The invention discloses a traffic demand prediction method, a traffic guidance method and equipment, and relates to the technical field of intelligent traffic. Traffic zones are divided for a research area, and mobile phone signaling data in the research area within a preset historical time range are acquired; oD data and road signaling flow data between traffic cells of a preset time aggregation window are calculated according to the mobile phone signaling data; obtaining road network data of a research area, and carrying out spatial association on OD data, road signaling flow data and road network data between traffic cells; key traffic nodes and flow bottlenecks are identified, and the corrected road section flow of each road on the road network is generated in combination with a space correlation result; training an LSTM model according to the corrected road section flow data; and using the trained LSTM model to predict traffic demand data of each road in the whole road network in a future time period. According to the method, the whole-day time is sliced, and the spatial granularity is improved to the road level by using the GIS technology, so that the high temporal-spatial resolution of traffic flow prediction is realized.
本发明公开一种交通需求预测方法、交通引导方法和设备,涉及智慧交通技术领域,对研究区域划分交通小区,获取预设历史时间范围的研究区域内的手机信令数据,根据手机信令数据计算预设时间聚合窗口的交通小区之间的OD数据和道路信令流量数据;获取研究区域的路网数据,将交通小区之间的OD数据、道路信令流量数据和路网数据进行空间关联;识别关键交通节点和流量瓶颈,结合空间关联结果生成路网上每一条道路校正后的路段流量;根据校正后的路段流量数据训练LSTM模型;采用训练好的LSTM模型预测未来时间段内全路网每一条道路交通需求数据。发明通过将全天时间切片,并利用GIS技术提高空间粒度至道路级别,实现了交通流量预测的高时空分辨率。
Traffic demand prediction method, traffic guidance method and equipment
一种交通需求预测方法、交通引导方法和设备
2025-03-11
Patent
Electronic Resource
Chinese
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