Highlights Propose a state transfer risk decision (STRD) method for a large-scale traffic network. This method is based on the conditional risk decision model. This method provides an acceptable traffic flow range at the sub-region boundary by risk interval. Proposed method improves the state stability compared to PI and NSTRD.
Abstract Network state degradation probabilities increase with traffic density. Specifically, at critical or congested densities, any over-input traffic flow may cause degradation, local traffic jams, and even hysteresis of the road network. Existing macroscopic traffic control methods usually target the maximum capacity or minimum delay to improve the road network efficiency and do not consider the negative effect of perimeter control on the state transfer of the road network. This study provides a method to prevent the transfer of the network state when implementing boundary control. First, according to the data detected for the road network, the concept of a conditional value at risk is used to establish a risk decision model that considers the influence of the boundary input flow rate on sub-region state degradation. Then, based on the risk decision model, we propose a state transfer risk decision (STRD) perimeter control method for a large-scale traffic network with multiple sub-regions. This perimeter control method predicts the traffic demand of every sub-region, selects an acceptable traffic flow range at the sub-region boundary by risk interval, regards the maximum trip completion flow and the minimum total travel time as the decision-making objectives, and controls multiple sub-region boundaries. The simulation results show that compared with proportional integral (PI) control and no STRD (NSTRD) control, the STRD control scheme can effectively prevent the state transfer of all sub-regions, improve the trip completion flow, and decrease the travel delay of the network.
Perimeter control for congested areas of a large-scale traffic network: A method against state degradation risk
Tramsportation Research, Part C: Emerging Technologies ; 112 ; 28-45
2020-01-18
18 pages
Article (Journal)
Electronic Resource
English