the transportation sector. New policies have focused on promoting sustainability and reducing emissions through smart and multimodal mobility [ 1 ]. To address these challenges, authorities are developing strategies that employ technological advances to gain a deeper understanding of travel behavior, produce more accurate travel demand estimates, and enhance transport system performance.

Undoubtedly, the development of Intelligent Transport Systems (ITS) and recent advances in Information and Communication Technology (ICT) have enabled the continuous generation, collection, and processing of data and the observation of mobility behavior with unprecedented precision [ 2 ]. Such data can be obtained from various sources, including ITS, cell phone call records, smart cards, geocoded social media, GPS, sensors, and video detectors.

Over the past decade, there has been increasing research interest in the application of big data in various transportation sectors, such as supply chain and logistics [ 3 ], traffic management [ 4 ], travel demand estimation [ 5 ], travel behavior [ 6 ], and real-time traffic operations [ 7 ]. Additionally, numerous studies in the field of transport planning and modeling have applied big data to extract vital attributes including trip identification [ 8 ] and activity inference [ 9 ]. Despite research efforts and existing applications of big data in transportation, many aspects remain unknown, and the prospects of big data to gain better insights into transport infrastructure and travel patterns have not yet been explored.

To maximize the benefits of big data in traffic operations, infrastructure maintenance, and predictive modeling, several challenges remain to be addressed. These include handling the high velocity and volume of streaming data for real-time applications, integrating multiple data sets with traditional information sources, ensuring that the data is representative of the entire population, and accessing big data without compromising privacy and ethical concerns. In terms of transport modeling and management, the research focuses on achieving short-term stability by incorporating more comprehensive data sets that cover hourly, daily, seasonal, or event-based variations, and on enhancing mobility on demand through real-time data processing and analysis. Additionally, it has become necessary to further investigate the methodology for processing large amounts of spatial and temporal information that has primarily been intended for non-transport purposes and to reconsider the existing analytical approaches to adapt to the changing data landscape.

There is an intention among policymakers, transport stakeholders, and researchers to better understand the relationship between big data and transport. The first step in this direction is to identify the key areas of big data utilization in the transport sector. Therefore, this study attempts to map big data applications within the transport domain. It provides a broad overview of big data applications in transport and contributes to the literature by introducing a methodology for identifying emerging areas where big data can be successfully applied and subfields that can be further developed.

The scope of the current study is twofold. First, a holistic literature analysis based on bibliometric techniques complemented by a topic classification model covering the complete domain of big data applications in the transportation sector was implemented. Despite numerous studies attempting to review the relevant literature, such as big data in public transport planning or transport management [ 10 , 11 ] to the best of our knowledge, no such investigation has produced a comprehensive and systematic cluster of multiple big data applications across the entire transportation domain based on a significant number of literature records. Therefore, the primary objective of this study is to classify the literature according to its particular interest and to pinpoint evolving scientific subfields and current research trends.

Second, as multiple studies have been conducted in this domain, the need to identify and assess them prior to running one’s own research through a thorough literature review is always necessary. However, the analysis and selection of appropriate studies can be challenging, particularly when the database is large. Therefore, this study aims to provide a comprehensive approach for evaluating and selecting appropriate literature that could be a methodological tool in any research field. Bibliometric methods have been widely applied in several scientific fields. Most of these studies use simple statistical methods to determine the evolution of the number of publications over the years, authors’ influence, or geographical distribution. There are also research works that attempt to categorize the literature mainly by manual content analysis [ 12 , 13 ] or by co-citation analysis, applying software for network and graphical analyses [ 14 , 15 ]. In this study, the review process also included an unsupervised topic model to classify literature into categories.

This paper presents a comprehensive evaluation of up-to-date published studies on big data and their applications in the transportation domain. A total of 2671 articles from Elsevier's Scopus database, published between 2012 and 2022 were analyzed. Bibliometric techniques were applied to capture the evolution of research over time and uncover emerging areas of interest. In addition, the focus of this study is to define categories and classify relevant papers based on their scientific interests. To achieve this, unsupervised classification was applied using the topic model proposed by Okafor [ 16 ] to identify clusters, extract the most representative topics, and group the documents accordingly.

The current study attempts to answer the following questions:

Which studies contribute the most to the field of big data in transportation?

What is the evolution of research over time in this field of interest?

What are the main research areas that have potential for further exploration?

What are the directions of future research?

This paper consists of six sections. Following the introduction, Sect.  2 provides a summary of previous research in this subject area. Section  3 outlines the methodology applied in this research. This includes the process of defining the eligible studies, bibliometric techniques utilized, and the topic model employed for paper classification. Section  4 presents the initial statistical results and the classification outcomes derived from the topic model. In Sect.  5 , the findings are summarized, and the results associated with the research questions are discussed. The final Section presents the general conclusions and research perspectives of the study.

2 Literature review

Due to the significant benefits of big data, several studies have been conducted in recent years to review and examine the existing applications of different big data sources in transportation. Most of these focus on a specific transport domain, such as transport planning, transport management and operations, logistics and supply chain and Intelligent Transportation Systems.

