基於強化學習之高速公路路肩流量管制策略

Abstract

為解決在公速公路上的交通壅塞情況，透過行車速度、通行車流量以及紅綠燈等都是現行的方式以控制交通。在壅塞情形發生時，透過外力的介入，來想辦法控制整體狀況，不要讓交通壅塞更加惡化。所幸在現代車聯網愈趨開發穩定的情況，透過(Vehicle to Vehicle, V2V)或是(Vehicle to Infrastructure, V2I)等方式，能夠更快速的將交通舒緩策略傳遞給所有在此範圍運行中的車輛，並讓他們及時地做出反應來幫助整體交通的舒緩。 在本篇研究中提出基於強化學習的路肩通行車流量管制策略(Reinforcement Learning Approach for Adaptive Road Shoulder Traffic Control, ARSTC)。不同於傳統固定路肩開放時間的方式，本研究提出適用且合乎現行高公局法規之下的路肩管制策略，藉由結合強化學習(Reinforcement Learning)的技術，使其能夠對應不同車流的情況，推薦不同的管制策略。透過在模擬環境的實驗結果 (Simulation of Urban Mobility, SUMO)，ARSTC能夠依照整體的車流變化來判斷是否開放路肩通行，讓路肩通行的車流量能夠控制在安全的範圍內，且能夠最小化與原本無管制車流的壅塞時間差異，來達到最安全且有效率的路肩通行環境。To reduce traffic congestion on the highway, variable speed limit, flow control, and traffic light are used in the current traffic control system. Through those approaches, the traffic can maintain in an acceptable condition when congestion occurred. With the development of the vehicular networks, i.e., Vehicle-to-Vehicle(V2V) and Vehicle-to-Infrastructure (V2I) techniques, drivers are able to receive updated traffic information which allows them to change their route plan immediately. In this research, we proposed a Reinforcement Learning Approach for Adaptive Road Shoulder Traffic Control (ARSTC) to dynamically change the opening and closing time of hard shoulder. Using the reinforcement learning approach, the proposed ARSTC technique, is able to adjust to different traffic situations and make a suitable decision which is different from the traditional static scheduling approach for the hard shoulder. The proposed technique is simulated in the Simulation of Urban Mobility (SUMO). The performance results showed that ARSTC can reduce traffic congestion time by adaptively control the hard shoulders’ opening time and the traffic flow within the safety range follow by the policy of the Freeway Bureau. Our proposed technique (ARSTC) is able to provide a safer and more efficient driving condition while using the hard shoulder to ease traffic congestion.