Online and Proactive Vehicle Rerouting with Uppaal Stratego

Abstract

Modern navigation systems warn the user of traffic jams ahead and suggest alternative routes. However, a lemming effect can cause the alternative routes also to become congested, as the system suggests the same route to all users. As such, in an attempt to optimize for the individual driver, the welfare of the traffic network is punished. In this paper we introduce an online and proactive method for collective rerouting recommendations based on real-time data and stochastic optimization. Our system periodically monitors the status of the network to identify potentially congested roads together with vehicles affected by them. The system then uses Uppaal Stratego to perform machine learning and approximate the best rerouting scenarios. As a proof of concept, we build a SUMO model of a representative traffic network. We perform exhaustive experiments considering different traffic loads and different traffic light controllers. Our results are promising, showing considerable improvement in travel times, queue lengths, and CO2 emissions.

An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints

“…However, this approach is based on static historical traffic data rather than dynamic real-time traffic statistics. The reference [29] attempted to spread out the cars to the way with low traffic ŕow, but did not fully use the capacity of the actual shortest path, which would induce some vehicles to the far path. Reference [30] uses traffic signals to gather real-time traffic data, prevent abrupt traffic congestion, and equally distribute cars along the whole road network.…”