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Road network structure and ride-sharing accessibility: A network science perspective / Mingshu Wang in Computers, Environment and Urban Systems, vol 80 (March 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 80 (March 2020) Titre : Road network structure and ride-sharing accessibility: A network science perspective Type de document : Article/Communication Auteurs : Mingshu Wang, Auteur ; Zheyan Chen, Auteur ; Lan Mu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] Atlanta (Géorgie) [Termes IGN] autopartage [Termes IGN] densité de population [Termes IGN] gestion urbaine [Termes IGN] migration pendulaire [Termes IGN] modèle conceptuel de données localisées [Termes IGN] OpenStreetMap [Termes IGN] réseau routier [Termes IGN] système d'information géographique Résumé : (auteur) The prosperity of ride-sharing services has rippled in the communities of GIScience, transportation, and urban planning. Meanwhile, road network structure has been analyzed from a network science perspective that focuses on nodes and relational links and aims to predictive models. However, limited empirical studies have explored the relationship between road network structure and ride-sharing accessibility through such perspective. This paper utilizes the spatial Durbin model to understand the relationship between road network structure and ride-sharing accessibility, proxied by Uber accessibility, through classical network measures of degree, closeness, and betweenness centrality. Taking the city of Atlanta as a case study, we have found in addition to population density and road network density, larger values of degree centrality and smaller values of closeness centrality of the road network are associated with better accessibility of Uber services. However, the effects of betweenness centrality are not significant. Furthermore, we have revealed heterogeneous effects of degree centrality and closeness centrality on the accessibility of Uber services, as the magnitudes of their effects vary by different time windows (i.e., weekday vs. weekend, rush hour in the morning vs. evening). Network science provides us both conceptual and methodological measures to understand the association between road network structure and ride-sharing accessibility. In this study, we constructed road network structure measures with OpenStreetMap, which is reproducible, replicable, and scalable because of its global coverage and public availability. The study resonates with the notion of cities as the set of interactions across networks, as we have observed time-sensitive heterogeneous effects of road network structure on ride-sharing accessibility. Numéro de notice : A2020-190 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2019.101430 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1016/j.compenvurbsys.2019.101430 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94852 [article]