Augmenting Transit Trip Characterization and Travel Behavior Comprehension

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.

Analyzing Large-Scale Smart Card Data to Investigate Public Transport Travel Behaviour Using Big Data Analytics

“…Different measures regarding the variability of travel behaviors of transit users were proposed. Chu and Chapleau [11] indicated the use of advance statistics, sophisticated GIS analysis, visualization, and machine learning and data mining to show travel behavior of passengers. Mohamed et al [12] showed a way to deal with analyzing the temporal behavior of the travelers in a public transportation system to obtain relevant clusters.…”

Constructing Transit Origin–Destination Matrices with Spatial Clustering

“…For instance, Chu and Chapleau used the term "anchor points" to define the places that a person repeatedly visits, which usually include residence, work, or study locations (21). They performed spatial aggregation by grouping stops within 50 m of each other to form a new node.…”

Estimation of transit origin destination matrices using smart card fare data

“…Although clustering groups method has the capability to characterize trips based on the passengers' travel behaviour (Chu and Chapleau, 2010), clustering groups technique cannot capture the complexity of travel patterns . In addition, clustering method is based on temporal and spatial variables and does not reflect passengers' motivation for travelling, whereas a classification method based on trip purpose does (Kuhlman, 2015).…”

“…However, trips that take place in the morning peak and have return trips in the evening peak were considered as work trips (Lee and Hickman, 2014). Chu and Chapleau (2010) and Lee and Hickman (2014) considered trips made with student cards and where the alighting for these trips were near schools or universities as education trips. In addition, trips that are at the end of the day, and not the only trips for corresponding passengers, are considered as home trips (Devillaine et al, 2012).…”

“…The range of considered trip purposes varied from just work trips (Jun and Dongyuan, 2013;Zhou et al, 2014), to the basic trip purposes such as work and home trips (Chakirov and Erath, 2012), and in some studies education trips are also considered (Chu and Chapleau, 2010;Devillaine et al, 2012).…”