Analyzing and Modeling Network Travel Patterns During the Ukraine Invasion Using Crowd-Sourced Pervasive Traffic Data

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

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.

Details

OriginalspracheEnglisch
Seiten (von - bis)491-507
Seitenumfang17
FachzeitschriftTransportation research record
Jahrgang2677 (2023)
Ausgabenummer10
PublikationsstatusVeröffentlicht - 17 Apr. 2023
Peer-Review-StatusJa

Externe IDs

ORCID /0000-0002-2939-2090/work/151979654
ORCID /0000-0002-0135-6450/work/151982390

Schlagworte

ASJC Scopus Sachgebiete

Schlagwörter

  • disaster response, performance metrics, planning and preparedness, planning data analysis, planning methods, sustainability and resilience, transportation network modeling