SAFETYLIT WEEKLY UPDATE

We compile citations and summaries of about 400 new articles every week.
Email Signup | RSS Feed

HELP: Tutorials | FAQ
CONTACT US: Contact info

Search Results

Journal Article

Citation

Paricio A, Lopez-Carmona MA. Sensors (Basel) 2019; 19(24): s19245342.

Affiliation

Departamento de Automática, Campus Universitario Universidad de Alcala, Ctra. Madrid-Barcelona, km. 33.600, Alcalá de Henares, 28805 Madrid, Spain.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s19245342

PMID

31817144

Abstract

Urban traffic routing is deemed to be a significant challenge in intelligent transportation systems. Existing implementations suffer from several intrinsic issues such as scalability in centralized systems, unnecessary complexity of mechanisms and communication in distributed systems, and lack of privacy. These imply force intensive computational tasks in the traffic control center, continuous communication in real-time with involved stakeholders which require drivers to reveal their location, origin, and destination of their trips. In this paper we present an innovative urban traffic routing framework and reference architecture (multimap traffic control architecture, MuTraff), which is based on the strategical generation and distribution of a set of traffic network maps (traffic weighted multimaps, TWM) to vehicle categories or fleets. Each map in a TWM map set has the same topology but a different distribution of link weights, which are computed by considering policies and constraints that may apply to different vehicle groups. MuTraff delivers a traffic management system (TMS), where a traffic control center generates and distributes maps, while routing computation is performed at the vehicles. We show how this balance between generation, distribution, and routing computation improves scalability, eases communication complexities, and solves former privacy issues. Our study presents case studies in a real city environment for (a) global congestion management using random maps; (b) congestion control on road incidents; and c) emergency fleets routing. We show that MuTraff is a promising foundation framework that is easy to deploy, and is compatible with other existing TMS frameworks.


Language: en

Keywords

TWM; crowdsensing; dynamic traffic assignment; gas emissions; intelligent transportation systems; multi-agent systems; multimap routing; smart cities; traffic big data; traffic management systems; traffic simulation; urban computing; vehicle routing

NEW SEARCH


All SafetyLit records are available for automatic download to Zotero & Mendeley
Print