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A multi-objective optimization approach to the location of road weather information system in New York State

机译:纽约州道路天气信息系统位置的多目标优化方法

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Inclement weather is a threat to the safety of transportation systems as well as the efficiency of their operation. A road weather information system (RWIS) is a network of environmental sensor stations (ESS) that collect a range of real-time data about weather and pavement conditions. These systems can support highway officials and civilians in making more informed transportation safety decisions, particularly in times of adverse weather, by giving them more accurate and localized weather information. This enables the proper maintenance activities to be executed and safety to be restored while using minimum resources. However, because of the range of network characteristics and geographical factors affecting the implementation of ESS, no widely adopted guidelines exist that outline where to implement ESS in a network beyond taking into the physical criteria of an appropriate site, although several methods have been suggested. This paper aims to take a practical approach to solving the location problem of RWIS by proposing a unified multi-objective optimization methodology that takes into account vehicular accident data, vehicle miles traveled, area coverage, access to power and maintenance, and existing ESS. This study produces an exact solution method that produces a Pareto set of multiple efficient solutions. The proposed methodology is applied to a real world case study focused on the deployment of additional ESS in the existing RWIS network across New York State. Further, a sensitivity analysis is conducted to examine the effects of different parameters and a nonpreference solution is proposed.
机译:恶劣的天气对运输系统的安全及其运行效率构成威胁。道路天气信息系统(RWIS)是环境传感器站(ESS)的网络,该网络收集有关天气和路面状况的一系列实时数据。这些系统可以为公路官员和平民提供更准确和本地化的天气信息,从而帮助他们做出更明智的交通安全决策,尤其是在不利天气时。这样可以在使用最少资源的情况下执行适当的维护活动并恢复安全性。但是,由于网络特性的范围和影响ESS实施的地理因素,尽管建议了几种方法,但除了考虑适当站点的物理标准外,没有广泛采用的指南概述了在网络中何处实施ESS。本文旨在通过提出一种统一的多目标优化方法,以一种可行的方法来解决RWIS的位置问题,该方法应考虑车辆事故数据,行进的车辆行驶里程,区域覆盖范围,电力和维护的获取以及现有的ESS。这项研究产生了一种精确的求解方法,该方法可以生成一组帕累托的多个有效解。拟议的方法应用于实际案例研究,该案例研究的重点是在纽约州现有RWIS网络中部署其他ESS。此外,进行了敏感性分析以检查不同参数的影响,并提出了非优先解决方案。

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