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.

Big Data Analytics in Intelligent Transportation Systems: A Survey

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.

Mining smart card data for transit riders’ travel patterns

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.

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Mobility as a Service: A Critical Review of Definitions, Assessments of Schemes, and Key Challenges

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

Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile

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.

The promises of big data and small data for travel behavior (aka human mobility) analysis.

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.

Smart card data use in public transit: A literature review

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 use in the transit planning context. First, the technologies are presented: the hardware and information systems required to operate these tools. Privacy concerns and legal issues related to the dissemination of smart card data, data storage, and encryption are also 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 survival models applied to ridership.

/pdf/smart-card-data-in-public-transit-planning-a-review-3y5ofveh8q.pdf

Smart Card Data in Public Transit Planning: A Review

OBJECTIVES
A few clinical screening tools for auditory processing disorder (APD) are currently available. However, most of these tools are in English and cannot be used to screen individuals with a first language other than English. This study aimed to develop an APD screening test battery in French and to assess its psychometric properties in detecting school-age children at risk of APD.


METHOD
Prior to their complete audiological APD assessment, 53 children (7-12 years old) were recruited from an audiology clinic. The APD assessment lasted between 2 and 3 hr, including 15-20 min for the screening test battery. The screening test battery was composed of four behavioral subtests and two questionnaires (parent and teacher).


RESULTS
When combined, two among the four behavioral subtests showed a sensitivity of 100% and a specificity of 80%.


CONCLUSION
The newly developed screening tool could help reduce the number of unnecessary APD assessments, therefore allowing early diagnosis in children with APD and increasing their chances of receiving adequate intervention.

Marie-Pier Pelletier

Papers

Smart card data use in public transit: A literature review

Smart Card Data in Public Transit Planning: A Review

Screening for Auditory Processing Disorder in French-Speaking School-Age Children: A Pilot Study.