A Dynamic Macroscopic Framework for Pricing of Ride-hailing Services with an Optional Bus Lane Access for Pool Vehicles (2310.01286v1)

Abstract: On-demand trip sharing is an efficient solution to mitigate the negative impact e-hailing has on congestion. It motivates platform operators to reduce their fleet size, and serves the same demand level with a lower effective distance traveled. Users nevertheless prefer to travel solo and for shorter distances despite the fare discount they receive. By offering them the choice to pool and travel in high occupancy dedicated bus lanes, we provide them with a larger incentive to share their rides, yet this creates additional bus delays. In this work, we develop dynamic feedback-based control schemes that adjust the price gap between solo and pool trips to improve multi-modal delays. First, we develop a modal- and space-dependent aggregate model for private vehicles, ride-pooling, and buses, and we use this model to test different control strategies. To minimize the error between the target and actual speeds in the bus network, we design a PI controller and show that by adjusting pool trip fares, we can, with little input data, minimize this error. We also put forward a Model Predictive Control (MPC) framework to minimize the total Passenger Hours Traveled (PHT) and Waiting Times (WT) for the different travelers. Moreover, we show how the MPC framework can be utilized to impose a minimum speed in dedicated bus lanes to ensure that the buses operate on schedule. The results mark the possibility of improving the overall network conditions by incentivizing or discouraging pooling in the vehicle or bus network.