Treffer: Modeling Dynamic Vehicle Navigation in a Self-Organizing, Peer-to-Peer, Distributed Traffic Information System.

Title:
Modeling Dynamic Vehicle Navigation in a Self-Organizing, Peer-to-Peer, Distributed Traffic Information System.
Source:
Journal of Intelligent Transportation Systems; Oct-Dec2006, Vol. 10 Issue 4, p185-204, 20p, 4 Diagrams, 3 Charts, 10 Graphs
Database:
Complementary Index

Weitere Informationen

This article presents a simulation-based framework to model the potential benefits from dynamic vehicle on-line routing in a distributed traffic information system based upon a vehicle-to-vehicle information-sharing architecture. Within this framework, certain vehicles with specific inter-vehicle communication equipment in the traffic network are capable of autonomous traffic surveillance and peer-to-peer information sharing. Based on real-time and historical traffic information, they independently optimize their routes, forming a self-organizing traffic information overlay to the existing vehicular roadway network. In-trip rerouting decisions arising from drivers' interactions with this distributed information system are modeled according both to a rational-boundary model and to a binary-logit model under the assumption that each driver is a rational entity. A path-based microscopic traffic simulation model is developed to study on-line vehicle navigation in the distributed traffic information system, testing nonrecurrent congestion cases on two different networks representing typical roadway scenarios in daily commuting. Based on simulation study results, potential benefits both for travelers with access to the traffic information system as well as for the traffic system as a whole are demonstrated. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Transportation Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)