Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data

Yajuan Deng,Renjie He,Qing Tang,Xianbiao Hu,Meiye Li

doi:10.1155/2020/8836511

Abstract

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.

Highlights

Analysis of the routing choice behavior provides theoretical support for route guidance and traffic assignment
Most early research studies on route choice behavior were based on the data collected from stated preference (SP) surveys or through small-scale experiments that were usually limited in data size or number of participants
In order to address the independence of irrelevant alternatives (IIA) issue of the multinomial logit (MNL) model, various modified models were proposed, such as the C-logit model and path size (PS)-logit model [4, 5], which were built by adding a modification term in the utility function to characterize the interactions among different routes

Summary

Introduction

Analysis of the routing choice behavior provides theoretical support for route guidance and traffic assignment. After that, based on the hypothesis that the random term of route utility function follows the Gumbel distribution, Dial constructed a discrete multinomial logit (MNL) model for multimode selection [2, 3]. According to the generalized extreme value (GEV) theorem proposed by McFadden, some researchers proposed CNL and PCL models [6, 7] to avoid the IIA assumption of the MNL model. These early research studies on route choice behavior lacked real-world data and were restricted by the algorithm complexity, and the numbers of explanatory variables used were usually limited as well

Methods

Results

Conclusion