City-Scale System-Optimal Route Planning with Route Replanning

Over the last decade, transportation has been evolving along a technology pathway towards automation and electrification, with the promise of broad positive economic impact. In the short term, increased use of algorithmic routing has instead led to increased road network congestion, which is itself a strong indicator of a negative impact in terms of economics, emissions, and safety. These outcomes are strongly tied to the effects of "user-optimal" route plans competing over the finite road network supply. As demonstrated in our prior work, these concerns can be addressed by computing a system-optimal (SO) batch route plan, which minimizes the system-level average travel time for a given set of traveling agents. However, this leads to two problems of scale. First, as network sizes increase, SO-assigned routes will decay in optimality more readily as network congestion effects vary. Second, as population sizes increase, the problem space quickly becomes intractable for SO route planning. In this paper we extend the original SO route planning problem to include within-route replanning, which addresses the decaying optimality of one-time route plans in large transportation networks. While this allows for a more adaptive SO solution, it greatly increases the number of agents seeking guidance in each batch. To address this, we introduce a sub-batching heuristic based on trajectory clustering to decompose the batch route planning problem into independent sub-batches. Once identified, a filtering technique is applied to re duce the total number of sub-batches. These remaining sub-batches are then each assigned route plans using our original SO technique. This approach can tailor-fit the problem to a set of compute resources, yielding a complete replanning batch pipeline suitable for online execution. An experimental study demonstrates the impact of the proposed technique in three city road networks, improving on the performance of our past work and delivering on the promise of improved scalability. In particular, where travel times were reduced by up to 48.49% with user-optimal replanning, they are further reduced by up to 60.96% using the SO technique.