Congestion-Aware Ride-Sharing

In its current form, ride-sharing is responsible for a small fraction of traffic load compared to other transportation modes, especially private vehicles. As its benefits became more evident, and obstacles, e.g., lack of liability legislation, that may hinder its larger scale adoption are being overcome, ride-sharing will be a more common mode of transportation. In particular, autonomous vehicles (AVs) are showing their proficiency on the roads, which may also catalyze ride-sharing ubiquity. For example, while an AV owner is at work, he may find it appealing to offer his AV as a service or rent it to Uber so that the vehicle serves others' transportation requests. Furthermore, this disruptive technology is backed up by companies like Google (Waymo), Tesla, and Uber. Therefore, ride-sharing will soon become a source of traffic congestion itself. In this article, we present an efficient congestion-aware ride-sharing algorithm which, instead of finding optimal travel plans based on traffic load generated by other means of transportation, it computes optimal travel plans for thousands of ride-sharing requests within a time interval. Note that in this problem, an optimal travel plan for a group of requests may affect an already computed travel plan for another concurrent group of requests, therefore plans cannot be isolated from each other.