Traffic Oscillation Absorption Strategy of Urban Expressway Based on WT-WOA

Traffic oscillations are primary causes of traffic accidents, delays, and increased energy consumption at bottlenecks of urban road networks. Mitigating oscillations can significantly improve traffic efficiency and safety. A wavelet transform-based time-frequency analysis method is used to accurately capture the cycle of traffic oscillations. Additionally, an adaptive wavelet parameter calibration method is developed based on the whale optimization algorithm (WOA). A fitness function is set up based on the absolute error in identifying the start and end times of traffic shockwaves. In further, a global search mechanism is introduced to overcome the issue of local optima, dynamically optimizing the scale and translation coefficients of the wavelet transform. This approach addresses the common problem of wavelet transforms getting trapped in local optima, and the inaccuracies or misjudgments caused by fluctuations in the discriminative parameters around threshold values in traditional traffic oscillations identification methods. Based on this, a multi-objective collaborative traffic wave absorption control framework integrating energy consumption and driving safety is proposed. A multi-objective optimization function is then investigated incorporating fuel consumption rate and traffic safety indicators. A speed-guided vehicle access control mechanism is designed with dynamic speed regulation implemented upstream of the bottleneck area. By optimizing the speed of specific vehicles, the number of vehicles entering the bottleneck is reduced, accelerating the dissipation of traffic oscillations and suppressing energy loss and safety risks caused by frequent acceleration and deceleration. The results indicate that collision duration and overall collision time decreased by 73.86% and 61.07% respectively, while fuel consumption was reduced by 16.15%, after implementing the traffic wave absorption method in the bottleneck area. The energy consumption and safety risks decrease as the penetration rate increases by analysis of the impact of changes in the penetration rate of connected and autonomous vehicles on the control method. When the penetration rate reaches 0.3 or higher, the control method becomes significantly more effective, with notable reductions in both energy consumption and safety risks.