| Citation: | LUO Shulin, ZHANG Cunbao, ZHANG Taiwen, CAO Yu. Active Traffic Guidance Method for Recurrent Congestion Points[J]. Journal of Transport Information and Safety, 2021, 39(5): 68-72. doi: 10.3963/j.jssn.1674-4861.2021.05.009
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Traffic guidance methods based on dynamic user equilibrium and optimal system allocation focus on macro forecasting and adjustment of road network demands. They are difficult to accurately identify related traffic flow, which restricts the guidance effects. An active traffic guidance method based on traceable demand is proposed to control traffic flows and alleviate recurrent congestion. The study following the idea of targeted guidance, analyzes the correlation between vehicle trajectory and traffic flow at frequent congestion points, and uses Kalman filter to make short-term predictions of the associated traffic flow. Furthermore, it is preferable to optimize target traffic flows in combination with indicators such as traffic ratio and path saturation. Meanwhile, based on balanced traffic distribution, the spatio-temporal correlation between the road section and the path traffic flow is used to update the road network traffic state and establish an active induction optimization model with saturation equilibrium as the goal. The simulation results show that, compared with the active induction based on path preference, this method result in a reduction from 30% to 60% in the average delay and the number of stops of vehicles at frequent congestion points, and a reduction from 10% to 15% in the whole road network. The convergence speed and traffic benefit of the model are significantly improved, which verifies the effectiveness of the method in the work.
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