Abstract

International audience; We consider an urban network with two traffic modes. We control the traffic lights in order to free the roads used by the public transport vehicles. To do this, we solve a flow assignement problem, from which we deduce an ideal distribution of private vehicles. Then using traffic lights, we design a regulator maintaining the car distributions in the roads around the ideal trajectory. This regulator is obtained by solving a linear quadratic problem. The result is a global feedback on the car numbers in the roads. We show the robustness of the obtained control. We compare the obtained results with those given by Bhouri and Lotito (2005).

Abstract

eer-reviewed 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.

Abstract

SUMMARY This article describes a static assignment method for a class of emerging public transport systems called personal rapid transit (PRT). PRT is a fully automated public transportation system where small-size vehicles run on exclusive guideways. Because of its automated, on-demand service, PRT must be able to automatically reroute empty vehicles after use to supply stations with waiting passengers. Consequently, any PRT traffic assignment must take into account the flow of empty vehicles as well as the flow of vehicles with passengers. The PRT assignment methods described in this work are based on linear programming models. One of the assignment methods has been applied to a realistic PRT network, and the statically assigned flows have been compared with averaged link flows produced by a PRT microsimulation. It is shown that the proposed static assignment method is not only useful to identify capacity bottlenecks of the planned PRT network but also serves to benchmark the microsimulator's vehicle management algorithms. Copyright © 2012 John Wiley & Sons, Ltd.