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ROThAr: Real-time On-line Traffic Assignment with Load Estimation
Date Issued
2013-11-01
Date Available
2015-10-05T08:27:11Z
Abstract
More and more drivers use on-board units to help them navigate in the increasing urbanised environment they live and work in. These system (e.g., routing applications on smart phones) are now very often on-line, and use information from the traffic situation (e.g., accidents, congestion) to get the best route. We can now envisage a world where all trips are assigned and updated by such an on-line system, making the best routing decisions based on traffic conditions. The problem is that current systems consider only 'local' elements (e.g., driver preference and current traffic condition) and do not make routing decisions from a global perspective. This can lead to a lot of similar routing assignments that could lead to further traffic congestion. The objective of the next generation on-line navigation systems is then to come up with a 'smart', real-time route assignment, which balances the load between the different road segments and offers the best quality to the drivers. However, every routing decision made has an impact on the traffic conditions (one more vehicle on the road segments selected) and computing the load induced by the trips is a computationally heavy problem. This paper addresses this question of real-time on-line traffic assignment, and shows that under certain conditions it is possible to have (i) an accurate estimation of the load and travel time on every road segment and (ii) an optimised traffic assignment that adapts to divergence and evolutions (e.g., accidents) of the system.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Lero
Type of Material
Conference Publication
Publisher
Institute of Electrical and Electronic Engineers (IEEE)
Copyright (Published Version)
2013 IEEE
Language
English
Status of Item
Peer reviewed
Conference Details
17th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT), Delft, Netherlands, 30 October - 1 November, 2013
This item is made available under a Creative Commons License
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