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traffic-big-data-theory-and-applications:《交通大数据理论与方法》-源码

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《交通大数据理论与方法》 目录 背景 随着信息通讯技术的不断发展，各行各业都产生了海量的数据，与此同时，一门新的学科应运而生—— 数据挖掘。数据挖掘是从大量数据（包括文本数据）中挖掘出隐含的、先前未知的、对决策有潜在价值的信 息、知识和关联关系，并基于这些信息和相应规则建立可用于决策支持与优化分析的模型，提供可支持预测 性决策的方法和工具。此外，数据挖掘还可帮助企业和科研团体发现业务与学科中的新趋势，揭示已知的 事实，预测未知的结果，因此“数据挖掘”已成为其保持竞争力的必要手段。 在大数据发展的背景下，交通领域的各类大数据同样以惊人的速度产生，并被应用于各种各样的应用 场景。例如，基于手机信令数据分析城市人口分布及交通出行分布特征，从而为交通规划的进一步决策提 供量化依据；利用网约车数据分析城市路网流量，进而优化信号配时方案，提升交通控制方案的效率；利用 线圈、视频、浮动车等多源数据，实现

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