Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data
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 occu...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8836511 |
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| author | Yajuan Deng Meiye Li Qing Tang Renjie He Xianbiao Hu |
| author_facet | Yajuan Deng Meiye Li Qing Tang Renjie He Xianbiao Hu |
| author_sort | Yajuan Deng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1dc4d9b867464550b42002913f506956 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-1dc4d9b867464550b42002913f5069562025-02-03T06:44:57ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88365118836511Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory DataYajuan Deng0Meiye Li1Qing Tang2Renjie He3Xianbiao Hu4College of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Transportation, Southeast University, Nanjing 211189, ChinaDepartment of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USACollege of Transportation Engineering, Chang’an University, Xi’an 710064, ChinaDepartment of Civil, Architectural and Environmental Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USAMost 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.http://dx.doi.org/10.1155/2020/8836511 |
| spellingShingle | Yajuan Deng Meiye Li Qing Tang Renjie He Xianbiao Hu Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data Journal of Advanced Transportation |
| title | Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data |
| title_full | Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data |
| title_fullStr | Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data |
| title_full_unstemmed | Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data |
| title_short | Heterogenous Trip Distance-Based Route Choice Behavior Analysis Using Real-World Large-Scale Taxi Trajectory Data |
| title_sort | heterogenous trip distance based route choice behavior analysis using real world large scale taxi trajectory data |
| url | http://dx.doi.org/10.1155/2020/8836511 |
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