| description abstract | To alleviate the sharp decline in traffic efficiency caused by the reduced number of lanes or the presence of obstacles and reduce the risk of accidents due to frequent lane changes or acceleration and deceleration in an expressway operation area, taking the Wufengshan north–south 4-lane river crossing channel in Jiangsu Province as the research environment, the related research on the typical operation area of expressway was conducted. A comprehensive indicator system for efficiency, safety, and fuel consumption in an expressway operation area was constructed. Based on Vissim simulation software, considering different traffic volumes, assembly rates, and warning distance conditions, the road scenario and warning model were created. The optimal warning distance for vehicles under different conditions in an expressway operation area was studied using a Bayesian neural network for prediction. The result shows that the average relative errors of this method for predicting the comprehensive safety and energy consumption indicators of an expressway operation area are 0.4%, 0.4% and 0.2% respectively. Through the analysis of the optimal warning distance for comprehensive efficiency, safety, and energy consumption indicators, it was concluded that (1) higher assembly rates lead to better results in all the three indicators, and the optimal warning distance becomes shorter; (2) under free-flow condition, the optimal warning distance is 300–800 m and it increases with the increase of traffic volume; (3) under saturated traffic-flow conditions, the optimal warning distance is 800–1,200 m, but the increase in traffic volume at this time has little influence on the warning distance; and (4) after traffic volume is oversaturated, the optimal warning distance is 1,200–1,800 m, and it continues to increase as the traffic volume increases. The research result can provide some reference for the safe driving of vehicles in an expressway operation area and the issuance of road warning by traffic management departments, and provide a method for predicting the optimal distance of vehicle warning in expressway operation areas. | |
