Optimal curbside pricing for managing ride-hailing pick-ups and drop-offs

Recent years have witnessed the rise of ride-hailing mobility services thanks to ubiquitous emerging technologies. Curbside spaces, as a category of public infrastructure, are being used by private ride-hailing services to pick up and drop off passengers, in addition to deliveries and parking access. This becomes quite common in urban areas and has led to additional congestion for ride-hailing, private and public transit vehicles on the driving lanes. Curb utilization by various traffic modes further alters travelers’ choices in modes/routes, clogging streets and polluting urban environment. However, there is a lack of theories and models to evaluate the effects of curbside ride-hailing stops in regional networks and to effectively manage ride-hailing pick-ups and drop-offs for system optimum. In view of this, this paper develops a bi-modal network traffic assignment model considering both private driving and ride-hailing modes who are competing for roads and curb spaces in general networks. To model the impact of limited curbside capacity to through traffic, a curbside queuing model is utilized to quantify the effect of congestion on both curbs and driving lanes induced by curbside stops in terms of waiting time and queue lengths. Travelers make joint choices of modes (driving or ride-hailing), curb stopping locations or parking locations. In addition, this study explores the option to regulate the amount of curbside stops to improve system performance, which is done by imposing a location-specific stopping fee on ride-hailing trips for using curbs to pick-up and drop-off. The curb pricing would influence travelers’ modal choices and parking location choices. To determine the optimal curbside pricing, a sensitivity analysis-based method is developed to minimize the total social cost of the network among all trips. The proposed methods are examined on three networks. We find that the optimal curbside pricing could effectively reduce curbside congestion and total social cost of the traffic system, benefiting all trips in the network.