期刊名称：Transportation Research Interdisciplinary Perspectives
印刷版ISSN：2590-1982
出版年度：2019
卷号：3
页码：1-9
DOI：10.1016/j.trip.2019.100071
出版社：Elsevier BV
摘要：Operating modern multi-modal surface transportation systems are becoming increasingly automated and driven by decision support systems. One aspect necessary for successful, safe, reliable, and efficient operation of any transportation network is real-time and forecasted weather and pavement condition information. Providing such information requires an adaptive system capable of blending large amounts of observational and model data that arrives quickly, in disparate formats and times, and blends and optimizes their use via expert systems and machine-learning algorithms. Quality control of the data is also essential, and historical data is required to both develop expert-based empirical algorithms and train machine learning models. This paper reports on the open-source Pikalert® system that brings together weather information and real-time data from connected vehicles to provide crucial information to enhance the safety and efficiency of surface transportation systems. This robust framework can be applied to a diverse array of user community specifications and is designed to rapidly ingest more, unique data sets as they become available. Ultimately, the developmental framework of this system will provide critical environmental information necessary to promote the development, growth, refinement, and expanded adoption of automated and connected multi-modal vehicular systems globally.
