Robert Chapleau

Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System

The Smart Card Automated Fare Collection (SCAFC) system is an Intelligent Transportation System that is becoming increasingly popular among transit operators. In addition to fare control, the data collected by these systems can be very useful in transit planning. Many SCAFC systems store the location where the passenger boarded due to the positioning device carried onboard; however, in most systems alighting locations are not validated and, thus, not stored in databases. This article presents a model to estimate the destination location for each individual boarding a bus with a smart card. Experiments carried out with a database programming approach show that the data must be thoroughly validated and corrected prior to the estimation process. The first application of the model provided a success rate of 66% for destination estimation, reaching about 80% at peak hours. Further research will tackle the issues of error detection, correction, and link results, comparing them with those of other data sources.

Enriching Archived Smart Card Transaction Data for Transit Demand Modeling

Transaction data from public transit smart cards represent a continuous stream of detailed travel information for transit demand modeling. Although certain aspects of information are incomplete in unprocessed data, efforts are devoted to deriving a more comprehensive understanding of the system and its users from partial information through data enrichment processes, with a long-term goal of establishing a dynamic model of demand. On the basis of previous work, methods are proposed to estimate the arrival time of bus runs at the stop level by using temporal constraints and to identify linked trips by using spatial–temporal concepts. These enrichments lead to the reconstruction of individual itineraries, the analysis of transfer activity, and the synthesis of vehicle load profiles. The latter provide planners with a detailed spatial–temporal progression of each run, origin and destination stops for each individual transaction, and boarding and alighting activity at each stop. The study draws on more than 37,000 smart card boarding transactions of an average weekday from a midsize transit agency. Results suggest that linked trips represent slightly above 10% of the total number of transactions in the network and the smart card system overestimates the proportion of linked trips by nearly 40%. The outcome is promising and lays a foundation to further enrich the itineraries by associating the boarding and alighting stops with trip generators, deriving trip purposes, and performing multiday analysis.

Augmenting Transit Trip Characterization and Travel Behavior Comprehension: Multiday Location-Stamped Smart Card Transactions

Trips need to be described and have always been characterized by various levels of abstraction. It varies from a simple label such as home-based work to complete itinerary with sociodemographic characteristics of the trip maker and household. The rationale behind such classifications is that planners and modelers recognize that the demand of transportation is highly differentiated. It is hoped that additional attributes would provide a more complete portrait of the demand and an improved understanding of the underlying travel behavior. Passive data collection technologies bring an extra dimension to travel data acquisition. Multiday data, which are difficult to collect, become accessible. In public transit, a smart card automatic fare collection system with automatic vehicle location capability provides high-resolution longitudinal data on travel pattern but also suffers from the inherent limitations of passive methods. This paper proposes a methodology to enhance transit trip characterization by adding a multiday dimension to a month of smart card transactions. On the basis of an individual, anchor points—precise to an exact address—are detected. Boarding and alighting locations are described with respect to those anchors. The enhancement allows in-depth travel behavior analysis on a subgroup sharing a common anchor or an individual. The paper demonstrates the use of spatial statistics, spatial analyses with geographic information system, visualizations, and data mining to describe activity space and locations and departure time dynamics, and to derive monthly trip table, activity schedule, and behavioral rules for cardholders. The results offer promising insights to transit planning and the understanding of travel behavior.

Can trip planner log files analysis help in transit service planning

Transit trip planners are now found on most transit authority websites. This feature gives transit users a full itinerary from a point of origin to a destination. The web service on which the trip planner is installed usually stores usage logs on a daily basis.Log files contain data on origins, destinations, calculated paths, and other website entries. The purpose of this article is to determine whether the analysis of trip planner log files can help to improve transit service by providing better knowledge on transit users. A website oriented analysis and a transit oriented analysis based on 4 years of observations on the Montreal Transit Commission website are presented. Results show that, even though not all transit users have access to the Internet or use the planner regularly, log files can be useful for identifying new locations to be assessed by a transit system for better understanding user behaviors, and for guiding updates of the geographic information system and the trip planner itself.

Driver-Assisted Bus Interview: Passive Transit Travel Survey with Smart Card Automatic Fare Collection System and Applications

A new concept in transit travel surveys, called the driver-assisted bus interview, is proposed. The survey uses data that are passively gathered by smart card automatic fare collection systems on public transit. Its superiority lies in the resolution of the data as well as the continuous geographic and temporal coverage of the network and cardholders. The paper first discusses the quality of survey data. It then describes a totally disaggregate object-oriented approach as a method to understand, validate, correct, and enrich the data. The study uses one month of archived smart card boarding data from a medium-size transit agency. The data go through a validation and correction process that makes use of planned service and cardholders’ historic travel pattern. Trip data not collected by the survey are obtained through enrichment techniques. The anchor points of a cardholder can be inferred from the derived employment status, multiday travel pattern, and a trip-generator database. The procedure that infers trip destination and trip purpose for the student subgroup is explained. Advanced analysis and visualization techniques demonstrate the versatility of the data, which can be scrutinized as a travel demand survey, a special trip generator survey, a resource allocation and consumption survey, and a multiday survey.

