Analyzing and modeling network travel patterns during the Ukraine invasion using crowd-sourced pervasive traffic data
| Autor: | S. Travis Waller, Moeid Qurashi, Anna Sotnikova, Lavina Karva, Sai Chand |
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| Rok vydání: | 2022 |
| Předmět: |
Social and Information Networks (cs.SI)
FOS: Computer and information sciences History Polymers and Plastics Mechanical Engineering Computer Science - Neural and Evolutionary Computing Computer Science - Social and Information Networks Applications (stat.AP) Neural and Evolutionary Computing (cs.NE) Business and International Management Statistics - Applications Industrial and Manufacturing Engineering Civil and Structural Engineering |
| Zdroj: | Web of Science |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.4185753 |
| Popis: | 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 because of 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. |
| Databáze: | OpenAIRE |
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