Transfer Learning-Based Deep Reinforcement Learning Approach for Robust Route Guidance in Mixed Traffic Environment
Donghoun Lee
Abstract
A previously developed Deep Reinforcement Learning-based Vehicle Routing (DRL-VR) algorithm aims to be used for providing the shortest Origin-Destination (OD) travel time path in dynamic traffic environment. However, several issues may still arise regarding uncertainty associated with mixed traffic conditions coexisting Automated Vehicles (AV) and Human-driven Vehicles (HV), particularly in a wide-area urban road network. To develop a robust and interoperable route guidance algorithm based on the DRL approach, this study proposes Transfer learning-based deep reinforcement Learning Algorithm for Route Guidance (TLARG). It is an extended framework on the previous approach by incorporating transfer learning scheme that enables the DRL model of TLARG to converge even in a wide-area urban road network. The TLARG is evaluated in terms of OD travel time based on diverse OD trips with different urban road networks, including narrow- and wide-area road networks. This research conducts several evaluation studies based on microscopic traffic simulation experiments. The simulation result shows that the TLARG enables the agent to complete its OD trips not only with flexible routes but also with reductions in travel time depending on given traffic situations irrespective of network type. Furthermore, it demonstrates that the robustness of the proposed approach by measuring the error of Estimated Time of Arrival (ETA) for various OD trips in different urban road networks under the mixed traffic conditions. Such findings suggest that the TLARG has great potential to enhance the punctuality of mobility service by providing robust route guidance, even in the era of coexisting AVs and HVs.