Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making

Rui Zhao, Linchuan Yang, Xinrong Liang, Yuanyuan Guo, Yi Lu, Yixuan Zhang and Xinyun Ren
Additional contact information
Rui Zhao: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Linchuan Yang: Department of Urban and Rural Planning, School of Architecture and Design, Southwest Jiaotong University, Chengdu 611756, China
Xinrong Liang: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Yuanyuan Guo: Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
Yi Lu: Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
Yixuan Zhang: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Xinyun Ren: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Sustainability, 2019, vol. 11, issue 23, 1-15

Abstract: Transit offers stop-to-stop services rather than door-to-door services. The trip from a transit hub to the final destination is often entitled as the “last-mile” trip. This study innovatively proposes a hybrid approach by combining the data mining technique and multiple attribute decision making to identify the optimal travel mode for last-mile, in which the data mining technique is applied in order to objectively determine the weights. Four last-mile travel modes, including walking, bike-sharing, community bus, and on-demand ride-sharing service, are ranked based upon three evaluation criteria: travel time, monetary cost, and environmental performance. The selection of last-mile trip modes in Chengdu, China, is taken as a typical case example, to demonstrate the application of the proposed approach. Results show that the optimal travel mode highly varies by the distance of the “last-mile” and that bike-sharing serves as the optimal travel mode if the last-mile distance is no more than 3 km, whilst the community bus becomes the optimal mode if the distance equals 4 and 5 km. It is expected that this study offers an evidence-based approach to help select the reasonable last-mile travel mode and provides insights into developing a sustainable urban transport system.

Keywords: last-mile; data mining; multiple attribute decision making; travel mode selection; big data; bike-sharing; community bus; on-demand ride-sharing service; Sina Weibo; China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/23/6733/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/23/6733/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2019:i:23:p:6733-:d:291537

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().