Preventing weather-related crashes is a significant part of maintaining the safety and mobility of the travelling public during winter months. To help mitigate detrimental effects of winter road conditions, transportation authorities rely on real-time and near-future road weather and surface condition information disseminated by road weather information systems (RWIS) to make more timely and accurate winter road maintenance-related decisions. However, the significant costs of these systems motivate governments to develop a framework for determining a region-specific optimal RWIS density. Building on our previous study to facilitate regional network optimization, this study is aimed at considering the nature of spatiotemporally varying RWIS measurements and integrating larger case studies comprising eight different US states. Space-time semivariogram models were developed to quantify the representativeness of RWIS measurements and examine their effects on regional topography and weather severity for improved generalization. The optimal RWIS density for different topographic and weather severity regions was then determined via one of the most successful combinatorial optimization techniques—particle swarm optimization. The findings of this study revealed a strong dependency of optimal RWIS density on varying environmental characteristics of the region under investigation. It is anticipated that the RWIS density guidelines developed in this study will provide decision makers with a tool they need to help design a long-term RWIS implementation plan.

中文翻译：

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使用时空半变异函数模型开发全州最佳RWIS密度准则

预防与天气有关的交通事故是维护冬季旅行公众的安全和机动性的重要组成部分。为了帮助减轻冬季道路状况的不利影响，运输当局依靠道路天气信息系统（RWIS）传播的实时和近乎未来的道路天气和地面状况信息来做出与冬季道路维护相关的更及时，准确的决策。但是，这些系统的巨额成本促使各国政府开发一种框架，以确定特定区域的最佳RWIS密度。在我们先前的研究以促进区域网络优化为基础的基础上，本研究旨在考虑时空变化的RWIS度量的性质，并整合由八个美国州组成的大型案例研究。开发了时空半变异函数模型以量化RWIS测量的代表性，并检查其对区域地形和天气严重程度的影响，以提高通用性。然后，通过最成功的组合优化技术之一（粒子群优化）确定不同地形和天气严重性区域的最佳RWIS密度。这项研究的结果表明，最佳RWIS密度强烈依赖于所调查区域的各种环境特征。可以预期，本研究中制定的RWIS密度指南将为决策者提供他们所需的工具，以帮助他们设计长期RWIS实施计划。