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.

• 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
• Corporate Authors:

  Transportation Research Board

  500 Fifth Street, NW
  Washington, DC  United States  20001
• Authors:
  • Woo, Soomin
  • Yeo, Hwasoo
• 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