1,721,053 research outputs found
sj-pdf-1-trr-10.1177_03611981231166942 – Supplemental material for Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit
No full textSupplemental material, sj-pdf-1-trr-10.1177_03611981231166942 for Evolution of Mode Use During the COVID-19 Pandemic in the United States: Implications for the Future of Transit by Tassio B. Magassy, Irfan Batur, Aupal Mondal, Katherine E. Asmussen, Chandra R. Bhat, Deborah Salon, Matthew Bhagat-Conway, Mohammadjavad Javadinasr, Rishabh Chauhan, Abolfazl (Kouros) Mohammadian, Sybil Derrible and Ram M. Pendyala in Transportation Research Record</p
Marc Barthelemy, <i>The structure and dynamics of cities: Urban data analysis and theoretical modeling</i>
No full textpreprocessed OpenStreetMap data edges for Côte d'Ivoire
No full textOpenStreetMap data for Côte d'Ivoire (downloaded on 4 March 2020) transformed to network format following the methods of Karduni et al. (2016).
References
Karduni, Alireza, Amirhassan Kermanshah, and Sybil Derrible. "A protocol to convert spatial polyline data to network formats and applications to world urban road networks." Scientific data 3.1 (2016): 1-7
Métro: centralité et robustesse
No full textDemain matin, nous aurons un TP pour le cours sur les réseaux, les flux et les transports. En particulier, en nous inspirant des travaux de Sybil Derrible, nous allons commencer par étudier la centralité dans les différents systèmes de métro, mais aussi la robustesse. Les matrices d'adjacence d'une trentaine de métros dans le monde sont en ligne dans un fichier xls. Histoire de gagner un peu de temps, le code pour créer une matrice d'adjacence peut être le suivant loc="/data/Metro_Networks_Ad..
Complexity in future cities: the rise of networked infrastructure
No full textHow will urban infrastructure systems (UIS) be planned in future cities? In the twenty-first century, cities will need to overcome many challenges. They will need to accommodate a growing urban population who aspire for higher standards of living, while reducing the amount of energy and resources that are being consumed, and UIS planning will be central to address these challenges. A conceptual approach is taken in this article to envision the role and structure of UIS in future cities. First by recalling concepts of diminishing marginal returns from Joseph Tainter, a brief history of infrastructure planning is then offered, spanning from the early human settlements and the Roman aqueducts to modern planning. A discussion of the current structure and co-dependence of UIS follows, and ideas are presented to better engineer networked infrastructure systems, notably using elements of complexity science. Finally, some ideas are offered to leverage current advances in information technology to better coordinate UIS planning across various departments. Overall, UIS planning is bound to change dramatically, and better integrating them into networked infrastructure may be key to solve our current challenges in future cities
Evolution of highest <i>C<sub>Bhi</sub></i> and lowest non-zero <i>C<sub>Blo</sub></i> betweenness centralities with size.
No full text<p>While both centralities fit power law functions, the exponent of highest betweenness is much lower than the lowest betweenness, suggesting that the loss of share in betweenness from most central nodes does not decay as fast as for least central nodes.</p
Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings
No full textAs cities keep growing worldwide, so does the demand for key resources such as energy (electricity and gas) and water that residents consume. Meeting the demand for these resources can be challenging and requires an understanding of their consumptions patterns. In this work, we apply XGBoost (Extreme Gradient Boosting) to predict and analyze water and energy consumption in large-scale buildings in New York City. For this, the New York City’s local law 84 extensive dataset was merged with the Primary Land Use Tax Lot Output (PLUTO) dataset as well as with other socio-economic databases. Specifically, we developed three models: electricity, gas, and water consumption. Seven major lessons were learnt in terms of interrelationships between electricity, gas, and water consumption. In particular, water and gas consumption are highly interrelated with one another (often because gas is used for water heating). Furthermore, electricity consumption is affected by building type, and electricity and water consumption are particularly interrelated in nonresidential buildings. Overall, the knowledge gained from the models and from the SHAP analysis can help planners, engineers, and policymakers develop more effective strategies and help them manage the demand for energy and water in large-scale buildings
Results for 28 metro networks.
No full text*<p>minimum non-zero values since termini have no betweenness.</p
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