Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data

Zhen Guo; Mengyan Hao; Bin Yu

doi:10.1109/ITSC45102.2020.9294668

Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data

Zhen Guo
, Mengyan Hao
, Bin Yu^*

^*Corresponding author for this work

School of Transportation Science and Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers' route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.

Original language	English
Title of host publication	2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728141497
DOIs	https://doi.org/10.1109/ITSC45102.2020.9294668
State	Published - 20 Sep 2020
Event	23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 - Rhodes, Greece Duration: 20 Sep 2020 → 23 Sep 2020

Publication series

Name	2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020

Conference

Conference	23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
Country/Territory	Greece
City	Rhodes
Period	20/09/20 → 23/09/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure
SDG 11 Sustainable Cities and Communities

Keywords

High-resolution GPS data
Logit model
Taxi customer searching behavior

Access to Document

10.1109/ITSC45102.2020.9294668

Cite this

Guo, Z., Hao, M., & Yu, B. (2020). Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020 Article 9294668 (2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITSC45102.2020.9294668

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title = "Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data",

abstract = "Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers' route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.",

keywords = "High-resolution GPS data, Logit model, Taxi customer searching behavior",

author = "Zhen Guo and Mengyan Hao and Bin Yu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020 ; Conference date: 20-09-2020 Through 23-09-2020",

year = "2020",

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doi = "10.1109/ITSC45102.2020.9294668",

language = "英语",

series = "2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020",

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Guo, Z, Hao, M & Yu, B 2020, Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data. in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020., 9294668, 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020, Institute of Electrical and Electronics Engineers Inc., 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020, Rhodes, Greece, 20/09/20. https://doi.org/10.1109/ITSC45102.2020.9294668

Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data. / Guo, Zhen; Hao, Mengyan; Yu, Bin.
2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9294668 (2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data

AU - Guo, Zhen

AU - Hao, Mengyan

AU - Yu, Bin

PY - 2020/9/20

Y1 - 2020/9/20

N2 - Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers' route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.

AB - Occupancy of vacant taxis brings waste of resources and heavy traffic pressure to urban traffic network. To enable better efficiency of taxis and less traffic congestion, a logit-based model is developed to describe vacant taxi drivers' route choice behavior and decision-making mechanism when searching for next customer. The proposed model is based on a multinomial logit model (MNL), considering two novel influencing indicators including the path unreliability (PU) and expected rate of return (EROR). In order to simplify the model construction and reduce computational cost, we divide the research area into identical squares with a 0.5-km resolution. Then the customer searching movements are extracted from the high-resolution GPS data of more than 8000 taxis in Shanghai to validate the logit model. The model results show that the customer searching behavior of vacant taxi drivers is significantly influenced by PU and EROR. Moreover, the effect of these two indicators on customer searching behavior varies with the time of day.

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KW - Logit model

KW - Taxi customer searching behavior

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Guo Z, Hao M, Yu B. Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data. In 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9294668. (2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020). doi: 10.1109/ITSC45102.2020.9294668

Modeling Taxi Customer Searching Behavior Using High-Resolution GPS Data

Abstract

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UN SDGs

Keywords

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