Abstract

Shortest path determination in a dynamic transportation network has been a real challenge where network traffic scenario changes continuously. The shortest path between any two nodes in the network depends not only on the travel time of links that constitutes the path but also on the time spent at the intersection which is a major part of the trip time. Existing algorithms found in the literature consider only the link travel time for the shortest path calculation between two nodes. A new algorithm is introduced in this study that calculates the dynamic minimum trip time path between two nodes by considering both link travel time and intersection turning delay. A macroscopic traffic simulator is developed to simulate vehicles that use the proposed algorithm to calculate the minimum trip time path. The trip time of guided vehicles which determine the travel path based on en-route traffic information is compared to the trip time of unguided vehicles which determine the travel path based on pre-trip traffic information. The guided vehicles are assumed to be a part of a decentralized traffic management system where onboard route guidance system allows the driver to calculate the minimum trip time path each time they cross an intersection. A part of Montréal city road network is also simulated to show the application of proposed path finding algorithm. From the results it is seen that for a long distance trip the amount of trip time savings is higher because the guided vehicles usually choose the path with less number of left turning delays.