01626054

Component

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.

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

01618707

17-01305

English

16p

2017

Transportation Research Board 96th Annual Meeting

Digital/other

Figures; References

Genetic algorithms; Mobility; Multimodal transportation; Public transit; Ridesharing

Shared-use vehicle systems

Highways; Operations and Traffic Management; Planning and Forecasting; Public Transportation

Transportation Research Board Annual Meeting 2017 Paper #17-01305

TRIS, TRB, ATRI

Dec 8 2016 10:24AM