| Title |
Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making |
| ID_Doc |
14934 |
| Authors |
Zhao, R; Yang, LC; Liang, XR; Guo, YY; Lu, Y; Zhang, YX; Ren, XY |
| Title |
Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making |
| Year |
2019 |
| Published |
Sustainability, 11, 23 |
| DOI |
10.3390/su11236733 |
| 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. |
| Author 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 |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000508186400205 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
https://www.mdpi.com/2071-1050/11/23/6733/pdf?version=1575445130
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