In the context of transport planning and modeling, Anda et al. [ 2 ] reviewed the current application of historical big data sources, derived from call data records, smart card data, and geocoded social media records, to understand travel behavior and to examine the methodologies applied to travel demand models. Iliashenko et al. [ 17 ] explored the potential of big data and Internet of Things technologies for transport planning and modeling needs, pointing out possible applications. Wang et al. [ 18 ] analyzed existing studies on travel behavior utilizing mobile phone data. They also identified the main opportunities in terms of data collection, travel pattern identification, modeling and simulation. Huang et al. [ 19 ] conducted a more specialized literature review focusing on the existing mode detection methods based on mobile phone network data. In the public transportation sector, Pelletier et al. [ 20 ] focused on the application of smart card data, showing that in addition to fare collection, these data can also be used for strategic purposes (long-term planning), tactical purposes (service adjustment and network development), and operational purposes (public transport performance indicators and payment management). Zannat et al. [ 10 ] provided an overview of big data applications focusing on public transport planning and categorized the reviewed literature into three categories: travel pattern analysis, public transport modeling, and public transport performance assessment.

In traffic forecasting, Lana et al. [ 21 ] conducted a survey to evaluate the challenges and technical advancements of traffic prediction models using big traffic data, whereas Miglani et al. [ 22 ] investigated different deep learning models for traffic flow prediction in autonomous vehicles. Regarding transport management, Pender et al. [ 23 ] examined social media use during transport network disruption events. Choi et al. [ 11 ] reviewed operational management studies associated with big data and identified key areas including forecasting, inventory management, revenue management, transportation management, supply chain management, and risk analysis.

There is also a range of surveys investigating the use of big data in other transport subfields. Ghofrani et al. [ 24 ] analyzed big data applications in railway engineering and transportation with a focus on three areas: operations, maintenance, and safety. In addition, Borgi et al. [ 25 ] reviewed big data in transport and logistics and highlighted the possibilities of enhancing operational efficiency, customer experience, and business models.

However, there is a lack of studies that have explored big data applications attempting to cover a wider range of transportation aspects. In this regard, Zhu et al. [ 26 ] examined the features of big data in intelligent transportation systems, the methods applied, and their applications in six subfields, namely road traffic accident analysis, road traffic flow prediction, public transportation service planning, personal travel route planning, rail transportation management and control, and asset management. Neilson et al. [ 27 ] conducted a review of big data usage obtained from traffic monitoring systems crowdsourcing, connected vehicles, and social media within the transportation domain and examined the storage, processing, and analytical techniques.

The study by Katrakazas et al. [ 28 ], conducted under the NOESIS project funded by the European Union's (EU) Horizon 2020 (H2020) program, is the only one we located that comprehensively covers the transportation field. Based on the reviewed literature, the study identified ten areas of focus that could further benefit from big data methods. The findings were validated by discussing with experts on big data in transportation. However, the disadvantage of this study lies in its dependence on a limited scope of the reviewed literature.

The majority of current review-based studies concentrate on one aspect of transportation, often analyzing a single big data source. Many of these studies rely on a limited literature dataset, and only a few have demonstrated a methodology for selecting the reviewed literature. Our review differs from existing surveys in the following ways: first, a methodology for defining the selected literature was developed, and the analysis was based on a large literature dataset. Second, this study is the only one to employ an unsupervised topic classification model to extract areas of interest and open challenges in the domain. Finally, it attempts to give an overview of the applications of big data across the entire field of transportation.

3 Research methodology

This study followed a three-stage literature analysis approach. The first stage includes defining the literature source and the papers' search procedures, as well as the “screening” to select the reviewed literature. The second stage involves statistics, which are widely employed in bibliometric analysis, to capture trends and primary insights. In the third stage, a topic classification model is applied to identify developing subfields and their applications. Eventually, the results are presented, and the findings are summarized.

3.1 Literature selection

The first step in this study was to define the reviewed literature. A bibliographic search was conducted using the Elsevier's Scopus database. Scopus and Web of Science (WOS) are the most extensive databases covering multiple scientific fields. However, Scopus offers wider overall coverage than WoS CC and provides a better representation of particular subject fields such as Computer Sciences [ 29 ] which is of interest in this study. Additionally, Scopus comprises 26591 peer-reviewed journals [ 30 ] including publications by Elsevier, Emerald, Informs, Taylor and Francis, Springer, and Interscience [ 15 ], covering the most representative journals in the transportation sector.

The relevant literature was identified and collected using the Scopus search API, which supports Boolean syntax. Four combinations of keywords were used in the “title, abstract, keywords” document search of the Scopus database including: “Big data” and “Transportation”, “Big data” and “Travel”, “Big data” and “Transport”, “Big data” and “Traffic”. The search was conducted in English as it offers a wider range of bibliographic sources. Only the last decade’s peer-reviewed research papers published in scientific journals and conference proceedings have been collected, written exclusively in English. Review papers and document types such as books and book chapters were excluded. As big data in transport is an interdisciplinary field addressed by different research areas, in order to cover the whole field of interest, the following subject areas were predefined in the Scopus search: computer sciences; engineering; social sciences; environmental sciences; energy; business, management and accounting. Fields considered irrelevant to the current research interest were filtered out.

The initial search resulted in a total of 5234 articles published between the period 2012–2022. The data was collected in December 2021 and last updated on the 5th of September 2023. The results were stored in a csv format, including all essential paper information such as paper title, authors’ names, source title, citations, abstracts, year of publication, and keywords. After removing duplications, a final dataset of 3532 papers remained.

The paper dataset went through a subject relevance review, at the first stage, by checking in the papers' title or keywords the presence of at least one combination of the search terms. If this condition was not met, a further review of the paper abstracts was conducted. From both stages, a filtered set of papers was selected, based on their relevance to the current study's areas of interest, associated with the search items. A total of 2671 selected papers formed the dataset which was further analyzed, evaluated, and categorized, based on clustering techniques.

