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Article

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

1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
Department of Urban and Rural Planning, School of Architecture and Design, Southwest Jiaotong University, Chengdu 611756, China
3
Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China
4
Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(23), 6733; https://doi.org/10.3390/su11236733
Received: 12 November 2019 / Revised: 26 November 2019 / Accepted: 26 November 2019 / Published: 27 November 2019
(This article belongs to the Special Issue New Concepts for Regeneration of Industrial Cities)
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. View Full-Text
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 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
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MDPI and ACS Style

Zhao, R.; Yang, L.; Liang, X.; Guo, Y.; Lu, Y.; Zhang, Y.; Ren, X. Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making. Sustainability 2019, 11, 6733. https://doi.org/10.3390/su11236733

AMA Style

Zhao R, Yang L, Liang X, Guo Y, Lu Y, Zhang Y, Ren X. Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making. Sustainability. 2019; 11(23):6733. https://doi.org/10.3390/su11236733

Chicago/Turabian Style

Zhao, Rui, Linchuan Yang, Xinrong Liang, Yuanyuan Guo, Yi Lu, Yixuan Zhang, and Xinyun Ren. 2019. "Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making" Sustainability 11, no. 23: 6733. https://doi.org/10.3390/su11236733

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