基于时空相关属性模型的公交到站时间预测算法

公交车辆到站时间的预测是公交调度辅助决策系统的重要依据,可帮助调度员及时发现晚点车辆,并做出合理的调度决策.然而,公交到站时间受交通拥堵、天气、站点停留和站间行驶时长不固定等因素的影响,是一个时空依赖环境下的预测问题,颇具挑战性.提出一种基于深度神经网络的公交到站时间预测算法STPM,算法采用时空组件、属性组件和融合组件预测公交车辆从起点站到终点站的总时长.其中,利用时空组件学习事物的时间依赖性与空间相关性.利用属性组件学习事物外部因素的影响.利用融合组件融合时空组件与属性组件的输出,预测最终结果.实验结果表明,STPM能够很好地结合卷积神经网络与循环神经网络模型的优势,学习关键的时间特征与空间特征,在公交到站时间预测的误差百分比和准确率上的表现均优于已有的预测方法.

Bus Arrival Time Prediction Algorithm Based on Spatio-temporal Correlation Attribute Model

Bus arrival time prediction is an important basis for the decision-making assistant system of bus dispatching. It helps dispatchers to find late vehicles in time and make reasonable dispatching decisions. However, bus arrival time is influenced by traffic congestion, weather, and variable time when stopping at stations or travelling duration between stations. It is a spatio- temporal dependence problem, which is quite challenging. This paper proposes a new algorithm called STPM for bus arrival time prediction based on deep neural network. The algorithm uses space-time components, attribute components and fusion components to predict the total bus arrival time from the starting point to the terminal. In this algorithm, time-dependence and space-time components are used to learn the internal spatio-temporal dependence. It uses attribute components to learn the influence of external factors, uses fusion components to fuse the output of temporal and spatial components, as well as attribute components, to predict the final results. Experimental results show that STPM can combine the advantages of convolutional neural network and recurrent neural network model to learn the key temporal and spatial features. The proposed algorithm outperforms existing methods on the error percentage and accuracy of bus arrival time prediction.