3.2 Initial statistics

Once the dataset was defined, statistical analysis was performed to identify influential journals and articles within the study field. The first task was to understand the role of the different journals and conference proceedings. Those with the most publications were listed and further analyzed according to their publication rate and research area of interest. Second, the number of citations generated by the articles was analyzed as a measure of the quality of the published studies, and the content of the most cited articles was further discussed. The above provided essential insights into research trends and emerging topics.

3.3 Topic classification

A crucial step in our analysis was to extract the most representative sub-topics and classify the articles into categories by applying an unsupervised topic model [ 16 ]. Initially, the Excel file with the selected papers’ data (authors, year, title, abstract, and keywords) was imported into the model. Abstracts, titles, and keywords were analyzed and text-cleaning techniques were applied. This step includes normalizing text, removing punctuations, stop-words, and words of length less than three letters, as well as the lemmatization of the words. The most popular software tools/libraries used for text mining and cleaning, as well as natural language processing (NLP) in the topic model process, are implemented in Python programming and include NLTK ( https://www.nltk.org/ ), spaCy ( https://spacy.io/ ), Gensim ( https://radimrehurek.com/gensim/ ), scikit-learn ( https://scikit-learn.org/stable/ ), and Beautiful Soup ( https://www.crummy.com/software/BeautifulSoup/ ). NLTK is a powerful NLP library with various text preprocessing functions, while spaCy handles tokenization, stop word removal, stemming, lemmatization, and part-of-speech tagging. Gensim is a popular library for topic labeling and document similarity analysis. To process textual data, scikit-learn is a machine learning library with text preprocessing functions. Finally, Beautiful Soup is a web-based library for parsing HTML and XML documents. For the approach explained in the following sections, NLTK and Beautiful Soup were used to parse web metadata for the research papers. Moreover, bigrams and trigrams of two or three words, frequently occurring together in a document, were created.

The basic aim was to generate clusters (topics) using a topic model. The proposed model extracts representative words from the title, abstract, and keyword section of each paper, aiming to cluster research articles into neighborhoods of topics of interest without requiring any prior annotations or labeling of the documents. The method initially constructs a word graph model by counting the Term Frequency – Inverse Document Frequency (TF-IDF) [ 31 ].

The resulting topics are visualized through a diagram that shows the topics as circular areas. For the aforementioned mechanism, the Jensen-Shannon Divergence & Principal Components dimension reduction methodology was used [ 32 ]. The model is implemented in the LDAvis (Latent Dirichlet Allocation) Python library, resulting in two principal components (PC1 and PC2) that visualize the distance between the topics on a two-dimensional plane. The topic circles are created using a computationally greedy approach (the first in-order topic gets the largest area, and the rest of the topics get a proportional area according to their calculated significance). The method picks the circle centroids randomly for the first one (around the intersection of the two axes), and then the distance of the circle centroids is relevant to the overlapping of the top words according to the Jensen-Shannon Divergence model. The former model was applied for numerous numbers of topics, while its performance was experimentally verified using a combination of Naïve Bayesian Networks and a Convolutional Neural Network approach.

In the sequence of the two machine learning methodologies, supervised (Bayesian Neural Networks) and unsupervised (Deep Learning) research article classification were considered via sentiment analysis. For the supervised case, the relations among words were identified, offering interesting qualitative information from the outcome of the TF-IDF approach. The results show that the Bayesian Networks perform in accuracies near 90% of the corresponding statistical approach [ 33 ] and the same happens (somewhat inferior performance) for the unsupervised case [ 34 ]. The two methods validated the results of the TF-IDF, reaching accuracies within the acceptable limits of the aforementioned proven performances.

3.3.1 TF-IDF methodology

The TF-IDF (Term Frequency–Inverse Document Frequency) method is a weighted statistical approach primarily used for mining and textual analysis in large document collections. The method focuses on the statistical importance of a word value emanating by its existence in a document and the frequency of occurrences. In this context, the statistical significance of words grows in proportion to their frequency in the text, but also in inverse proportion to their frequency in the entire corpus of documents. Therefore, if a word or phrase appears with high frequency in an article (TF value is high), but simultaneously seldom appears in other documents (IDF value is low), it is considered a highly candidate word that may represent the article and can be used for classification [ 35 ]. In the calculation of TF-IDF, the TF word value is given as:

In Eq.  1 , \({n}_{ij}\) denotes the frequency of the word occurrence of the term \({t}_{i}\) in the document \({d}_{j}\) , and the denominator of the above fraction is the sum of the occurrence frequency of all terms in the document \({d}_{j}.\) At the same time, the calculation of the IDF value of a term \({t}_{i}\) , is found by dividing the total number of documents in the corpus by the number of documents containing the term \({t}_{i}\) , and then obtains the quotient logarithm:

In Eq .  2 , | D | denotes the total number of documents, and the denominator represents the number of documents j which contain the term \({t}_{i}\) in that specific document \({d}_{j}\) . In other words, we consider only the documents where \({n}_{ij}\ne 0\) in Eq. ( 1 ). In the case scenario in which the term \({t}_{i}\) does not appear in the corpus, it will cause the dividend to be zero in the above equation, causing the denominator to have the values of + 1. Using Eqs. ( 1 ) and ( 2 ), TF-IDF is given by:

From Eq.  3 , it can be assumed that high values of TF-IDF can be produced by high term frequencies in a document, while at the same time, low term frequencies occur in the entire document corpus. For this reason, TF-IDF succeeds in filtering out all high-frequency “common” words, while at the same time, retaining statistically significant words that can be the topic representatives [ 36 ]

3.3.2 Naïve bayes methodology

This methodology evaluates the performance of TF-IDF by following a purely “machine-learning” oriented approach, which is based on the Bayesian likelihood, i.e., reverse reasoning to discover arbitrary factor occurrences that impact a particular result. These arbitrary factors correspond to the corpus terms and their frequencies within each document and corpus. The model is a multinomial naive Bayes classifier that utilizes the Scikit-learn, which is a free programming Artificial Intelligence (AI) library for the Python programming language to support: a. training text, b. feature vectors, c. the predictive model, and d. the grouping mechanism.

