移动轨迹大数据的挖掘、分析与利用，为解决交通拥堵等城市问题提供了新思路。本项目研究Spark框架下出租车轨迹大数据分析中的分布式建模与并行优化算法，致力于解决实时性、鲁棒性和精确性问题。具体的是，搭建基于Spark的分布式存储与并行计算平台，（1）采用时空动力学分布式建模方法研究乘客出行特征，基于海量小文件处理策略提出并行关联挖掘算法多维度分析出租车运行特性，动态感知交通状况；（2）设计面向相关性分析的并行近邻优化分类器和并行预测算法，实时预测交通流量；（3）利用古典概型与极大似然估计法构建基于路段的分布式等待时间预测模型和并行时序算法，推荐最佳候车地点；（4）提出并行近邻传播优化算法和分布式差分自回归移动平均优化模型预测乘客数量，并计算到达载客点的行驶时间和建立分布式排队模型估算在载客点的等待时间，推荐最快载客路线。本项目的研究能为复杂交通网络的动态监测与预警控制提供理论依据和实用技术。

Mining, analysis and utilization of mobile trajectory big data provide new ideas for solving the urban issues, such as traffic congestion. In this project, we focus on the timeliness, robustness and accuracy of distributed modeling and parallel optimization algorithms in the Spark-based taxi trajectory big data deep-mining and analysis. More specifically, this project intends to build a distributed storage and parallel computing platform based on the Spark. First, we study passenger travel characteristics using spatio-temporal dynamics of distributed modeling approach, and propose a parallel association mining algorithm to analyze taxi operating characteristics in multiple dimensions based on the processing strategies associated with massive small files, thereby perceiving real-time traffic conditions. Second, we develop a parallel nearest neighbor optimal classifier and a parallel prediction algorithm using correlation analysis to forecast traffic flow in real time. Third, using the classical probability and maximum likelihood estimation methods, we establish a link-based distributed waiting time prediction model and a parallel time series algorithm to recommend the best waiting spot. Finally, we present a parallel affinity propagation optimization algorithm and a distributed autoregressive integrated moving average optimization model to predict the number of passengers. Simultaneously, we compute the arrival travel time at the waiting spot and build a distributed queuing model for estimating passenger waiting time to recommend the fastest routes for passengers. The research of this project will offer a theoretical basis and technical support for the dynamic monitoring and early warning control of complex transportation networks.