01787208

Component

https://doi.org/10.1177/03611981211049422

Efficient dynamic ride-matching (DRM) in large-scale transportation systems is a key driver in transport simulations to yield answers to challenging problems. Although the DRM problem is simple to solve, it quickly becomes a computationally challenging problem in large-scale transportation system simulations. Therefore, this study thoroughly examines the DRM problem dynamics and proposes an optimization-based solution framework to solve the problem efficiently. To benefit from parallel computing and reduce computational times, the problem’s network is divided into clusters utilizing a commonly used unsupervised machine learning algorithm along with a linear programming model. Then, these sub-problems are solved using another linear program to finalize the ride-matching. At the clustering level, the framework allows users adjusting cluster sizes to balance the trade-off between the computational time savings and the solution quality deviation. A case study in the Chicago Metropolitan Area, U.S., illustrates that the framework can reduce the average computational time by 58% at the cost of increasing the average pick up time by 26% compared with a system optimum, that is, non-clustered, approach. Another case study in a relatively small city, Bloomington, Illinois, U.S., shows that the framework provides quite similar results to the system-optimum approach in approximately 62% less computational time.

Taner Cokyasar https://orcid.org/0000-0001-9687-6725 © National Academy of Sciences: Transportation Research Board 2021.

English

0000-0001-9687-6725

0000-0002-4858-141X

0000-0002-2492-0093

0000-0002-4831-7475

pp 172-182

2022-3

Transportation Research Record: Journal of the Transportation Research Board

Web

References (26)

Intercity bus lines; Mobility; Public transit

Bloomington (Illinois); Chicago (Illinois)

Passenger Transportation; Public Transportation

TRIS, TRB, ATRI

Oct 19 2021 3:15PM