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Utilizing an Analytical Hierarchy Process with Stochastic Return On Investment to Justify Connected Vehicle-Based Deployment Decisions
Transportation Research Record: Journal of the Transportation Research Board ( IF 1.6 ) Pub Date : 2020-07-12 , DOI: 10.1177/0361198120929686
Mahmoud Arafat 1 , Shahadat Iqbal 1 , Mohammed Hadi 1
Affiliation  

With the increasing interest in connected vehicles (CV), it becomes all the more important to support decisions by transportation agencies to invest in Connected Vehicle to Infrastructure (V2I) applications. This paper presents a method that can be used to justify the investment in CV-based safety applications considering the availability of existing solutions. The method utilizes a combination of stochastic return on investment (ROI) analysis and a multi-criteria decision-analysis (MCDA) procedure to account for uncertainties, to consider effects that cannot be converted to dollar values, and to account for stakeholder priorities. The stochastic ROI analysis is applied using Monte Carlo simulations and included as part of the selection criteria in the MCDA method using the Analytical Hierarchy Process (AHP). This paper applies the method to support the deployment of CV-based applications to address transportation safety concerns on urban arterials. These applications can be categorized as CV-based support of signalized intersection safety, CV-based support of unsignalized intersection safety, and CV-based hazard warning applications. The results of the Monte Carlo simulation analysis for a project case study indicated the cost-effectiveness of these applications. The results of the AHP analysis indicate that utilizing V2I applications is 41.3% more favorable than utilizing the investigated existing solutions to address safety concern on the arterial facility that is the subject of the case study.



中文翻译:

利用具有随机投资回报率的层次分析过程来证明基于车辆的互联部署决策的合理性

随着人们对联网汽车(CV)的兴趣日益增加,支持运输机构做出的投资联网汽车到基础设施(V2I)应用程序的决定变得越来越重要。考虑到现有解决方案的可用性,本文提出了一种可用于证明基于CV的安全应用投资的合理性的方法。该方法结合了随机投资回报(ROI)分析和多标准决策分析(MCDA)程序,以解决不确定性,考虑无法转换为美元价值的影响并考虑利益相关者的优先级。随机ROI分析是使用蒙特卡洛模拟进行的,并作为使用层次分析法(AHP)的MCDA方法的选择标准的一部分。本文应用该方法来支持基于CV的应用程序的部署,以解决城市动脉交通安全问题。这些应用程序可以分类为基于CV的信号交叉口安全支持,基于CV的无信号交叉口安全支持以及基于CV的危险警告应用程序。一个项目案例研究的蒙特卡洛模拟分析的结果表明了这些应用的成本效益。AHP分析的结果表明,利用V2I应用程序比利用已研究的现有解决方案解决作为案例研究主题的动脉设施的安全性问题要好41.3%。这些应用程序可以分类为基于CV的信号交叉口安全支持,基于CV的无信号交叉口安全支持以及基于CV的危险警告应用程序。一个项目案例研究的蒙特卡洛模拟分析结果表明了这些应用的成本效益。AHP分析的结果表明,利用V2I应用程序比利用已研究的现有解决方案解决作为案例研究主题的动脉设施的安全性问题要好41.3%。这些应用程序可以分类为基于CV的信号交叉口安全支持,基于CV的无信号交叉口安全支持以及基于CV的危险警告应用程序。一个项目案例研究的蒙特卡洛模拟分析结果表明了这些应用的成本效益。AHP分析的结果表明,利用V2I应用程序比利用已研究的现有解决方案解决作为案例研究主题的动脉设施的安全性问题要好41.3%。

更新日期:2020-07-13
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