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Performance measure for reliable travel time of emergency vehicles

机译:应急车辆可靠行驶时间的性能衡量

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Travel time is very critical for emergency response and emergency vehicle (EV) operations. Compared to ordinary vehicles (OVs), EVs are permitted to break conventional road rules to reach the destination within shorter time. However, very few previous studies address the travel time performance of EVs. This study obtained nearly 4-year EV travel time data in Northern Virginia (NOVA) region using 76,000 preemption records at signalized intersections. First, the special characteristics of EV travel time are explored in mean, median, standard deviation and also the distribution, which display largely different characteristics from that of OVs in previous studies. Second, a utility-based model is proposed to quantify the travel time performance of EVs. Third, this paper further investigates two important components of the utility model: benchmark travel time and standardized travel time. The mode of the distribution is chosen as benchmark travel time, and its nonlinear decreasing relationship with the link length is revealed. At the same time, the distribution of standardized travel time is fitted with different candidate distributions and Inv. Gaussian distribution is proved to be the most suitable one. Finally, to validate the proposed model, we implement the model in case studies to estimate link and route travel time performance. The results of route comparisons also show that the proposed model can support EV route choice and eventually improve EV service and operations. (C) 2016 Elsevier Ltd. All rights reserved.
机译:出行时间对于应急响应和应急车辆(EV)操作非常重要。与普通车辆(OVs)相比,允许EV违反常规道路规则以在较短时间内到达目的地。但是,很少有研究涉及电动汽车的行驶时间性能。这项研究使用信号交叉口的76,000个抢先记录,获得了北弗吉尼亚(NOVA)地区近4年的EV行驶时间数据。首先,在平均值,中位数,标准差以及分布方面探讨了电动汽车行驶时间的特殊特征,这些电动汽车的行驶时间与以往研究中的电动汽车具有很大的不同。其次,提出了一种基于效用的模型来量化电动汽车的行驶时间性能。第三,本文进一步研究了本实用新型的两个重要组成部分:基准旅行时间和标准旅行时间。选择分布方式作为基准行进时间,揭示了其与链节长度的非线性递减关系。同时,标准旅行时间的分布与不同的候选分布和Inv拟合。高斯分布被证明是最合适的分布。最后,为了验证所提出的模型,我们在案例研究中实施了该模型,以估算链接和路线的旅行时间性能。路线比较的结果还表明,该模型可以支持电动汽车的路线选择,并最终改善电动汽车的服务和运营。 (C)2016 Elsevier Ltd.保留所有权利。

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