Toward a Dynamic Reversible Lane Management Strategy by Empowering Learning-Based Predictive Assignment Scheme

Traffic congestion is a long-lasting worldwide problem and even becoming more severe in well-developed regions. Reversible lanes have been used worldwide on various road types to mitigate the effects of congestion and optimize mobility since the 1930s. However, with the limitation of traditional control and management methods, existing solutions can not meet the increasing travel demands. Therefore, the paper introduces a Predictive Empowered Assignment scheme for Reversible Lane (PEARL). By integrating the advanced traffic flow prediction module and bi-level optimization model, PEARL can be a more flexible dynamic lane assignment strategy with foresight compared to traditional lane management methods. In the prediction module, taking advantage of the development of machine learning technologies, the input of PEARL covers not only historical sequence and real-time data but also the environmental conditions in the region. The advanced Bi-directional Long Short-term Memory (Abi-LSTM) model is employed for short-term traffic flow prediction. Then, the study introduces a bi-level optimization method to maximize the total throughput in both directions and minimize the total user costs which determine the lane deployment. The iterations on the prediction module and optimization module can help PEARL coordinate the future lane control plan and make the best decision. Finally, the paper builds up an experiment platform to simulate PEARL in a real-world scenario with heavy input flow for its performance evaluation.