DESTINATION ESTIMATION FROM PUBLIC TRANSPORT SMARTCARD DATA

Abstract In public transport authorities, Smartcard Automated Fare Collection System generate huge amount of information on user travel behaviour. This data can be very useful for service planning, network extensions and logistics planning. Unfortunately, in most systems, only the boarding locations of users are known. This paper proposes a model for the estimation of the destination (deboarding) locations. It also emphasizes on some issues related to smartcard data: privacy concerns, data structure, data errors, missing information. The experiments with real data show that about two-third of the trip destinations can be successfully derived with the model.

TRANSIT ITINERARY CALCULATION ON THE WEB: BASED ON A TRANSIT USER INFORMATION SYSTEM

In this article, a hybrid algorithm based on heuristics and optimization is presented for the calculation of urban transit itineraries including information on pedestrian access and egress paths, route sequences, schedules, and stops. The use of the Transit User Information System (TUIS) to support the calculations is emphasized. The TUIS uses the Totally Disaggregate Approach (TDA) and Transportation Object-Oriented Modeling (TOOM) in transportation to gather data on territory (for origin and destination specifications and for the pedestrian network) and transit operation (route geometry, schedules). Websites that have been implemented are referenced to demonstrate the applicability of the hybrid algorithm. These websites make use of some special techniques for disseminating user information over the Internet.

Use of Subway Smart Card Transactions for the Discovery and Partial Correction of Travel Survey Bias

Although the theoretical sources of bias in travel surveys have been documented, data that describe an entire population of travelers rarely permit the reliable detection and measurement of bias. The existence of large databases of smart card transactions in public transit systems presents an opportunity to do so. In this paper, a typical average weekday of travel demand data from the Montreal, Canada, household travel survey is confronted with a single, specific day of smart card transactions. The object of comparison is the Montreal subway system, which is involved in 10% of all daily trips within the metropolitan area. The results of the initial analysis indicate that although the survey accurately reproduces daily subway ridership, it overestimates subway boardings by 24% during peak periods. This overestimation can be corrected by adjusting the weights of home-based trips to match entry volumes at subway stations during the morning peak period. The results of the reweighting procedure suggested that francophone households that use transit had a greater propensity to respond to the survey compared with other households. Furthermore, even after reweighting, the travel survey underestimated off-peak demand by roughly 21%. The underestimation was likely attributable to underreporting of non–home-based trips by respondent households and nonresponse of specific population groups.

Origin-destination survey data dissemination in metropolitan context : A multimedia experience

Since the early 1970s, the Montreal Urban Community Transit Commission has held a series of six major origin-destination surveys in the greater Montreal area. These studies, which include approximately 50,000 households per survey, benefit from a totally disaggregate approach. This framework permits the spatialization of each trip end at the x-y coordinate level and associates multiple variables to every recorded trip. The dissemination of such a vast quantity of data requires different levels of resolution with respect to processing methods and software, zonal aggregation, itineraries, and sociodemographic variables. In this context, two tools have been created for public use: MADEOD (Origin-Destination Survey Data Disaggregate Analysis Model) and MADGEN (Trip Generator Disaggregate Analysis Model). Because of the recent and rapid evolution of multimedia technology, these tools have been developed in an interactive Microsoft Excel worksheet format and in hypertext markup language for the Groupe MADITUCs World Wide Web site on the Internet. Stand-alone multimedia presentations and a Windows help file have also been developed for tutorial use.

Transit Path Calculation Supported by Special Geographic Information System-Transit Information System

The Societe de Transport de la Communaute Urbaine de Montreal (STCUM; Montreal Urban Community Transit Corporation) has recently undertaken the task of computerizing its phone information center, called AUT-OBUS. AUT-OBUS provides callers with optimal route choices within the STCUM transit network. An original system based on geographic and operations data bases combined with interactive path calculation and data processing software has already been implemented. A special geographic information system was developed to process the origins and destinations on the basis of the clients specifications. Multiple forms of spatial referencing are provided: street addresses, trip generators and attractors, monuments, special activities, street intersections, transit references (subway and rail stations, bus routes, terminals, etc.). The best path calculation is carried out by using interactively calibrated impedance functions (walking, in-vehicle, waiting, transfers, fares, and mode restriction). An added challen...