Agent-based modeling to simulate road travel using Big Data from smartphone GPS: An application to the continental United States
Agent-based modeling to simulate road travel using Big Data from smartphone GPS: An application to the continental United States
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使用智能手机 GPS 的大数据进行基于代理的建模来模拟道路旅行：在美国大陆的应用
DOI：
10.1109/bigdata47090.2019.9006339
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Pinjari
中科院分区：
文献类型：
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作者：
Sashikanth Gurram;V. Sivaraman;Jonathan T. Apple;A. Pinjari
人们对城市可持续性、经济和公共卫生活力以及气候变化日益关注是世界各地的共同特征。交通运输往往与这些问题有着千丝万缕的联系，这就需要开发强大且可扩展的工具，以帮助及时了解智能体与系统的交互。这种方便而准确的分析对于政策制定至关重要，特别是在当前城市交通正在经历快速转型的环境下。为了支持此类分析，我们演示了一种新颖的方法，该方法使用全球定位系统 (GPS) 派生的原始观测数据，对城市旅行进行自上而下的大规模基于代理的模拟。具体来说，我们使用整个美国大陆单日（2019 年 3 月 6 日星期三）的 GPS 数据构建了设备（即代理）的日常活动和旅行模式。应用数据过滤技术来识别大约 270 万台高度可见和移动的智能设备（每日总数为 3050 万台）。我们从开放街道地图 (OSM) 获取了整个北美的道路网络数据。然后，我们将客服人员的日常活动和出行记录以及道路网络数据输入 MATSim（一个基于客服人员的出行模拟器），以生成高度时空解析的客服人员活动及其估计的出行轨迹。我们处理这些出行轨迹（15 亿条记录），以估计美国每个州的车辆行驶里程 (VMT)，并对美国大陆每个道路连接的车辆流量进行建模。总体而言，我们发现我们的结果与联邦公路管理局的 VMT 估计值之间存在很强的排名相关性，尽管绝对测量值显示出较高的变异性。当将推断的交通量与选定州（佛罗里达州）的道路计数站数据进行比较时，我们在分类道路链路级别观察到类似的趋势（即低等级相关误差但较高的绝对误差）。最后，我们的道路流量估计的均方根误差与佛罗里达州区域性出行需求模型的均方根误差比较相似，表明模型性能令人满意。我们研究中提出的方法表明，这种由大数据驱动的大规模基于代理的模拟可以为估计和预测旅行需求提供价值。
Growing concerns about urban sustainability, economic and public health vitality, and climate change are common features across the world. Transportation is often inextricably linked to these concerns and this necessitates the development of robust and scalable tools that can assist in timely understanding of the agent-system interactions. Such expedient but accurate analyses are critical for policymaking, especially in the current environment where urban mobility is witnessing a rapid transformation. To support such analyses, we demonstrate a novel methodology that implements a top-down large-scale agent-based simulation of urban travel using Global Positioning System (GPS) derived raw sightings. Specifically, we constructed the daily activity and travel patterns of devices (i.e. agents) using GPS data for a single day (Wednesday, March 6, 2019) for the entire continental United States. Data filtering techniques were applied to identify approximately 2.7 million smart devices (out of a daily total of 30.5 million) that were highly visible and mobile. We sourced roadway network data for the entire North America from Open Street Maps (OSM). We then fed the daily activity and travel records of agents along with the roadway network data into MATSim, an agent-based travel simulator, to produce highly spatiotemporally resolved agent activities along with their estimated travel trajectories. We processed these travel trajectories (1.5 billion records) to estimate vehicle miles traveled (VMT) for each U.S. state and modeled vehicle volumes per roadway link in the continental U.S. Overall, we found strong rank correlations between our results and Federal Highway Administration’s VMT estimates, although absolute measures displayed a higher variability. We observed similar trends (i.e. low rank correlation errors but higher absolute errors) at the disaggregate roadway link level when comparing our extrapolated traffic volumes against roadway count station data from a select state (Florida). Finally, root mean squared error of our roadway volume estimates are comparatively similar to those for Florida’s regionwide travel demand models indicating a satisfactory model performance. The proposed methodology in our study demonstrates that such big data-powered large-scale agent-based simulations may provide value in estimating and predicting travel demand.