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.
Highlights
Cities worldwide have grappled with various conspicuous issues resulting from the excessive use of motor vehicles, including but not limited to traffic congestion, air/noise pollution, environmental degradation, limited green spaces, and carbon emissions
By investigating last-mile travel modes of Chengdu residents, this study comprehensively evaluates and ranks four travel modes and various travel-mode combinations in different travel distances to verify the effectiveness of the proposed data-mining-based weighting and multiple attribute decision making (MADM) coupling method
Identifying the optimal mode for last-mile travel can be deemed as a MADM problem, which involves the determination of weights according to some methods
Summary
Cities worldwide have grappled with various conspicuous issues resulting from the excessive use of motor vehicles, including but not limited to traffic congestion, air/noise pollution, environmental degradation, limited green spaces, and carbon emissions. The AHP method has received thriving scholarly attention, and quite a few criticisms (e.g., rank reversal) and has been extensively refined by numerous researchers [16] and evolved into weighting assignment for a number of criteria in a hierarchical indicator system [17] It highly hinges on, and is distorted by, the knowledge or experience of resorted individual experts, though it has been substantially improved; and it is time-consuming in computation, especially when the number of alternatives or attributes is large in the decision-making process [18]. Four last-mile modes, including walking, bike-sharing, community bus (shortened to “bus” hereinafter), and RSS, serve as decision-making units and constitute the choice set; three indicators, travel time, monetary cost, and environmental performance, are set as evaluation criteria; and Sina Weibo data are used to determine the weights of the proposed criteria (data mining).
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