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Big Data and the Calibration and Validation of Traffic Simulation Models

机译：大数据与交通仿真模型的校准与验证

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摘要

Real-world data are the essential elements in the validation and calibration of microscopicrntraffic simulation models. Availability, accuracy and relevance of real-world data canrnseriously affect the reliability of the models’ predictions. Traditional sources of traffic data arerneither limited to a limited number of typical conditions or may not be reliable enough. With thernadvent of new technologies, information is on the fingertips of users by means of smartphones,rnGPS-equipped devices, and radio frequency identification (RFID) readers. The rapid rise inrninformation technology has also resulted in innovative ways to obtain space- and time-sensitiverninformation in real time. This, in turn, has led to massive amount of passively collected locationrnand event data for various time periods, also called “Big Data.” With the availability of Big Datarnthere is an opportunity to validate and calibrate traffic simulation models in a way that has neverrnbeen possible in the past. In this paper, we examine the current practice of calibration of trafficrnsimulation models with an emphasis on data needs. We also describe the various sources of BigrnData that might be available to the traffic simulation community now being collected through invehiclernand infrastructure-based technologies. Various real-world case studies are presented tornillustrate the importance and future of Big Data in the calibration of traffic simulation models.rnFuture applications of Big Data are also discussed in detail.
机译：真实世界的数据是微观交通仿真模型的验证和校准的基本要素。实际数据的可用性，准确性和相关性严重影响模型预测的可靠性。传统的交通数据源要么局限于有限数量的典型条件，要么可能不够可靠。随着新技术的出现，信息通过智能手机，配备GPS的设备和射频识别（RFID）读取器触手可及。快速增长的信息技术还带来了创新的方式，可以实时获取时空敏感信息。反过来，这导致了在各个时间段内大量被动收集的位置和事件数据，也称为“大数据”。随着大数据的可用性，将有机会以前所未有的方式来验证和校准交通仿真模型。在本文中，我们研究了流量模拟模型的当前校准实践，重点是数据需求。我们还描述了BigrnData的各种来源，这些数据现在可以通过基于交通工具和基于基础架构的技术收集到的流量模拟社区中。提出了各种实际案例研究，以说明大数据在交通仿真模型校准中的重要性和未来。还详细讨论了大数据的未来应用。

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