Developing Optimal Peer-to-Peer Ridesharing Strategies

Lee, Young-Jae; Nickkar, Amirezza; Morgan State University; Urban Mobility & Equity Center

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Developing Optimal Peer-to-Peer Ridesharing Strategies

2023-08-01
By Lee, Young-Jae ; Nickkar, Amirezza

[PDF-1.59 MB]

English

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Details:

Creators:

Lee, Young-Jae ; Nickkar, Amirezza

Lee, Young-Jae ; Nickkar, Amirezza Less -
Corporate Creators:

Morgan State University ; Urban Mobility & Equity Center

Morgan State University ; Urban Mobility & Equity Center Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Costs Incentives Optimization Ridesharing Routes And Routing Subsidies
Publication/ Report Number:

UMEC-022
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

Urban Mobility & Equity Center
Abstract:

Thanks to recent developments in ride-hailing transit services, the Peer-to-Peer (P2P) ridematching problem has been actively considered in academia in recent years. P2P ridematching not only reduces travel costs for riders but also benefits drivers by saving them money in exchange for their additional travel time and costs. However, assigning riders to drivers in an efficient way is a complex problem that requires a focus on maximizing the benefits for both riders and drivers. This study first aims to formulate a multi-driver multirider (MDMR) P2P ride-matching problem based on rational preferences and cost allocation for both driver and rider. This model also enables riders to transfer between multiple drivers to complete their journeys if needed. To solve the ride-matching problem, a Tabu Search (TS) for system optimum ride-matchings and Greedy Matching (GM) algorithm for the stable ridematchings were created to produce stable ride-matchings.
Format:

PDF
Funding:

69A43551747123
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:8fd56e0a9274404a6be8d5a10d83417f3bd15879998df108a22d47df08689c1b
Download URL:

https://rosap.ntl.bts.gov/view/dot/68858/dot_68858_DS1.pdf
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Developing Optimal Peer-to-Peer Ridesharing Strategies

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