Improve traffic efficiency with advanced travel time feedback in urban networks

Abstract

With the development of modern intelligent traffic system technology, the travel time information can be collected and processed to provide route-choice suggestions to travellers. However, due to the complex nature of a traffic system, the feedback of traffic information might lead to undesired congestion in some concerned areas (such as the central business district). In this paper, an improved time shortest path strategy (ITSP) based on advanced travel time information feedback is proposed and applied in a Manhattan-like urban traffic system. With the strategy, the link travel time in the concerned area is adjusted by a travel-cost-related coefficient before being sent to final users. We study the effects of ITSP on traffic performance based on cellular automaton model of urban traffic. What we found most interesting is that when providing the traffic time information with an slightly larger than 1.0 (typically ), the performance of the system will be enhanced as compared to the situations of no information feedback or providing the real travel time information. Simulation results show that, ITSP can increase the average arrival rate and the traffic flow in scenario of fixed total number of vehicles. Vehicle density decreases with ITSP strategy under scenario with varying total number of vehicles, which helps to avoid traffic gridlock and improve the system reliability. Furthermore, the effects of different size of core areas and different origin-destination patterns are also explored. All the results show that ITSP can improve the traffic performance of network systems.