The results of the TF-IDF method were imported into the Bayesian classifier. More specifically, the entire dataset was first prepared to be inserted by applying noise, stop-words, and punctuation removal. The text was then tokenized into words and phrases. For topic modeling, TF-IDF was used for feature extraction, creating the corresponding vectors as features for classification. In the next step, the Naive Bayes classifier is trained on the pre-processed and feature-extracted data. During training, it learns the likelihood of observing specific words or features given each topic and the prior probabilities of each topic occurring in the training data. Not all data was used for training. The model split the data into a 70% portion used for the unsupervised training, with the remaining 30% to be used for validation. This split ratio is a rule of thumb and not a strict requirement. However, this popular split ratio is to strike a balance between having enough data for training the machine learning model effectively and having enough data for validation or testing to evaluate the model's performance. The split was used within the k-fold cross-validation to assess the performance of the model while mitigating the risk of overfitting. While the dataset is divided into k roughly equal-sized "folds" or subsets, a fixed train-test split is used within each fold. The results from each fold were then averaged to obtain an overall estimate of model performance. This approach has the advantage of assessing the model's performance in a more granular way within each fold, similar to how it is assessed in a traditional train-test split, but at the same time, it provides additional information about how the model generalizes to different subsets of the data within each fold.

In the process of text examination, a cycle of weighting is dynamically updated for cases of term recurrences in the examined documents. The documents still contain only the title, abstract, and keywords for each article. For these cases, the Bayes theorem is used:

where \(P(c|x)\) is the posterior probability, \(P(x|c)\) is the likelihood, \(P(c)\) is the class prior probability, and \(P(x)\) is the predictor prior probability with \(P(c|x)\) resulting from the individual likelihoods of all documents \(P\left({x}_{i}|c\right),\) as depicted in Eq. ( 5 ):

This model was used as a validation method for the TF-IDF methodology, producing accuracy results reaching values of up to 91%. This value is widely acceptable for most cases of Bayesian classification and is expected to occur since prior classification has been applied [ 37 ].

3.3.3 Deep learning classification methodology

This is a secondary method of TF-IDF validation, which is based on the bibliometric coupling technique. However, this technique does not use the likelihood probability of the initial classification performed by TF-IDF but rather, this classifier deploys a character-based (i.e., the alphabetical letters composing the articles’ text) convolutional deep neural network. Using the letters as basic structural units, a Convolutional Neural Network [ 38 ] learns words and discovers features and word occurrences in various documents. The model has been primarily applied for computer vision and image machine learning techniques [ 39 ], but it is easily adapted for textual analysis.

All features previously used features (title, abstract, keywords) were kept and concatenated into a single string of text, which was itself truncated to a maximum length of 4000 characters. The size of the string can be dynamically adapted for each TensorFlow-model [ 40 ] according to the GPU performance of the graphics adapter of the hardware used, and basically represents the maximum allowable length for each feature in the analysis. The encoding involved all 26 English characters, 10 digits, all punctuation signs, and the space character. Other extraneous characters were eliminated. Furthermore, keyword information was considered primary in topic classification and encoded into a vector of the proportion of each subfield mentioned in the reference list of all documents/articles used in data. The system was rectified to behave as a linear unit, producing the activation function of the deep neural network between each layer. Only the last layer of the network utilized the SoftMax activation function for the final classification. The model was trained with a stochastic gradient descent as the optimizer and categorical cross-entropy as the loss function producing inferior results when compared with the corresponding Bayesian case as expected.

4.1 Source analysis

To understand the role of diverse academic sources, the leading eleven journals or conference proceedings were identified (Table  1 ), which have published a minimum of twenty papers between 2012 and 2022 in the field of interest. According to the preliminary data, 995 journals and conference proceedings have contributed to the publication of 2671 papers. Eleven sources have published 553 articles, representing the 2100% of all published papers.

Three sources in the field of computer science have published 239 articles. Five transportation journals and conference proceedings have published 208 papers, while there are journals on urban planning and policies (e.g., Cities) that have also significantly contributed to the research field (106 papers).

The research findings indicate the interdisciplinarity of the application of big data in transportation, encompassing not only computer science but also transport and urban planning journals, showing that transport specialists acknowledge the advantages of examining the applications of big data in the transport domain.

4.2 Citation analysis

Citation analysis classifies the papers by their citation frequency, aiming to point out their scientific research impact [ 14 ] and to identify influential articles within a research area. Table 2 demonstrates the top fifteen studies published between 2012 and 2022 (based on citation count on Scopus). Lv et al. [ 41 ] published the most influential paper in this period and received 2284 citations. This study applied a novel deep learning method to predict traffic flow. In this direction, four other articles focused on traffic flow prediction, real-time traffic operation, and transportation management [ 7 , 11 , 42 , 43 ].

Other important contributions highlighted the significance of big data generated in cities and analyzed challenges and possible applications in many aspects of the city, such as urban planning, transportation, and the environment [ 44 , 45 , 46 , 47 ]. Xu et al. [ 48 ] focused on the Internet of Vehicles and the generated big data. Finally, among the most influential works, there are papers that investigated big data usage in various subfields of spatial analysis, as well as urban transport planning and modeling, focusing on travel demand prediction, travel behavior analysis, and activity pattern identification [ 5 , 6 , 49 , 50 , 51 ].

