The role of travel demand and network centrality on the connectivity and resilience of an urban street system

Akbarzadeh, Meisam; Memarmontazerin, Soroush; Derrible, Sybil; Reihani, Sayed Farzin Salehi

doi:10.1007/s11116-017-9814-y

Cited by 52 publications

(18 citation statements)

References 36 publications

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“…By stressing the complementary nature of topological and dynamic approaches, Gauthier et al (7) proposed to dynamically weight the network graph with trafficrelated information to include traffic dynamics in centrality metrics computation. Indeed, in previous works (8,17,18), the sensitivity of edge weights and centrality measures with respect to traffic dynamics has been verified. More specifically, the correlation analysis between centrality measures (and, particularly, betweenness centrality values) and traffic variables (i.e., traffic flow) shows that by weighting the graph edges with (dynamic) travel time information permits the increase of the correlation coefficient (19)(20)(21).…”

Section: Resilience Approachesmentioning

confidence: 79%

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

Henry

Furno

Faouzi

2021

Transportation Research Record: Journal of the Transportation R

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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.

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Section: Resilience Approachesmentioning

confidence: 79%

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

Henry

Furno

Faouzi

2021

Transportation Research Record: Journal of the Transportation R

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“…CN provides a simple yet efficient abstract of a network's components and their relationships, allowing for the analysis of these relations by other methods, such as game theory or Monte Carlo simulations [30,40]. This combination of methods can significantly reduce the computational resource demands and provide comparability between various methodologies [39,41,42]. Moreover, CN can leverage other technologies, such as geographic information systems (GIS), to give the network abstract spatial meaning and more accurate representations [43,44].…”

Section: Resiliency In Transportation Systemsmentioning

confidence: 99%

“…For this study, we will focus our resilience assessment on the system's ability to preserve functionality; functionality evaluation here refers to the evaluation of suitable performance metrics for each level. For example, it is widely accepted in the literature to use, at a network level, network centrality and cohesion as performance criteria and resilience metrics [3,41,43,58,59]. While representing a small part of the system level, it is more common to use the shortest path to travel between point A and point B or to use changes in travel pace instead [7,28,[60][61][62].…”

Section: Mega Sports Events and Resiliencementioning

confidence: 99%

Resiliency Assessment of Road Networks during Mega Sport Events: The Case of FIFA World Cup Qatar 2022

Serdar

Al-Ghamdi

2021

Sustainability

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Hosting Mega Sport Events (MSEs) is a formidable expedition that requires enormous investments and that has the potential to reform the nation’s future and create a lasting legacy. However, the increase in environmental concerns is pushing host cities to adopt a compact event approach. Compactness increases the concentration of the load on host cities’ infrastructures, which have to preserve an acceptable level of functionality under any possible disturbance; in other words, they should be resilient. Among these infrastructures, the road network plays the most prominent role in the fans’ experiences and the event’s success. To assess its resilience during MSE, we proposed a multilevel assessment approach that focuses on the network cohesion and critical trips performance under several disturbance scenarios, including natural hazards, intentional attacks, and accidents. The framework was applied to the Doha road network, since Doha will be a host city for the FIFA World Cup in Qatar in 2022, which exhibited a high level of resilience to intentional attacks and accidents scenarios. However, during the natural hazard scenario (flooding), the network experienced severe fragmentation, signaling weak resilience and highlighting the need to improve storm management plans. Future research could investigate the use of weighted graphs to increase the accuracy or incorporate different assessment approaches into the framework.

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“…Data science methods are increasingly being applied to problems of urban infrastructure analysis and design ( 7 , 9 , 14 – 19 ). In particular, a wide variety of statistical learning approaches have been applied to predictive learning tasks within the realm of urban research ( 7 , 20 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine

Lee

Mulrow

Haboucha

et al. 2019

Transportation Research Record: Journal of the Transportation R

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This article applies machine learning (ML) to develop a choice model on three choice alternatives related to autonomous vehicles (AV): regular vehicle (REG), private AV (PAV), and shared AV (SAV). The learned model is used to examine users’ preferences and behaviors on AV uptake by car commuters. Specifically, this study applies gradient boosting machine (GBM) to stated preference (SP) survey data (i.e., panel data). GBM notably possesses more interpretable features than other ML methods as well as high predictive performance for panel data. The prediction performance of GBM is evaluated by conducting a 5-fold cross-validation and shows around 80% accuracy. To interpret users’ behaviors, variable importance (VI) and partial dependence (PD) were measured. The results of VI indicate that trip cost, purchase cost, and subscription cost are the most influential variables in selecting an alternative. Moreover, the attitudinal variables Pro-AV Sentiment and Environmental Concern are also shown to be significant. The article also examines the sensitivity of choice by using the PD of the log-odds on selected important factors. The results inform both the modeling of transportation technology uptake and the configuration and interpretation of GBM that can be applied for policy analysis.

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The role of travel demand and network centrality on the connectivity and resilience of an urban street system

Cited by 52 publications

References 36 publications

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

Approach to Quantify the Impact of Disruptions on Traffic Conditions using Dynamic Weighted Resilience Metrics of Transport Networks

Resiliency Assessment of Road Networks during Mega Sport Events: The Case of FIFA World Cup Qatar 2022

Attitudes on Autonomous Vehicle Adoption using Interpretable Gradient Boosting Machine

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