The invention relates to a parking space searching behavior simulation method and system based on expert decision, and the method comprises the following steps: S1, simulating the traffic state of a real environment through a traffic simulation platform, and collecting an effective parking space searching track; s2, performing clustering analysis on the trajectory data based on the selection preference of the driver to form an expert trajectory data set; s3, training the inverse reinforcement learning model by using the expert track data set to obtain a feedback function set; s4, utilizing the feedback function set to establish a reinforcement learning model, obtaining a decision strategy through reinforcement learning, and taking the decision strategy as a path-finding driving function to establish a parking position-finding model; and S5, using a traffic simulation platform and a parking space searching model to realize parking space searching behavior simulation in a simulation environment, and systematically analyzing the collected data to obtain a statistical analysis result of the parking system. Compared with the prior art, the method can provide theoretical basis and technical support for urban parking management optimization and parking parallel simulation in a big data environment.
本发明涉及一种基于专家决策的停车寻位行为仿真方法与系统,包括以下步骤:S1、通过交通仿真平台模拟现实环境的交通状态,并收集有效的停车寻位轨迹;S2、基于驾驶人选择偏好对轨迹数据进行聚类分析,形成专家轨迹数据集;S3、使用专家轨迹数据集训练逆强化学习模型,得到反馈函数集;S4、利用反馈函数集建立强化学习模型,通过强化学习得到决策策略,以决策策略作为寻路驱动函数建立停车寻位模型;S5、使用交通仿真平台及停车寻位模型,实现在仿真环境中的停车寻位行为模拟,系统分析所采集的数据以得出停车系统的统计性分析结果。与现有技术相比,本发明能为大数据环境下的城市停车管理优化与停车平行仿真提供理论基础与技术支撑。
Parking position searching behavior simulation method based on expert decision
一种基于专家决策的停车寻位行为仿真方法
2023-03-31
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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