Spatiotemporal Modeling and Real-Time Prediction of Origin-Destination Traffic Demand

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.

交通需求预测一直是交通管理领域规划、调度与优化的核心问题。针对起讫点（Origin-Destination, OD）对的交通需求流量预测，由于数据存在高度变异性与复杂的时空相关性，一直被认为是极具挑战性的任务。尽管已有不少研究尝试基于特定观测点位的流量数据估算交通流量，但现有的交通预测模型要么极少针对特定OD对的时空需求流量数据开展研究，要么未能有效结合交通网络领域知识以提升交通需求预测精度。为解决上述挑战，本文构建并提出了一种针对交通需求问题定制参数化方式的多元泊松对数正态模型，该模型能够捕捉不同路径与时段间交通需求的时空相关性，并基于需求相关性自动对路径进行聚类。本文进一步采用期望最大化（Expectation-Maximization, EM）算法对该模型进行参数估计，并将其用于预测后续时段的未来需求流量。模型的估计与预测过程融入了马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）采样方法，以应对计算层面的挑战。本文通过仿真实验与纽约黄色出租车（New York yellow taxi）真实数据集的应用案例，验证了所提方法的适用性与有效性。本文的补充材料可在线获取。