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.

A Dynamic Tree Algorithm for Peer-to-Peer Ridesharing Matching

“…Recent studies extended the matching problem and corresponding solution algorithms. For example, investigating the stability of the matching between passengers and drivers (Wang et al, 2018), improving the traffic efficiency with advanced travel time feedback (Wu et al, 2019), and optimizing the many-to-many matching time interval and matching radius (Yang et al, 2020). Studies concerning the effects of transportation services on emergency events, such as epidemic disease spreading (Chen et al, 2020a(Chen et al, , 2020b were also recently investigated in the literature.…”

Network Congestion Diffusion Model Considering Congestion Distribution Information

“…Existing network congestion diffusion models considering the influence of congestion distribution information mainly focus on road networks. For example, based on cellular automaton models, network congestion diffusion considering city central business congestion district distribution has been simulated by giving an additional travel time weight to describe the congestion district [27]. However the congestion distribution influence expressed by a unified and fixed weight is not suitable for distribution of dynamical varying congestion.…”