Modeling an enhanced ridesharing system with meet points and time windows

Area of Science:

• Operations Research
• Transportation Science
• Urban Planning

Background:

• E-hailing services and ridesharing are increasingly common in urban transportation.
• Traditional ridesharing models face challenges in operational flexibility and efficiency.

Purpose of the Study:

• To develop a mathematical model for an enhanced ridesharing system incorporating meet points and user time windows.
• To improve system performance by reducing total travel time and increasing passenger capacity.

Main Methods:

• Formulation of a mixed integer linear program (MILP) for the enhanced ridesharing problem.
• Implementation of a Tabu-based meta-heuristic algorithm to solve the MILP.
• Validation using CPLEX for optimal solutions in various scenarios.

Main Results:

• The model effectively generates detailed route plans with passenger assignments to meet points, achieving en-route delay savings.
• The meta-heuristic algorithm demonstrates high computational efficiency, yielding solutions close to optimal (8-15% difference).
• Introducing meet points reduces total travel time by 2.7%-3.8% in small-scale systems, with greater benefits expected for larger fleets.

Conclusions:

• The proposed model and algorithm provide an efficient tool for operating ridesharing systems, especially during peak demand.
• Meet points enhance operational flexibility and contribute to traffic congestion mitigation.
• The study highlights the potential of optimized ridesharing for sustainable urban mobility.