服务区人车流预测模型

服务区人车流预测模型通过整合高速公路主线流量、服务区车位、充电桩状态、加油站负荷及餐饮商超等多维数据，采用机器学习算法与多模态时空预测引擎，精准预测未来2小时的车流高峰时段与规模。该模型不仅提供应对突发事件的智能调度方案，还支持节假日流量预警及新能源车充电服务优化。其核心能力包括车流预测、资源调度、路径优化和用户服务推荐，旨在提升服务区运营效率，优化司乘人员体验。

The pedestrian and vehicle flow prediction model for highway service areas integrates multi-dimensional data sources such as mainline traffic volumes of expressways, on-site parking spaces, EV charging pile status, gas station operational loads, and catering and retail service conditions. Equipped with machine learning algorithms and a multimodal spatiotemporal prediction engine, it accurately predicts the peak timing and scale of vehicle flows over the subsequent two hours. It not only provides intelligent scheduling schemes for emergency response scenarios, but also supports holiday traffic flow early warning and new energy vehicle charging service optimization. Its core capabilities include traffic flow prediction, resource scheduling, route optimization and user service recommendation, aiming to improve the operational efficiency of highway service areas and enhance the travel experience of drivers and passengers.