Evaluation of Remote Sensing Products and Their Application in Transportation Safety Analysis Over Texas
During the recent decade, with the emergence of smart systems and infrastructures, an unprecedented amount of large-scale data is generated that can be used in various types of research and studies. Moreover, scientists have found that multiple sources of information have to be merged together if a comprehensive analysis of a problem is to be conducted. Therefore, multidisciplinary studies are growing very fast in an effort to find the answers for problems of larger scales compared to previous approaches. Considering these two new realities, this research plans to find the answers to large-scale problems by using multiple data sources and combining multiple fields of analysis such as: (1) Hydrometeorology, (2) Transportation, (3) Data Science, and (4) Statistics.
Performance of remote sensing products was evaluated across multiple time scales and these products were then use to analyze the large-scale effect of adverse weather conditions on crash risk on Texas roadways. Moreover, by using advanced statistical techniques, a comprehensive analysis of precipitation events was conducted over the entire state of Texas and the outputs were merged with the crash data prepared using data mining techniques. Results show that more than 60% of crashes are related to adverse weather conditions during a year over Texas. Using advanced computational methods, the results from this study will clarify the role of adverse weather conditions on crash risk.