Data-Driven Transit System Modeling Using Automated Fare Collection Data
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Automated Fare Collection (AFC) systems, often called smart transit card systems, have found use in public transportation systems worldwide. Not only do AFC systems enable a secure and fast way of fare collection, but also, they offer a cost-effective way of collecting and monitoring travel information of each user, recorded as time-stamped transactions. These data provide detailed travel information about transit system users that can potentially be informative for operators and planners for understanding traffic condition and travelers’ travel patterns, constructing models to find out travelers’ true ODs and optimize the transit route offerings. This dissertation is composed of four pieces related to the modeling and understanding of transit networks using AFC data.The first piece relates to methods for obtaining system level transit information from AFC data. Monitoring transit system "health" by extracting and tracking such quantities as travel time, transfer time, number of passengers, etc., is critical to the benefit of travelers, planners and operators within a transit system. This chapter presents methods for obtaining system level transit information from AFC system, which provides hour-to-hour, day-to-day transit information. The AFC data of public transit system in Seoul, South Korea is used as an example to illustrate the proposed data extraction methods and analysis, to further provide both methodological and practical guidance for researchers and data-handling analysts.The second piece investigates mobility patterns of various traveler groups. Characterizing individual mobility is critical for understanding urban dynamics and developing high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns, however, those datasets could not reveal individual travelers’ decision-making logic to distill any demographics-related trends. This piece uses AFC data to tackle this challenge and show how spatio-temporal mobility patterns vary over user characteristics and modal preferences. The third piece presents a methodology to identify individual travelers' true ODs as well as their travel preferences. Origin-Destination (OD) information is critical for enabling public transit system policy-makers and operators to serve travelers in a calculated way. Travelers' preferences in choosing best routes are also important to understand, in order to assess or predict the service levels offered by such a system. This piece presents a two-step methodological framework to identify individual travelers' true ODs as well as their travel preferences of route choice decisions. A presented case study, based on actual AFC data, demonstrates a high inference accuracy, both for travelers' true ODs and preferences.The fourth piece develops an idea of a data-driven modeling approach based on artificial neural network, to predict and recommend bus routing decisions for bus drivers and/or operators. For each bus, the model chooses its next station, based on many factors such as current road network, current heading direction, potential demands of passengers, the movement of other buses, etc., which can be obtained from AFC data, fast and easy.