Next Road Rerouting: A Multiagent System for Mitigating Unexpected Urban Traffic Congestion

Abstract

Abstract-During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

Towards a distributed and infrastructure-less vehicular traffic management system

“…Another example of a centralized vehicle rerouting system is the Next Road Rerouting (NRR) [5]. The NRR's goal is to assist drivers in making the most suitable next road choice by focusing on a higher travel time reliability in the face of congestions.…”

“…This can be done without the need to invest in new road infrastructure. Due to the limitations mentioned in the first solution, significant research efforts have been directed toward the second one [3,4,5,6,7]. One of the promising technologies for an efficient traffic management system is the Vehicular ad hoc Networks (VANETs).…”

“…Using the beaconing approach, a real-time traffic management system can collect raw data and extract useful information (or knowledge) about the traffic congestion in a region of interest. Several systems were designed and implemented to recommend an alternative route, in real-time, when an en-route event is detected [4,5,6,7].…”

A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters

“…Traffic state information, such as the density, speed on road segments and the queue size in front of an intersection, is the basis of various road traffic management and control strategies. They range from traffic light control [1], ramp metering [2] to link control [3], and route guidance [4]. Estimation of the traffic state is necessary due to the limited coverage of sensors and to the noisy measurements that the sensors produce [5].…”

Aplicando Redes Sociais Veiculares para Aprimorar o Gerenciamento da Mobilidade Urbana

“…Além disso, muitas aplicações de ATMS necessitam que os veículos periodicamente compartilhem os seus dados (floating car data) entre os veículos vizinhos ou com o servidor central e roadside unit (RSU). Através dessa operaçãoé possível criar o conhecimento sobre as condições de tráfego veicular [Wang et al 2016, Doolan and Muntean 2017, Pan et al 2017. Essa práticaé conhecida como beaconing e os dados trocados estão relacionados com a mobilidade do veículo tais como identificação do veículo, posição atual do veículo, velocidade, direção de deslocamento, entre outros.…”