Modeling users' vehicles selection behavior in the urban carsharing program

Songhua Hu; Hangfei Lin; Kun Xie; Xiaohong Chen; Hongjie Shi

doi:10.1109/ITSC.2018.8569386

Modeling users' vehicles selection behavior in the urban carsharing program

Songhua Hu, Hangfei Lin, Kun Xie, Xiaohong Chen, Hongjie Shi

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

15 Citations (Scopus)

Abstract

Electric carsharing network has been expanding at a very fast rate in the last few years, accompanied by more complex challenges to carsharing operators. Increasing vehicles usage allows for benefits maximization. However, vehicles usage varies significantly in a fleet because of users' preference to vehicles with different features. This study investigated contributing factors to users' vehicles selection behavior through random forests and binary logistic regression using the administrative datasets collected from EVCARD carsharing program. Results showed state of charge (SOC) of electric vehicles and the number of available vehicles parked at a station had the greatest effect on user's vehicles selection behavior. Users tend to be greedy rather than rational when making decisions as they always choose the vehicles with the maximum SOC even their real trips are short. The attributes of trips and users, like real trip distances and users' familiarity with carsharing program, also play an important role in the selection process. Findings from this research can be beneficial to carsharing operators to prioritize investments when purchasing new vehicles and develop optimal management strategies to enhance existing vehicles attraction for users. © 2018 IEEE.

Original language	English
Title of host publication	2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018
Publisher	IEEE
Pages	1546-1551
Volume	2018-November
ISBN (Print)	9781728103235
DOIs	https://doi.org/10.1109/ITSC.2018.8569386
Publication status	Published - 7 Dec 2018
Externally published	Yes
Event	21st IEEE International Conference on Intelligent Transportation Systems (ITSC 2018) - Maui, United States Duration: 4 Nov 2018 → 7 Nov 2018 https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8569487 (Link to IEEE Program book)

Publication series

Name	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
Volume	2018-November

Conference

Conference	21st IEEE International Conference on Intelligent Transportation Systems (ITSC 2018)
Abbreviated title	IEEE ITSC '18
Place	United States
City	Maui
Period	4/11/18 → 7/11/18
Internet address	https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8569487 (Link to IEEE Program book)

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.

Funding

The authors would like to thank EVCARD carsharing program in Shanghai for providing the data necessary for study. The contents of this paper reflect views of the authors who are responsible for the facts and accuracy of the data presented herein. The contents of the paper do not necessarily reflect the official views or policies of the agencies.

UN SDGs

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

SDG 11 Sustainable Cities and Communities

Research Keywords

carsharing
electric vehicles
logistic regression
random forests
user's selection behavior

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10.1109/ITSC.2018.8569386

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Hu, S., Lin, H., Xie, K., Chen, X., & Shi, H. (2018). Modeling users' vehicles selection behavior in the urban carsharing program. In 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018 (Vol. 2018-November, pp. 1546-1551). Article 8569386 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November). IEEE. https://doi.org/10.1109/ITSC.2018.8569386

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abstract = "Electric carsharing network has been expanding at a very fast rate in the last few years, accompanied by more complex challenges to carsharing operators. Increasing vehicles usage allows for benefits maximization. However, vehicles usage varies significantly in a fleet because of users' preference to vehicles with different features. This study investigated contributing factors to users' vehicles selection behavior through random forests and binary logistic regression using the administrative datasets collected from EVCARD carsharing program. Results showed state of charge (SOC) of electric vehicles and the number of available vehicles parked at a station had the greatest effect on user's vehicles selection behavior. Users tend to be greedy rather than rational when making decisions as they always choose the vehicles with the maximum SOC even their real trips are short. The attributes of trips and users, like real trip distances and users' familiarity with carsharing program, also play an important role in the selection process. Findings from this research can be beneficial to carsharing operators to prioritize investments when purchasing new vehicles and develop optimal management strategies to enhance existing vehicles attraction for users. {\textcopyright} 2018 IEEE.",

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Hu, S, Lin, H, Xie, K, Chen, X & Shi, H 2018, Modeling users' vehicles selection behavior in the urban carsharing program. in 2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. vol. 2018-November, 8569386, IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, vol. 2018-November, IEEE, pp. 1546-1551, 21st IEEE International Conference on Intelligent Transportation Systems (ITSC 2018), Maui, Hawaii, United States, 4/11/18. https://doi.org/10.1109/ITSC.2018.8569386

Modeling users' vehicles selection behavior in the urban carsharing program. / Hu, Songhua; Lin, Hangfei; Xie, Kun et al.
2018 IEEE Intelligent Transportation Systems Conference, ITSC 2018. Vol. 2018-November IEEE, 2018. p. 1546-1551 8569386 (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Vol. 2018-November).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Lin, Hangfei

AU - Xie, Kun

AU - Chen, Xiaohong

AU - Shi, Hongjie

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to <a href="mailto:[email protected]">[email protected]</a>.

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N2 - Electric carsharing network has been expanding at a very fast rate in the last few years, accompanied by more complex challenges to carsharing operators. Increasing vehicles usage allows for benefits maximization. However, vehicles usage varies significantly in a fleet because of users' preference to vehicles with different features. This study investigated contributing factors to users' vehicles selection behavior through random forests and binary logistic regression using the administrative datasets collected from EVCARD carsharing program. Results showed state of charge (SOC) of electric vehicles and the number of available vehicles parked at a station had the greatest effect on user's vehicles selection behavior. Users tend to be greedy rather than rational when making decisions as they always choose the vehicles with the maximum SOC even their real trips are short. The attributes of trips and users, like real trip distances and users' familiarity with carsharing program, also play an important role in the selection process. Findings from this research can be beneficial to carsharing operators to prioritize investments when purchasing new vehicles and develop optimal management strategies to enhance existing vehicles attraction for users. © 2018 IEEE.

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ER -

Modeling users' vehicles selection behavior in the urban carsharing program

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Research Keywords

Access Output

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