2022/08/08 by S. Travis Waller, Waller, S. Travis, Moeid Qurashi +7
Engineering · #Applications (stat.AP) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #Social and Information Networks (cs.SI) #Transportation Systems and Logistics
paper · pdf · doi:10.48550/arxiv.2208.04297
openalex publication_date 2022/08/08 · openalex created_date 2022/10/02 · openalex updated_date 2026/07/28
In 2022, Ukraine is suffering an invasion which has resulted in acute impacts playing out over time and geography. This paper examines the impact of the ongoing disruption on traffic behavior using analytics as well as zonal-based network models. The methodology is a data-driven approach that utilizes obtained travel-time conditions within an evolutionary algorithm framework which infers origin-destination demand values in an automated process based on traffic assignment. Because of the automation of the implementation, numerous daily models can be approximated for multiple cities. The novelty of this paper versus the previously published core methodology includes an analysis to ensure the obtained data is appropriate since some data sources were disabled due to the ongoing disruption. Further, novelty includes a direct linkage of the analysis to the timeline of disruptions to examine the interaction in a new way. Finally, specific network metrics are identified which are particularly suited for conceptualizing the impact of conflict disruptions on traffic network conditions. The ultimate aim is to establish processes, concepts and analysis to advance the broader activity of rapidly quantifying the traffic impacts of conflict scenarios.