The prevalence of on-street parking search in urban downtown areas has led to significant externalities such as congestion,pollution,and collisions.Understanding the intricacies of parking search behavior is crucial f...The prevalence of on-street parking search in urban downtown areas has led to significant externalities such as congestion,pollution,and collisions.Understanding the intricacies of parking search behavior is crucial for developing effective management strategies to mit-igate these issues.Parking search is inherently a complex,sequential decision-making pro-cess,influenced by diverse driver preferences and dynamic urban environments.This study introduces a deep inverse reinforcement learning(DIRL)approach to model drivers’park-ing search behavior.First,we constructed a high-fidelity parking simulation platform using Unity3D to replicate an urban road network,enabling the collection of 987 valid trajecto-ries.We modeled the parking search process as a Markov decision process(MDP),with meticulously designed state-action pairs for accurate representation.Then,a maximum entropy-based DIRL model was developed to learn the reward function and search-for-parking policies of drivers.The experimental results demonstrate that the maximum entropy DIRL model significantly outperforms the traditional maximum entropy inverse reinforcement learning model,achieving a 19.0%improvement in accurately capturing final parking states and a 13.5%enhancement in characterizing overall trajectory distribu-tions.Finally,we integrated these trained models into traditional traffic simulation sys-tems to effectively observe the traffic state evolution with different parking search behaviors,providing valuable insights for optimizing urban traffic management strategies.展开更多
基金supported by the National Natural Science Foundation of China(No.52102383)in part by the China Post-doctoral Science Foundation(Nos.2021M692428 and 2023T160487)part by the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(No.YESS20220215).
文摘The prevalence of on-street parking search in urban downtown areas has led to significant externalities such as congestion,pollution,and collisions.Understanding the intricacies of parking search behavior is crucial for developing effective management strategies to mit-igate these issues.Parking search is inherently a complex,sequential decision-making pro-cess,influenced by diverse driver preferences and dynamic urban environments.This study introduces a deep inverse reinforcement learning(DIRL)approach to model drivers’park-ing search behavior.First,we constructed a high-fidelity parking simulation platform using Unity3D to replicate an urban road network,enabling the collection of 987 valid trajecto-ries.We modeled the parking search process as a Markov decision process(MDP),with meticulously designed state-action pairs for accurate representation.Then,a maximum entropy-based DIRL model was developed to learn the reward function and search-for-parking policies of drivers.The experimental results demonstrate that the maximum entropy DIRL model significantly outperforms the traditional maximum entropy inverse reinforcement learning model,achieving a 19.0%improvement in accurately capturing final parking states and a 13.5%enhancement in characterizing overall trajectory distribu-tions.Finally,we integrated these trained models into traditional traffic simulation sys-tems to effectively observe the traffic state evolution with different parking search behaviors,providing valuable insights for optimizing urban traffic management strategies.
