Spatiotemporal Modeling and Real-Time Prediction of Origin-Destination Traffic Demand
Spatiotemporal Modeling and Real-Time Prediction of Origin-Destination Traffic Demand
复制标题
DOI：
10.1080/00401706.2019.1704887
复制
发表时间：
2020-01
期刊：
影响因子：
2.5
通讯作者：
Xiaochen Xian;Honghan Ye;Xin Wang;Kaibo Liu
中科院分区：
文献类型：
--
作者：
Xiaochen Xian;Honghan Ye;Xin Wang;Kaibo Liu
摘要交通需求预测是交通规划、调度和优化的重要问题。由于数据的高度可变性和复杂的时空相关性，对起讫点（OD）对的交通需求量预测一直被认为是具有挑战性的。虽然有一些文章已经考虑估计交通流量在特定位置观察到的计数，现有的交通预测模型很少处理时空需求计数数据的某些OD对，或他们未能有效地考虑交通网络的领域知识，以提高预测精度的交通需求。为了解决上述挑战，我们制定并提出了一个多变量泊松对数正态模型与特定的参数化量身定制的交通需求的问题，它捕获的时空相关性的交通需求在不同的路线和时期，并自动聚类的基础上的需求相关性的路线。该模型进一步估计使用期望最大化算法，并应用于预测未来的需求计数在随后的时代。估计和预测程序采用马尔可夫链蒙特卡罗抽样，以克服计算的挑战。最后通过对纽约一辆黄色出租车的数据进行仿真和真实的应用，验证了该方法的适用性和有效性。本文的补充材料可在网上查阅。
Abstract Traffic demand prediction has been a crucial problem for the planning, scheduling, and optimization in transportation management. The prediction of traffic demand counts for origin-destination (OD) pairs has been considered challenging due to the high variability and complicated spatiotemporal correlations in the data. Though several articles have considered estimating traffic flows from counts observed at specific locations, existing traffic prediction models seldom dealt with spatiotemporal demand count data of certain OD pairs, or they failed to effectively consider domain knowledge of the traffic network to enhance the prediction accuracy of traffic demand. To tackle the aforementioned challenges, we formulate and propose a multivariate Poisson log-normal model with specific parameterization tailored to the traffic demand problem, which captures the spatiotemporal correlations of the traffic demand across different routes and epochs, and automatically clusters the routes based on the demand correlations. The model is further estimated using an expectation-maximization algorithm and applied for predicting future demand counts at the subsequent epochs. The estimation and prediction procedures incorporate Markov chain Monte Carlo sampling to overcome the computational challenges. Simulations as well as a real application on a New York yellow taxi data are performed to demonstrate the applicability and effectiveness of the proposed method. Supplementary materials for this article are available online.