时空依赖的城市道路旅行时间预测

城市道路的旅行时间预测，对于路径规划以及交通管理至关重要.尽管旅行时间预测会受路段依赖、时空相关性以及其他因素的影响，但现有的方法并未考虑如何结合外部因素进行建模，因而可能会有引入错误信息、路段建模时忽略上下游路段间的依赖关系等问题，导致预测精度较差.鉴于此，提出了两阶段的旅行时间预测框架：首先，使用Skip-Gram模型对轨迹数据地图匹配后的路段序列进行编码，将其映射为低维向量，通过该编码方式避免引入错误信息的同时保留了路段间的上下游依赖信息.随后，基于路段编码模式整合天气、日期等外部因素，设计了基于深度神经网络的城市道路旅行时间预测模型.基于真实出租车轨迹数据集的对比实验结果表明，所提方法比对比算法具有更高的预测精度.

Travel Time Prediction for Urban Road Based on Spatial-temporal Dependency

Travel time prediction is critical for route planning and traffic monitoring. Due to complex relationships among road segments, spatial-temporal dependency, and other factors, it is challenging to perform modeling upon trajectory dataset. Without incorporating external factors into modeling, existing methods may import incorrect information and ignore road segment dependence, which results in poor prediction accuracy. A two-phase travel time prediction framework is proposed to solve the mentioned issues. During the first stage, trajectory data are mapped to a sequence of segments to generate a low-dimensional vector, which avoids introducing incorrect information while preserving the road segment dependence. During the second phase, after integrating road segment encoding and external factors such as weather and date, a travel time prediction model based on deep neural network is designed. The detailed experimental results on a real-world taxi trajectory dataset show that the proposed method is more accurate than existing methods.