4.3 Topic model

As previously mentioned, a topic model based on the TF-IDF methodology was used to categorize the papers under different topics. The basic goal was to identify subscientific areas of transportation where big data were implemented. A critical task was to define the appropriate parameters of the model, particularly the number of topics that could provide the most accurate and meaningful results, which would be further processed. To achieve this, a qualitative analysis was conducted taking into account the accuracy results obtained from the validation methodology. Therefore, to obtain a more precise view of the project, the model was implemented under various scenarios, increasing each time the number of topics by one, starting from four topics to fifteen.

The mapping of the tokenized terms and phrases to each other was used to understand the relationships between words in the context of topics and documents. The technique applied is the Multidimensional Scaling (MDS), which helps to visualize and analyze these relationships in a lower-dimensional space [ 52 , 53 ]. To highlight the relationships between terms, a matrix of tokens in the corpus was created based on their co-occurrence patterns. Each cell in the matrix represents how often two terms appear together in the same document. The MDS methodology provides the ability to view high-dimensional data in a lower-dimensional space while retaining as much of the pairwise differences or similarities between the terms as possible. More specifically, MDS translates the high-dimensional space of term co-occurrence relationships into a lower-dimensional space, where words that frequently co-occur are located closer to one another, and terms that infrequently co-occur are situated farther apart. When terms are displayed as points on a map or scatterplot via MDS, points that are close together in the scatterplot are used in the documents in a more connected manner.

As a consequence, the scatterplot that MDS produces can shed light on the structure of the vocabulary in relation to the topic model. It can assist in identifying groups of related terms that may indicate subjects or themes in the corpus. The pyLDAvis library of the Python programming language was used in conjunction with the sklearn library to incorporate the MDS into the clustering visualization process. This was done by superimposing the scatterplot with details about the subjects assigned to documents or the probability distributions over different topics.

Initially, the papers were divided into four topics. Figure  1 a displays four distinct clusters, each representing a scientific sub-area of interest. Clusters appear as circular areas, while the two principal components (PC1 and PC2) are used to visualize the distance between topics on a two-dimensional plane. Figure 1 b illustrates the most relevant terms for Topic 1 in abstracts, titles, and keywords. Table 3 contains the most essential words associated with each sub-area based on the TF-IDF methodology, representing the nature of the four topics. The results of the model, considering, also, the content of the most influential papers in each group, reveal that the biggest cluster is associated with “transport planning and travel behavior analysis”. In this topic, most papers have focused on long-term planning, utilizing big data mostly from smart cards, mobile phones, social media, and GPS trajectories to reveal travel and activity patterns or estimate crucial characteristics for transport planning, such as travel demand, number of trips, and trip purposes. The second topic refers to “smart cities and applications”, containing papers about how heterogeneous data generated in cities (streamed from sensors, devices, vehicles, or humans) can be used to improve the quality of human life, city operation systems, and the urban environment. Most studies have concentrated on short-term thinking about how innovative services and big data applications can support function and management of cities. In terms of transportation, several studies have managed to integrate Internet of Things (IoT) and big data analytics with intelligent transportation systems, including public transportation service plan, route optimization, parking, rail transportation, and engineering and supply chain management. Two smaller clusters were observed. One cluster is dedicated to “traffic forecasting and management” and includes papers related to traffic flow prediction or accident prediction, while many of them focus on real-time traffic management, city traffic monitoring, and control, mainly using real-time traffic data generated by sensors, cameras, and vehicles. The other is associated with “intelligent transportation systems and new technologies”, focusing on topics such as the connected vehicle-infrastructure environment and connected vehicle technologies as a promising direction to enhance the overall performance of the transportation system. Considerable attention has also been given to Internet of Vehicles (IoV) technology, autonomous vehicles, self-driving vehicle technology, and automatic traffic, while many contributions in this topic are related to green transportation and suggest solutions for optimizing emissions in urban areas with a focus on vehicle electrification. The four topics are represented as circular areas. The two principal components (PC1 and PC2) are used to visualize the distance between topics on a two-dimensional plane.

Four topics distance map, b Top 30 most relevant terms for Topic 1

By increasing the number of topics, various categories are becoming more specific, and it can be observed that there is a stronger co-relationship among clusters. The results of the eight topics’ categorization are presented in Fig.  2 a and b and Table  4 . The eight topics are represented as circular areas. The two principal components (PC1 and PC2) are used to visualize the distance between topics on a two-dimensional plane.

a Eight topics distance map, b Top 30 most relevant terms for Topic 1

According to the top words in each cluster and taking into consideration the content of top papers abstracts, the eight topics were specified as follows:

Topic 1 (742 papers), “Urban transport planning”: This topic concerns long-term transportation planning within cities, utilizing big and mainly historical data, such as call detail records from mobile phones and large-scale geo-location data from social media in conjunction with geospatial data, census records, and surveys. The emphasis is on analyzing patterns and trends in the urban environment. Most studies aim to investigate the spatial–temporal characteristics of urban movements, detect land use patterns, reveal urban activity patterns, and analyze travel behavior. Moreover, many papers focus on travel demand and origin–destination flow estimation or extract travel attributes, such as trip purpose and activity location.

Topic 2 (723 papers), “Smart cities and applications”: This topic remains largely consistent with the previous categorization. As above, the papers aim to take advantage of the various and diverse data generated in cities, analyze new challenges, and propose real-time applications to enhance the daily lives of individuals and city operation systems.

