GA-based Multi-modal Rideshare Matching Solution with Public Transportation
Rideshare is one way to share and improve mobility in transportation without increasing traffic demand. However, current research allows only one-modal trips and may be limited in the matching efficiency, especially when there is a large gap between the supply and demand of mobility. Therefore, this paper attempts to develop a multi-modal matching framework of shared mobility with public transportation and to evaluate its performance regarding spatial and temporal flexibility of rideshare. Genetic Algorithm is used to verify the multi-modal matching framework developed in this paper and a simplified network of Sioux Falls and its demand data are used for the performance evaluation. The results show that private vehicles, due to the flexible routes, achieve a much higher match rate than the public vehicles. Also, the potential of public transportation in a rideshare system may not be significant as foreseen, with only a slight increase in matching efficiency. As well, as schedule flexibility increases, the match rate increases largely even at a low supply of private vehicles, but not for public vehicles with rigid route. This confirms the need for a flexible design of sharing mobility, as can be fulfilled with the proposed multi-modal matching framework.
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Supplemental Notes:
- This paper was sponsored by TRB committee AP020 Standing Committee on Emerging and Innovative Public Transport and Technologies. Alternate title: Genetic Algorithm-Based Multimodal Rideshare Matching Solution with Public Transportation
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Woo, Soomin
- Yeo, Hwasoo
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Conference:
- Transportation Research Board 96th Annual Meeting
- Location: Washington DC, United States
- Date: 2017-1-8 to 2017-1-12
- Date: 2017
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References;
- Pagination: 16p
- Monograph Title: TRB 96th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Genetic algorithms; Mobility; Multimodal transportation; Public transit; Ridesharing
- Candidate Terms: Shared-use vehicle systems
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Public Transportation;
Filing Info
- Accession Number: 01626054
- Record Type: Publication
- Report/Paper Numbers: 17-01305
- Files: TRIS, TRB, ATRI
- Created Date: Feb 15 2017 5:03PM