The path most traveled: Travel demand estimation using big data resources
The path most traveled: Travel demand estimation using big data resources
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DOI：
10.1016/j.trc.2015.04.022
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发表时间：
2015-09-01
影响因子：
8.3
通讯作者：
Gonzalez, Marta C.
中科院分区：
文献类型：
--
作者：
Toole, Jameson L.;Colak, Serdar;Gonzalez, Marta C.
快速的城市化正在给本已不堪重负的交通基础设施带来越来越大的压力。无处不在的移动计算及其产生的海量数据为衡量这一基础设施的需求、诊断问题和规划未来提供了新的机会。然而，在实现这些好处之前，必须更新方法和模型，将这些新的数据源整合到现有的城市和交通规划框架中，以估计出行需求和基础设施使用情况。虽然最近的工作取得了很大进展，从新的数据资源中提取有效和有用的测量方法，但很少有端到端的解决方案将原始的海量数据转换和集成到旅行需求和基础设施性能的估计中。在这里，我们提出了一个灵活的、模块化的、计算高效的软件系统来填补这一空白。我们的系统使用移动电话的呼叫详细信息记录(CDR)以及开放和众包的地理空间数据、人口普查记录和调查来估计旅行需求的多个方面。我们将许多现有的和新的算法结合在一起，以生成具有代表性的始发地-目的地矩阵，通过使用开放和众包数据存储库构建的道路网络选择行程，并对系统的输出进行分析。我们还提供了一个在线、交互式可视化平台，将这些结果传达给研究人员、政策制定者和公众。我们通过对全球多个城市的分析，展示了该系统的灵活性。我们希望这项工作将对将新的大数据资源整合到常规交通需求建模中起到统一和全面的指导作用。(C)爱思唯尔有限公司出版的2015年。
Rapid urbanization is placing increasing stress on already burdened transportation infrastructure. Ubiquitous mobile computing and the massive data it generates presents new opportunities to measure the demand for this infrastructure, diagnose problems, and plan for the future. However, before these benefits can be realized, methods and models must be updated to integrate these new data sources into existing urban and transportation planning frameworks for estimating travel demand and infrastructure usage. While recent work has made great progress extracting valid and useful measurements from new data resources, few present end-to-end solutions that transform and integrate raw, massive data into estimates of travel demand and infrastructure performance. Here we present a flexible, modular, and computationally efficient software system to fill this gap. Our system estimates multiple aspects of travel demand using call detail records (CDRs) from mobile phones in conjunction with open- and crowdsourced geospatial data, census records, and surveys. We bring together numerous existing and new algorithms to generate representative origin-destination matrices, route trips through road networks constructed using open and crowd-sourced data repositories, and perform analytics on the system's output. We also present an online, interactive visualization platform to communicate these results to researchers, policy makers, and the public. We demonstrate the flexibility of this system by performing analyses on multiple cities around the globe. We hope this work will serve as unified and comprehensive guide to integrating new big data resources into customary transportation demand modeling. (C) 2015 Published by Elsevier Ltd.