Stable Infrastructure-based Routing for Intelligent Transportation Systems

Intelligent Transportation Systems (ITSs) have been instrumental in reshaping transportation towards safer roads, seamless logistics, and digital business-oriented services under the umbrella of smart city platforms. Undoubtedly, ITS applications will demand stable routing protocols that not only focus on Inter-Vehicle Communications but also on providing a fast, reliable and secure interface to the infrastructure. In this paper, we propose a novel stable infrastructure- based routing protocol for urban VANETs. It enables vehicles proactively to maintain fresh routes towards Road-Side Units (RSUs) while reactively discovering routes to nearby vehicles. It builds routes from highly stable connected intersections using a selection policy which uses a new intersection stability metric. Simulation experiments performed with accurate mobility and propagation models have confirmed the efficiency of the new protocol and its adaptability to continuously changing network status in the urban environment

CBPRS: A City Based Parking and Routing System

Navigational systems assist drivers in finding a route between two locations that is time optimal in theory but seldom in practice due to delaying circumstances the system is unaware of, such as traffic jams. Upon arrival at the destination the service of the system ends and the driver is forced to locate a parking place without further assistance. We propose a City Based Parking Routing System (CBPRS) that monitors and reserves parking places for CBPRS participants within a city. The CBPRS guides vehicles using an ant based distributed hierarchical routing algorithm to their reserved parking place. Through means of experiments in a simulation environment we found that reductions of travel times for participants were significant in comparison to a situation where vehicles relied on static routing information generated by the well known Dijkstra’s algorithm. Furthermore, we found that the CBPRS was able to increase city wide traffic flows and decrease the number and duration of traffic jams throughout the city once the number of participants increased.information systems;computer simulation;dynamic routing

EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System

Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas. Existing methods for EMV dispatch typically optimize routes based on historical traffic-flow data and design traffic signal pre-emption accordingly; however, we still lack a systematic methodology to address the coupling between EMV routing and traffic signal control. In this paper, we propose EMVLight, a decentralized reinforcement learning (RL) framework for joint dynamic EMV routing and traffic signal pre-emption. We adopt the multi-agent advantage actor-critic method with policy sharing and spatial discounted factor. This framework addresses the coupling between EMV navigation and traffic signal control via an innovative design of multi-class RL agents and a novel pressure-based reward function. The proposed methodology enables EMVLight to learn network-level cooperative traffic signal phasing strategies that not only reduce EMV travel time but also shortens the travel time of non-EMVs. Simulation-based experiments indicate that EMVLight enables up to a $42.6\%$ reduction in EMV travel time as well as an $23.5\%$ shorter average travel time compared with existing approaches.Comment: 19 figures, 10 tables. Manuscript extended on previous work arXiv:2109.05429, arXiv:2111.0027