Topic 3 (438 papers), “Traffic flow forecasting and modeling”: This area of research involves the use of machine and deep learning techniques to analyze mainly historical data aiming to improve traffic prediction accuracy. The majority of these papers concentrate on short-term traffic flow forecasting, while a significant number of them address passenger flow and traffic accident prediction.

Topic 4 (231 papers), “Traffic management”: this topic concentrates on traffic management and real-time traffic control. City traffic monitoring, real-time video, and image processing techniques are gaining significant attention. Numerous studies utilize real-time data to evaluate traffic conditions by image-processing algorithms and provide real-time route selection guidance to users. Most of them manage to identify and resolve traffic congestion problems, as well as to detect anomalies or road traffic events, aiming to improve traffic operation and safety.

Topic 5 (194 papers), “Intelligent transportation systems and new technologies”: this topic remains nearly identical to the prior (4-clusters) classification, containing articles on emerging technologies implemented in an intelligent and eco-friendly transport system. Most studies focus on the connected vehicle-infrastructure environment and connected vehicle technologies as a promising direction for improving transportation system performance. Great attention is also given to Internet of Vehicles (IoV) technology and the efficient management of the generated and collected data. Autonomous and self-driving vehicle technologies are also crucial topics. Many papers, also, discuss green transportation and suggest ways to optimize emissions in urban areas, with a particular emphasis on vehicle electrification.

Topic 6 (144 papers), “Public transportation”: since public transportation gained special scientific interest in our database, a separate topic was created regarding public transport policy making, service, and management. Most publications focus on urban means of transport, such as buses, metro, and taxis, while a significant proportion refers to airlines. This topic covers studies related to public transportation network planning and optimization, performance evaluation, bus operation scheduling, analysis of passenger transit patterns, maximization of passenger satisfaction levels, and real-time transport applications. Moreover, smart cards and GPS data are extensively used to estimate origin–destination matrices.

Topic 7 (104 papers), “Railway”: this topic presents research papers that apply big data to railway transportation systems and engineering, encompassing three areas of interest: railway operations, maintenance, and safety. A significant proportion of studies focus on railway operations, including train delay prediction, timetabling improvement, and demand forecasting. Additionally, numerous researchers employ big data to support maintenance decisions and conduct risk analysis of railway networks, such as train derailments and failure prediction. These papers rely on diverse datasets, including GPS data, passenger travel information, as well as inspection, detectors, and failure data.

Topic 8 (95 papers), “GPS Trajectories”: This topic contains papers that take advantage of trajectory data primarily obtained from GPS devices installed in taxis. Most studies forecast the trip purpose of taxi passengers, trip destination, and travel time by analyzing historical GPS data. Additionally, a significant number of these studies focus on real-time analysis to provide passengers with useful applications and enhance the quality of taxi services. Finally, there is research interest in maritime routing and ship trajectory analysis to facilitate maritime traffic planning and service optimization.

In the eight-topic classification, the initial four clusters either remained almost unchanged or were divided into subcategories. For example, the previous cluster “transport planning and travel behavior analysis” is now divided into “urban transport planning” and “public transportation”, with “transport management” constituting a separate category. Moreover, several distinct smaller clusters have been identified (e.g., “railway” and “trajectories”). These, along with “public transportation”, are highly specialized categories with no correlation to the other clusters. Nevertheless, they constitute a significant proportion of the literature and merit separate analysis.

As the number of topics increased, so did the overlaps among the clusters. Thus, based on this observation and the accuracy results of the validation method, it was assumed that eight clusters were the most appropriate for further analysis.

Based on the results of eight-topic classification, Fig.  3 demonstrates the evolution of the number of published articles per topic and per year. As shown three topics have gained researchers’ interest: (1) urban transport planning, (2) smart cities and applications, and (3) traffic forecasting and modeling. Initially, the primary topic was “smart cities”, largely based on the computer science sector. Despite a slight decline in publications in 2019, there is an overall upward trend. “Urban transport planning” experienced a steady and notable increase until 2019. A sudden drop was recorded in 2022, but it is not clear whether this is a coincidence or a trend. However, it remains the dominant topic, with most publications over the years. The observed decrease could indicate further specialization and research evolution in the field, given it is also the topic with the most subcategories during the classification process.

Number of papers per topic and year

5 Discussion

5.1 statistical analysis.

As shown in the analysis, there is an increasing research interest in big data usage in the transportation sector. It is remarkable that besides computer science journals and conferences, transportation journals have also published relevant articles representing a notable proportion of the research and indicating that transportation researchers acknowledge the significance of big data and its contribution to many aspects of transportation. According to the citation analysis, three research areas emerged among the most influential studies: (1) traffic flow prediction and management (2) new challenges of the cities (smart cities) and new technologies (3) urban transport planning and spatial analysis.

5.2 Topic classification

Following the topic model results, eight paper groups are proposed. Most articles (742) fall into the topic of “urban transport planning”. Several representative papers in this area attempted to estimate travel demand [ 5 ], analyze travel behavior [ 6 ], or investigate activity patterns [ 51 ] by utilizing big data sourced primarily from mobile phone records, social media, and smart card fare collection systems.

Big data also has significant impacts on “smart cities and applications”. Topic 2 is a substantial part of the dataset, which includes 723 papers. They mainly refer to new challenges arising from big data analytics to support various aspects of the city, such as transportation or energy [ 44 ] and investigate big data applications in intelligent transportation systems [ 26 ] or in the supply chain and logistics [ 54 ].

