Eliminating the impacts of traffic volume variation on before and after studies: a causal inference approach

IF 2.8 3区 工程技术 Q3 TRANSPORTATION
Xiaobo Ma , Abolfazl Karimpour , Yao-Jan Wu
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引用次数: 0

Abstract

A before and after study framework measures the outcomes in a group of participants before introducing an intervention, and then again afterward. In this study, a before and after study framework is adopted to evaluate the effectiveness of transportation policies and emerging technologies. Generally, the outcome of every before and after study will help decision-makers to monitor and understand the effects of interventions and to make sound decisions. However, many factors such as seasonal factors, holidays, and lane closures might interfere with the evaluation process by inducing variation in traffic volume during the before and after periods. In practice, limited effort has been made to eliminate the effects of these factors. In this study, an extreme gradient boosting (XGBoost)-based propensity score matching (PSM) method is proposed to reduce the biases caused by traffic volume variation during the before and after periods. In order to evaluate the effectiveness of the proposed method, a corridor in the City of Chandler, Arizona where an advanced traffic signal control system has been recently implemented was selected. The results indicated that the proposed method can effectively eliminate the variation in traffic volume caused by the COVID-19 during the evaluation process. In addition, the results of the t-test and Kolmogorov-Smirnov (KS) test demonstrated that the proposed method outperforms other state-of-the-art PSM methods. The application of the proposed method is also transferrable to other before and after evaluation studies and can significantly assist transportation engineers to eliminate the impacts of traffic volume variation on the evaluation process.
消除交通量变化对研究前后的影响:一种因果推理方法
在引入干预措施之前和之后的研究框架测量了一组参与者的结果。在本研究中,采用前后研究框架来评估交通政策和新兴技术的有效性。通常,每次前后研究的结果将有助于决策者监测和了解干预措施的效果,并做出合理的决策。然而,许多因素,如季节因素、节假日和车道关闭,可能会通过诱导前后交通量的变化来干扰评估过程。在实践中,为消除这些因素的影响所作的努力有限。本文提出了一种基于极限梯度提升(XGBoost)的倾向得分匹配(PSM)方法,以减少交通流量前后变化所造成的偏差。为了评估所提出的方法的有效性,选择了亚利桑那州钱德勒市的一条走廊,该走廊最近实施了先进的交通信号控制系统。结果表明,该方法能有效消除评价过程中因新冠肺炎引起的交通量变化。此外,t检验和Kolmogorov-Smirnov (KS)检验的结果表明,所提出的方法优于其他最先进的PSM方法。该方法的应用也可推广到其他前后评价研究中,并可显著帮助交通工程师消除交通量变化对评价过程的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.80
自引率
19.40%
发文量
51
审稿时长
15 months
期刊介绍: The Journal of Intelligent Transportation Systems is devoted to scholarly research on the development, planning, management, operation and evaluation of intelligent transportation systems. Intelligent transportation systems are innovative solutions that address contemporary transportation problems. They are characterized by information, dynamic feedback and automation that allow people and goods to move efficiently. They encompass the full scope of information technologies used in transportation, including control, computation and communication, as well as the algorithms, databases, models and human interfaces. The emergence of these technologies as a new pathway for transportation is relatively new. The Journal of Intelligent Transportation Systems is especially interested in research that leads to improved planning and operation of the transportation system through the application of new technologies. The journal is particularly interested in research that adds to the scientific understanding of the impacts that intelligent transportation systems can have on accessibility, congestion, pollution, safety, security, noise, and energy and resource consumption. The journal is inter-disciplinary, and accepts work from fields of engineering, economics, planning, policy, business and management, as well as any other disciplines that contribute to the scientific understanding of intelligent transportation systems. The journal is also multi-modal, and accepts work on intelligent transportation for all forms of ground, air and water transportation. Example topics include the role of information systems in transportation, traffic flow and control, vehicle control, routing and scheduling, traveler response to dynamic information, planning for ITS innovations, evaluations of ITS field operational tests, ITS deployment experiences, automated highway systems, vehicle control systems, diffusion of ITS, and tools/software for analysis of ITS.
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