基于时空大数据的高速公路智能监控系统交通拥堵建模
Modeling Traffic Congestion with Spatiotemporal Big Data for An Intelligent Freeway Monitoring System
摘要：
交通拥挤是一个复杂的、非线性的时空建模问题。智能交通系统通过收集和分析大量不同类别的信息，可以预测和控制交通流量和道路拥挤。这份报告提供了一个从北到南穿越台湾的旅行时间分析，反之亦然。本文以国道台南至高雄段为例，分析南台湾地区民众的交通状况。数据是使用台湾交通部提供的电子收费系统（ETC）记录的。我们使用MapReduce框架将数据处理成一个更小的任务，可以分布在多个计算机集群，以加快进程。结果表明，交通流时空模型受方向、工作时间和节假日的强烈影响，每周都有一个循环模式。时空数据集内的独特模式可以用于人工智能决策系统，以供未来开发。
英文摘要：
Traffic congestion is a complex, nonlinear spatiotemporal modeling problem. By collecting and analyzing a vast quantity and different categories of information, traffic flow, and road congestion can be predicted and controlled on an intelligent transportation system. This report provides an analysis of traveling time across Taiwan from North to South, vice versa. We analyze traffic in a national freeway between Tainan and Kaohsiung section, which represents the common trip of the population in Southern Taiwan. The data is recorded using the Electronic Toll Collection System (ETC) provided by Ministry of Transportation in Taiwan. We use MapReduce framework to process data into a smaller task which can be distributed on several computer clusters to speed up the process. The results show that the spatiotemporal model of traffic flow is strongly influenced by direction, working hour, and holidays with a recurring pattern for each week. The distinctive pattern inside the spatiotemporal dataset can be used on an AI-powered decision-making system for future development.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