A total of 438 papers were categorized in Topic 3 labeled as “traffic flow forecasting and modeling”. The majority applied big data and machine learning techniques to predict traffic flow [ 41 , 42 , 43 ]. In risk assessment, Chen et al. [ 55 ] proposed a logit model to analyze hourly crash likelihood, considering temporal driving environmental data, whereas Yuan et al. [ 56 ] applied a Convolutional Long Short-Term Memory (ConvLSTM) neural network model to forecast traffic accidents.

Among the papers, a special focus is given to different aspects of “traffic management” (231 papers), largely utilizing real-time data. Shi and Abdel-Aty [ 7 ] employed random forest and Bayesian inference techniques in real-time crash prediction models to reduce traffic congestion and crash risk. Riswan et al. [ 57 ] developed a real-time traffic management system based on IoT devices and sensors to capture real-time traffic information. Meanwhile, He Z. et al. [ 58 ] suggested a low-frequency probe vehicle data (PVD)-based method to identify traffic congestion at intersections to solve traffic congestion problems.

Topic 5 includes 194 records on “intelligent transportation systems and new technologies”. It covers topics such as Internet of Vehicles [ 48 , 59 , 60 ], connected vehicle-infrastructure environment [ 61 ], electric vehicles [ 62 ], and the optimization of charging stations location [ 63 ], as well as autonomous vehicles (AV) and self-driving vehicle technology [ 64 ].

In recent years, three smaller and more specialized topics have gained interest. Within Topic 6, there are 144 papers discussing public transport. Tu et al. [ 65 ] examined the use of smart card data and GPS trajectories to explore multi-modal public ridership. Wang et al. [ 66 ] proposed a three-layer management system to support urban mobility with a focus on bus transportation. Tsai et al. [ 67 ] applied simulated annealing (SA) along with a deep neural network (DNN) to forecast the number of bus passengers. Liu and Yen [ 68 ] applied big data analytics to optimize customer complaint services and enhance management process in the public transportation system.

Topic 7 contains 104 papers on how big data is applied in “railway network”, focusing on three sectors of railway transportation and engineering. As mentioned in Ghofrani et al. [ 24 ], these sectors are maintenance [ 69 , 70 , 71 ], operation [ 72 , 73 ] and safety [ 74 ].

Topic 8 (95 papers) refers mainly to data deriving from “GPS trajectories”. Most researchers utilized GPS data from taxis to infer the trip purposes of taxi passengers [ 75 ], explore mobility patterns [ 76 ], estimate travel time [ 77 ], and provide real-time applications for taxi service improvement [ 78 , 79 ]. Additionally, there are papers included in this topic that investigate ship routes. Zhang et al. [ 80 ] utilized ship trajectory data to infer their behavior patterns and applied the Ant Colony Algorithm to deduce an optimal route to the destination, given a starting location, while Gan et al. [ 81 ] predicted ship travel trajectories using historical trajectory data and other factors, such as ship speed, with the Artificial Neural Network (ANN) model.

6 Conclusions

An extensive overview of the literature on big data and transportation from 2012 to 2022 was conducted using bibliometric techniques and topic model classification. This paper presents a comprehensive methodology for evaluating and selecting the appropriate literature. It identifies eight sub-areas of research and highlights current trends. The limitations of the study are as follows: (1) The dataset came up by using a single bibliographic database (Scopus). (2) Research sources, such as book chapters, were excluded. (3) Expanding the keyword combinations could result in a more comprehensive review. Despite these limitations, it is claimed that the reviewed dataset is representative, leading to accurate findings.

In the process of selecting the suitable literature, various criteria were defined in the Scopus database search, including the language, subject area, and document type. Subsequently, duplicate and non-scientific records were removed. However, the last screening of the titles and abstracts to determine the relevance of the studies to the paper’s research interests was conducted manually. This could not be possible for a larger dataset. Additionally, as previously stated, the dataset was divided into eight distinct topics due to multiple overlaps caused by an increase in the number of topics. Nevertheless, the topic of “smart cities and applications” remains broad, even with this division. This makes it challenging to gain in-depth insights into the field and identify specific applications, unlike in “transport planning”, where two additional topics were generated by the further classification. Applying the classification model to each topic separately could potentially overcome these constraints by revealing more precise applications and filtering out irrelevant studies.

Despite the above limitations and constraints, the current study provides an effective methodology for mapping the field of interest as a necessary step to define the areas of successful applications and identify open challenges and sub-problems that should be further investigated. It is worth mentioning that there is an intense demand from public authorities for a better understanding of the potential of big data applications in the transport domain towards more sustainable mobility [ 82 ]. In this direction, our methodology, along with the necessary literature review and discussion with relevant experts, can assist policymakers and transport stakeholders in identifying the specific domains in which big data can be applied effectively and planning future transport investments accordingly.

Having defined the critical areas of big data implementation within transportation, trends, and effective applications, the aim is to conduct a thorough literature review in a subarea of interest. This will focus on transport planning and modeling, and public transportation, which appears to be highly promising, based on our findings. A more extensive literature review and content analysis of key studies are crucial to further examine open challenges and subproblems as well as to investigate the applied methodologies for possible revision or further development.

The current study provides a broad overview of the applications of big data in transport areas, which is the initial step in understanding the characteristics and limitations of present challenges and opportunities for further research in the field.

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This research is financed by the Research, Innovation and Excellence Program of the University of Thessaly.

Open access funding provided by HEAL-Link Greece. This research is supported by the Research, Innovation and Excellence Program of the University of Thessaly.

