Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand
modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers
have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand,
this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp
functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential
of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not
require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology
is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations
with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show
that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to
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ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average
distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17.