Most early research on route choice behavior analysis relied on the data collected from the stated preference survey or through small-scale experiments. This manuscript focused on the understanding of commuters' route choice behavior based on the massive amount of trajectory data collected from occupied taxicabs. The underlying assumption was that travel behavior of occupied taxi drivers can be considered as no different than the well-experienced commuters. To this end, the DBSCAN algorithm and Akaike information criterion (AIC) were first used to classify trips into different categories based on the trip length. Next, a total of 9 explanatory variables were defined to describe the route choice behavior, and and the path size (PS) logit model was then built, which avoided the invalid assumption of independence of irrelevant alternatives (IIA) in the commonly seen multinomial logit (MNL) model. The taxi trajectory data from over 11,000 taxicabs in Xi'an, China, with 40 million trajectory records each day were used in the case study. The results confirmed that commuters' route choice behavior are heterogenous for trips with varying distances and that considering such heterogeneity in the modeling process would better explain commuters' route choice behaviors, when compared with the traditional MNL model.


Civil, Architectural and Environmental Engineering


The research is supported by the National Key Research and Development Program of China (grant no. 2018YFB1600900), the Shaanxi Provincial Science and Technological Project (grant nos. 2020JM-244), and the Science and Technological Project of Shaanxi Provincial Transport Department (grant no. 19-24X).

International Standard Serial Number (ISSN)

0197-6729; 2042-3195

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Article - Journal

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Final Version

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© 2020 The Authors, All rights reserved.

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Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publication Date

09 Sep 2020