Augmenting Transit Trip Characterization and Travel Behavior Comprehension

Ka Kee Alfred Chu · Robert Chapleau

78 citations0/0 files storedTransportation Research Record Journal of the Transportation Research Board

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Urban Transport and Accessibility

Abstract

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.

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References

2002Observing the rhythms of daily life: A six-week travel diary 2001Elimination of the Travel Diary: Experiment to Derive Trip Purpose from Global Positioning System Travel Data 2007Individual Trip Destination Estimation in a Transit Smart Card Automated Fare Collection System 2009Processing Raw Data from Global Positioning Systems without Additional Information 2007Measuring transit use variability with smart-card data 2017Urban Transportation Planning 2006Enhanced System for Link and Mode Identification for Personal Travel Surveys Based on Global Positioning Systems 2005A Trip Reconstruction Tool for GPS-based Personal Travel Surveys

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