User preference-based intelligent road route recommendation using SARSA and dynamic programming  

作　　者：Roopa Ravish Shanta Rangaswamy Arpitha V Vasuprada U 

机构地区：[1]PES University,Bengaluru,India [2]Rashtreeya Vidyalaya College of Engineering,Bengaluru,India

出　　处：《Journal of Control and Decision》2023年第3期443-453,共11页控制与决策学报（英文）

摘　　要：Traffic congestion is one of the main challenges in transportation engineering. It directly impactsthe economy by increasing travel time and affecting the environment by excessive fuel consumptionand emission. Road route recommendation to overcome the congestion by alternativeroute suggestions has gained high importance. The existing route recommendation systems areproposed using the reinforcement learning algorithm (Q-learning). The techniques suggestedin this paper are state-action-reward-state-action (SARSA) algorithm and dynamic programming(DP) to guide the commuters to reach the destination with an optimal solution. The algorithmconsiders travel time, cost, flexibility, and traffic intensity as the user preference attributes torecommend an optimal route. The recommended system is implemented by building a roadnetwork graph. We assign values to each user preference attribute along the edges, which cantake high(1) or low(0) values. By considering these values, the system recommends the route.The proposed system performance is evaluated based on computation time, cumulative reward,and accuracy. The results show that DP outperforms the SARSA algorithm.

关 键 词：Intelligent transport system machine learning techniques in ITS SARSA algorithm dynamic programming route guidance system travel time prediction traveller information system 

分 类 号：U495[交通运输工程—交通运输规划与管理]