The invention discloses an urban road travel time prediction method, system, device and medium, and relates to the technical field of traffic prediction, and the method comprises the steps: obtaining online car-hailing GPS track data and corresponding OD data, and obtaining the weather data of a city; quantified weather features are extracted, driver features are extracted according to driver historical GPS trajectory data, and a feature data set is constructed by using the weather features and the driver features; inputting the feature data set into a BP neural network model, training the BP neural network model, and obtaining a trained travel time prediction model; and inputting the features of the test sample into the trained travel time prediction model to output predicted travel time, and evaluating the accuracy and performance of the prediction model. The time-space characteristics of the traffic system are comprehensively considered, modeling of driver and environment characteristics is introduced to predict the travel time or the time for arriving at the destination, and the efficiency and safety of urban road traffic are improved.
本发明公开了一种城市道路行程时间预测方法、系统、设备与介质,涉及交通预测技术领域,包括步骤:获取网约车GPS轨迹数据和对应的OD数据,并获取城市的天气数据;提取出量化的天气特征,根据驾驶员历史GPS轨迹数据提取出驾驶员特征,利用天气特征和驾驶员特征构建特征数据集;利用特征数据集输入BP神经网络模型中,对BP神经网络模型进行训练,获得训练后的行程时间预测模型;将测试样本的特征输入训练完成的行程时间预测模型中输出预测的行程时间,评估预测模型的准确性和性能。本发明综合考虑交通系统的时空特性,引入对驾驶员和环境特征的建模,以预测行程时间或到达目的地的时间,提高城市道路交通的效率和安全性。
Urban road travel time prediction method, system, equipment and medium
一种城市道路行程时间预测方法、系统、设备与介质
2024-09-10
Patent
Electronic Resource
Chinese
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