Collective dynamics of capacity-constrained ride-pooling fleets

Area of Science:

• Operations Research
• Transportation Science
• Complex Systems

Background:

• Ride-pooling services enhance urban mobility by combining passenger trips.
• Fleet dynamics are crucial for ride-pooling efficiency, with existing models often assuming unlimited capacity.
• The impact of vehicle capacity constraints on these efficiency scaling laws remains poorly understood.

Purpose of the Study:

• To investigate how vehicle capacity constraints influence the collective dynamics and efficiency of ride-pooling fleets.
• To develop a generalized model for ride-pooling efficiency that accounts for fleet capacity limitations.
• To provide a framework for predicting required fleet sizes in diverse operational settings.

Main Methods:

• Mapping the dynamics of capacity-constrained ride-pooling fleets to an equivalent unlimited-capacity system.
• Identifying an 'effective fleet size' as the key scaling parameter for constrained dynamics.
• Utilizing queueing theory to analyze dynamics in a minimal model and approximate scaling functions.

Main Results:

• An effective fleet size parameter was identified, unifying the analysis of constrained and unconstrained ride-pooling dynamics.
• Generalized scaling laws for ride-pooling efficiency were derived, applicable to capacity-constrained fleets.
• Queueing theoretical analysis provided a method for mean-field predictions of fleet size requirements.

Conclusions:

• The study successfully generalizes ride-pooling efficiency scaling laws to account for vehicle capacity constraints.
• The concept of effective fleet size offers a powerful tool for analyzing and optimizing ride-pooling operations.
• Findings facilitate the transfer of operational insights across different ride-pooling services and geographical locations.