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The authors confirm their contribution to the paper as follows: study conception and design done by DT and EN; DT helped in data collection; DT, EN, and KK done analysis and interpretation of results, draft manuscript preparation. All authors reviewed the results and approved the final version of the manuscript.

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Tzika-Kostopoulou, D., Nathanail, E. & Kokkinos, K. Big data in transportation: a systematic literature analysis and topic classification. Knowl Inf Syst (2024). https://doi.org/10.1007/s10115-024-02112-8

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Received : 06 August 2023

Revised : 26 January 2024

Accepted : 21 March 2024

Published : 08 May 2024

DOI : https://doi.org/10.1007/s10115-024-02112-8

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Literature searches and literature reviews for transportation research projects.

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Crack and Concrete Deck Sealant Performance

Snow removal at extreme temperatures, development of a concrete maturity test protocol, quality of life: assessment for transportation performance measures.

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Excerpted from  Literature Searches and Literature Reviews for Transportation Research Projects: How to Search, Where to Search, and How to Put It All Together: Current Practices .   

Crack and Concrete Deck Sealant Performance Karl Johnson, Arturo Schultz, Catherine French, Jacob Reneson Minnesota Department of Transportation Report No. MN/RC 2009-13, March 2009. http://www.lrrb.org/media/reports/200913.pdf 

The appendix of this report includes a thorough summary of each study cited in the literature review. The review itself, however, effectively synthesizes this raw information into a more useful form that supports the overall paper’s purpose of defining the current state of the art regarding bridge deck sealants and crack sealers.

The literature review addresses bridge deck sealants and crack sealers in turn. Regarding deck sealants, it defines the two categories of sealants, the four performance measures used to evaluate sealants, and variables that affect performance such as concrete parameters and environmental conditions. The section on crack sealers discusses different types of sealers, their properties and application methods, performance measures, general trends in their effectiveness and variables affecting performance.

While there isn’t a specific “Gaps in Findings” section, this literature review effectively notes these gaps throughout the review, identifying areas for nearly every topic that existing research has not investigated as well as noteworthy limits to specific research projects cited. Of particular note is how the review identifies a shortcoming with a widely used deck sealant evaluation procedure and a suitable method to compensate for it:

It should be noted that the NCHRP Series II procedure, which is commonly used by vendors and state highway agencies to evaluate sealer performance, does not implement abrasion or freeze–thaw exposure to which sealers on bridge decks are frequently subjected. However, in determining the absorption properties of concrete sealers, a test was developed by Alberta Department of Transportation and Utilities which is essentially a modification of the NCHRP 244 procedure that incorporates abrasion (Kottke, 1987). Absorption is measured before and after abrading 0.04 in. off the faces of treated, cubic specimens to measure quantitatively the effect of abrasion on the absorption characteristics of sealers (p. 5).

The report clearly identifies the deck sealants and crack sealers that performed best for each of the performance measures, while noting how differences in test procedures can affect results. This provides useful information to support the report’s overall conclusions and recommendations. 

Snow Removal at Extreme Temperatures Michelle Akin, Jiang Huang, Xianming Shi, David Veneziano, Dan Williams Clear Roads Program, Minnesota Department of Transportation, March 2013. http://www .clearroads.org/downloads/Snow-Removal-Extreme-Temps-Final-Report.pdf

This report is immediately noteworthy for the thoroughness of its literature review in Appendix A, which makes up more than two-thirds of the report: 47 of 72 pages. Moreover, it includes international research and research from fields such as airports where snow-removal practices are different but potentially relevant to the work of state DOTs. The literature review also represents a clear topical organization, first providing an overview of literature available on various deicing chemicals with a focus on their physical properties, and then reviewing various strategies for clearing snow and ice from roads at low temperatures. 

Development of a Concrete Maturity Test Protocol  

W. James Wilde Center for Transportation Research and Implementation, Minnesota State University, Mankato Report No. MN/RC 2013-10, April 2013. http://www.dot.state.mn.us/research/TS/2013/201310.pdf 

Field and laboratory studies were undertaken to evaluate the applicability of the concrete maturity method to establishing criteria for opening portland cement concrete pavements to traffic. The field study included visits to18 paving projects in Minnesota over a 3-year period. At these projects, different sensor types were evaluated. In the laboratory study, 2-in. mortar cubes were tested to develop sensitivity analyses related to the proportions of cementitious materials, water–cementitious materials ratio, and other mix components. The literature review chapter of the report summarizes and discusses the literature regarding (1) the maturity method in general and its use in concrete pavements in particular; (2) supplementary cementing materials; (3) maturity and flexural strength; and (4) various types of sensors for measuring maturity.

Quality of Life: Assessment for Transportation Performance Measures  

Ingrid Schneider, Tian Guo, Sierra Schroeder Minnesota Department of Transportation Report No. MN/RC 2013-05, January 2013. http://www.dot.state.mn.us/research/TS/2013/2013-05.pdf​

This report investigates a topic (the effect of transportation on quality of life) with relatively little published research and none that addresses the topic comprehensively. To provide context for the report, the researchers start with a broader assessment of research into quality of life. This assessment defines key terms relevant to the study as well as methodologies that have been used to measure and predict quality of life, with a number of demographic distinctions.

Connecting the literature to transportation requires something of a patchwork approach, collecting papers that illuminate some specific element of transportation’s effect on quality of life to give as complete a picture as possible. Chapter 2 reports on the limited assessments that have been conducted as well as the strengths and weaknesses of their methodologies, organized by the specific factor investigated. In doing so, the literature review clearly delineates what is known and what is not known about the subject. 

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