Smart card data use in public transit: A literature review
01 Aug 2011-Transportation Research Part C-emerging Technologies (Pergamon)-Vol. 19, Iss: 4, pp 557-568
TL;DR: The most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.
Abstract: Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions. These data can be very useful to transit planners, from the day-to-day operation of the transit system to the strategic long-term planning of the network. This review covers several aspects of smart card data use in the public transit context. First, the technologies are presented: the hardware and information systems required to operate these tools; and privacy concerns and legal issues related to the dissemination of smart card data, data storage, and encryption are addressed. Then, the various uses of the data at three levels of management are described: strategic (long-term planning), tactical (service adjustments and network development), and operational (ridership statistics and performance indicators). Also reported are smart card commercialization experiments conducted all over the world. Finally, the most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.
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TL;DR: Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced.
Abstract: Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.
627 citations
Cites background from "Smart card data use in public trans..."
...information on travel behaviour [17], [21], smart card data is becoming a significant component of public transportation services planning and management....
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TL;DR: Wang et al. as mentioned in this paper proposed an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China and identified trip chains based on the temporal and spatial characteristics of their smart card transaction data.
Abstract: To mitigate the congestion caused by the ever increasing number of privately owned automobiles, public transit is highly promoted by transportation agencies worldwide. A better understanding of travel patterns and regularity at the “magnitude” level will enable transit authorities to evaluate the services they offer, adjust marketing strategies, retain loyal customers and improve overall transit performance. However, it is fairly challenging to identify travel patterns for individual transit riders in a large dataset. This paper proposes an efficient and effective data-mining procedure that models the travel patterns of transit riders in Beijing, China. Transit riders’ trip chains are identified based on the temporal and spatial characteristics of their smart card transaction data. The Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm then analyzes the identified trip chains to detect transit riders’ historical travel patterns and the K-Means++ clustering algorithm and the rough-set theory are jointly applied to cluster and classify travel pattern regularities. The performance of the rough-set-based algorithm is compared with those of other prevailing classification algorithms. The results indicate that the proposed rough-set-based algorithm outperforms other commonly used data-mining algorithms in terms of accuracy and efficiency.
510 citations
29 Jun 2017
TL;DR: In this paper, the authors define a set of attributes through a literature review, which is then used to describe selected mobility as a service (MaaS) schemes and existing applications, and examine the potential implications of the identified core characteristics of the service on the following three areas of transport practices.
Abstract: Mobility as a Service (MaaS) is a recent innovative transport concept, anticipated to induce significant changes in the current transport practices. However, there is ambiguity surrounding the concept; it is uncertain what are the core characteristics of MaaS and in which way they can be addressed. Further, there is a lack of an assessment framework to classify their unique characteristics in a systematic manner, even though several MaaS schemes have been implemented around the world. In this study, we define this set of attributes through a literature review, which is then used to describe selected MaaS schemes and existing applications. We also examine the potential implications of the identified core characteristics of the service on the following three areas of transport practices: travel demand modelling, a supply-side analysis, and designing business model. Finally, we propose the necessary enhancements needed to deliver such an innovative service like MaaS, by establishing the state of art in those fields.
475 citations
TL;DR: This paper presents a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile and generates an estimation of time and position of alighting for over 80% of the boarding transactions.
Abstract: A high-quality Origin–Destination (OD) matrix is a fundamental prerequisite for any serious transport system analysis However, it is not always easy to obtain it because OD surveys are expensive and difficult to implement This is particularly relevant in large cities with congested networks, where detailed zonification and time disaggregation require large sample sizes and complicated survey methods Therefore, the incorporation of information technology in some public transport systems around the world is an excellent opportunity for passive data collection In this paper, we present a methodology for estimating a public transport OD matrix from smartcard and GPS data for Santiago, Chile The proposed method is applied to two 1-week datasets obtained for different time periods From the data available, we obtain detailed information about the time and position of boarding public transportation and generate an estimation of time and position of alighting for over 80% of the boarding transactions The results are available at any desired time–space disaggregation After some post-processing and after incorporating expansion factors to account for unobserved trips, we build public transport OD matrices
445 citations
TL;DR: The purpose of this paper is to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two.
Abstract: The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called "travel behavior analysis" or "human mobility analysis"). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.
425 citations
Cites background from "Smart card data use in public trans..."
...…sightings generated by phone operators for operation purposes (Calabrese et al., 2011), social media data generated voluntarily by users’ online activities (Chen and Schintler, forthcoming), and smartcard data collected at many transit systems worldwide (Pelletier et al., 2011; Ma et al., 2013)....
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..., 2011), social media data generated voluntarily by users’ online activities (Chen and Schintler, forthcoming), and smartcard data collected at many transit systems worldwide (Pelletier et al., 2011; Ma et al., 2013)....
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References
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Book•
18 Feb 2005
TL;DR: In this paper, the authors introduce theoretical concepts and relationships in transit systems, followed by practical methodologies for operations, planning, and analysis, including transit lines and networks; planning of rail transit station locations; transit agency operations, economics, and marketing; transit fares; financing of transit; transit ownership, regulation, and organization; transit systems planning; analysis, evaluation, and selection of transit modes; and planning and selecting of medium and high-performance transit modes.
Abstract: Transportation influences the form of cities and their livability, their economic, social, and environmental characteristics. In recent years, urban public transportation (transit systems) has begun to grow again. Transit has great possibilities for reducing traffic congestion, offering alternative means of travel, and contributing greatly to the quality of urban life. This textbook introduces theoretical concepts and relationships in transit systems, followed by practical methodologies for operations, planning, and analysis. The book offers 12 chapters in three sections: transit systems operations and networks; transit agency economics and organization; and transit systems planning and mode selection. Topics include transit operations and service scheduling; capacity, speed, accelerated, and special operations; modeling and optimization in transit systems analysis; transit lines and networks; planning of rail transit station locations; transit agency operations, economics, and marketing; transit fares; financing of transit; transit ownership, regulation, and organization; transit systems planning; analysis, evaluation, and selection of transit modes; and planning and selection of medium- and high-performance transit modes. Each chapter includes tables and diagrams, practice exercises, and references; a subject index and answers to the practical exercises conclude the volume.
737 citations
TL;DR: There are limitations, mainly that trip length is not recorded on systems based on validating cards only on entry to a bus, and that certain types of data still require direct survey methods for their collection (such as journey purpose).
386 citations
TL;DR: A model to estimate the destination location for each individual boarding a bus with a smart card, with a success rate of 66% for destination estimation and reaching about 80% at peak hours is presented.
323 citations
TL;DR: The potential of smart-card data for measuring the variability of urban public transit network use is the focus of this paper and measures of spatial and temporal variability of transit use for various types of card are defined and estimated.
266 citations
TL;DR: Results show that the public transport users of this study can rapidly be divided in four major behavioural groups, whatever type of ticket they use, which demonstrates that a combination of planning knowledge and data mining tool allows producing travel behaviours indicators, mainly regarding regularity and daily patterns.
204 citations