Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks
| Autor: | Nour-Eddin El Faouzi, Elise Henry, Angelo Furno |
|---|---|
| Přispěvatelé: | Laboratoire d'Ingénierie Circulation Transport (LICIT UMR TE ), École Nationale des Travaux Publics de l'État (ENTPE)-Université de Lyon-Université Gustave Eiffel, RP1-S19100 , PROMENADE, Platform for Resilient Multi-modal Mobility via Multi-layer Networks & Real-time Big-Data Processing |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
0211 other engineering and technologies RESEAU DE TRANSPORT 02 engineering and technology CIRCULATION ROUTIERE EXPLOITATION DE RESEAU DE TRANSPORT 0502 economics and business TRAFIC ROUTIER DISRUPTIONS Resilience (network) Civil and Structural Engineering 050210 logistics & transportation PERTURBATION Mechanical Engineering 05 social sciences 021107 urban & regional planning RESILIENCE MODELISATION [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation CHARGE DE TRAFIC TRANSPORT NETWORKS GESTION DU TRAFIC Risk analysis (engineering) RECUEIL DE DONNEES Traffic conditions WEIGHTED DEGREE CENTRALITY |
| Zdroj: | Transportation Research Record Transportation Research Record, 2021, pp1-18. ⟨10.1177/0361198121998663⟩ |
| Popis: | Transport networks are essential for societies. Their proper operation has to be preserved to face any perturbation or disruption. It is therefore of paramount importance that the modeling and quantification of the resilience of such networks are addressed to ensure an acceptable level of service even in the presence of disruptions. The paper aims at characterizing network resilience through weighted degree centrality. To do so, a real dataset issued from probe vehicle data is used to weight the graph by the traffic load. In particular, a set of disrupted situations retrieved from the study dataset is analyzed to quantify the impact on network operations. Results demonstrate the ability of the proposed metrics to capture traffic dynamics as well as their utility for quantifying the resilience of the network. The proposed methodology combines different metrics from the complex networks theory (i.e., heterogeneity, density, and symmetry) computed on temporal and weighted graphs. Time-varying traffic conditions and disruptions are analyzed by providing relevant insights on the network states via three-dimensional maps. |
| Databáze: | OpenAIRE |
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