Stochastic motion planning and applications to traffic

This paper presents a stochastic motion planning algorithm and its application to traffic navigation. The algorithm copes with the uncertainty of road traffic conditions by stochastic modeling of travel delay on road networks. The algorithm determines paths between two points that optimize a cost function of the delay probability distribution. It can be used to find paths that maximize the probability of reaching a destination within a particular travel deadline. For such problems, standard shortest-path algorithms don’t work because the optimal substructure property doesn’t hold. We evaluate our algorithm using both simulations and real-world drives, using delay data gathered from a set of taxis equipped with GPS sensors and a wireless network. Our algorithm can be integrated into on-board navigation systems as well as route-finding Web sites, providing drivers with good paths that meet their desired goals.National Science Foundation (U.S.) (grant EFRI-0710252)National Science Foundation (U.S.) (grant IIS-0426838

Finding least fuel emission paths in a network with time-varying speeds

This article considers the problem of finding a route and schedule for a vehicle starting from a depot, visiting a set of customers, and returning to the depot, in a time-dependent network where the objective is to minimize the greenhouse gas emissions. In this formulation, the speeds of the vehicle as well as the routes chosen are decision variables subject to limits determined by the level of congestion on the roads at the time. Two methods are proposed to find the optimal strategy for a single route. One is a time-increment-based dynamic programming method, and the other is a new heuristic approach. In addition, a case study is carried out, which compares the performances of these methods, as well as the least polluting routes with the shortest time routes between two customer nodes

Effects of countdown displays in public transport route choice under severe overcrowding

The paper presents a route choice model for dynamic assignment in congested, i.e. overcrowded, transit networks where it is assumed that passengers are supported with real-time information on carrier arrivals at stops. If the stop layout is such that passenger congestion results in First-In-First-Out (FIFO) queues, a new formulation is devised for calculating waiting times, total travel times and route splits. Numerical results for a simple example network show the effect of information on route choice when heavy congestion is observed. While the provision of information does not lead to a remarkable decrease in total travel time, with the exception of some particular instances, it changes the travel behaviour of passengers that seem to be more averse to queuing at later stages of their journey and, thus, prefer to interchange at less congested stations

An approach to time dependence and reliability in dynamic route guidance

This paper presents a methodology, in order to increase the reliability of the route suggestions in route guidance systems. Based on the A* path finding algorithm and Chen’s link penalty method, the procedure involves penalising links with a high risk of being congested and obtaining a set of reliable route suggestions. Time-dependence of travel times is considered by adapting the Flow Speed Model technique accordingly. Modifications to the structure of the path finding algorithms are also made, so as to account for real road network features. Finally, experiments using simulated travel time and reliability data are carried out on a road network and the results are discussed