Identifying Changes in Critical Locations for Transportation Networks Using Centrality

Aydin, Nazli Yonca; Casali, Ylenia; Düzgün, Şebnem; Heinimann, Hans Rudolf

doi:10.1007/978-3-030-19424-6_22

Cited by 9 publications

(6 citation statements)

References 27 publications

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“…There is a wide range of literature that defines accessibility factors (Geurs and Wee 2004;Li et al 2021;Wee and Mouter 2021), and these factors are essential for evaluating accessibility in the context of education. Some critical services (e.g., job centers (Hu and Downs 2019), healthcare (Aydin et al 2019;Cheng et al 2020)) and their accessibility had been studied more than others. Especially, novel methodologies developed to measure the accessibility to educational services have fewer examples than the other services.…”

Section: School Accessibility Factorsmentioning

confidence: 99%

“…For example, Aydin et al (2018) used topological metrics to measure the resilience of transportation networks after the major Gorkha earthquake in 2015 in Kathmandu, Nepal. Aydin et al (2019) used a modified version of betweenness centrality to identify the critical locations when traveling to a healthcare service using a modified betweenness centrality. Wang et al (2020) examined the relationship between road network structure and ride-sharing accessibility by analyzing degree, closeness, and betweenness centrality.…”

Section: Complex Network Approachesmentioning

confidence: 99%

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Assessing the evolution of educational accessibility with self-avoiding random walk: insights from Helsinki

Aydin,

Yigitbasi,

Casali

et al. 2023

Appl Netw Sci

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Rapid urbanization has posed challenges to accessibility to critical services that require in-depth analysis. Complex networks theory has been used to evaluate the evolution of network topologies or the overall accessibility of transportation systems. However, topological metrics to explain the temporal changes in accessibility levels do not fully capture the dynamics and implications of accessibility to specific critical services. In this study, we address this gap and investigate the opportunities of using a self-avoiding random walk (SARW) algorithm to evaluate and explain the evolution of spatial accessibility to education facilities. We used hotspot analysis to understand the temporal changes and investigated changes in hot and cold spots over time. Furthermore, we explored the relationship between the network indicators and the SARW-based accessibility metric. We illustrated this method in a case study from Helsinki, where large-scale open data spanning from 1991 to 2016 is available. Our findings indicate that the SARW-based metric delivers more detailed node-level results than the traditional isochrone-based metric. The latter generates accessibility zones where accessibility is assumed to be uniform, while the SARW metric captures the dynamic nature of educational facility accessibility more accurately. The developed methodology helps to identify the impacts on the historical development of accessibility and can be applied to investigate accessibility to other critical services.

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Section: School Accessibility Factorsmentioning

confidence: 99%

Section: Complex Network Approachesmentioning

confidence: 99%

Assessing the evolution of educational accessibility with self-avoiding random walk: insights from Helsinki

Aydin,

Yigitbasi,

Casali

et al. 2023

Appl Netw Sci

Self Cite

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“…We used the Python Networkx package [39] to estimate the betweenness centrality of the road junctions in Knoxville. This metric has been applied in various domains, such as social network analysis, infrastructure protection, and network resilience assessments [40]- [42]. Nodes with high betweenness centrality control information flow and resource distribution within networks [37].…”

Section: Quantification Of Accessibility To Identify Key Building Nodesmentioning

confidence: 99%

Initial Systems-Level Assessment of a Distributed Direct Air Capture System Concept at the Urban-scale (UrbanDAC)

Kobayashi-Carvalhaes,

Ahmad

2023

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Direct Air Capture (DAC) systems offer a promising solution for mitigating global carbon emissions by directly removing ambient carbon dioxide (CO2) from the atmosphere. While future DAC facilities are typically envisioned as being large and centralized, small-scale systems present an alternative approach with advantages such as adaptability and lower uptake costs. By harnessing waste heat from the built environment, such small-scale systems become distributed DAC at the urban scale (UrbanDAC) that benefit from existing urban infrastructure, while presenting challenges such as identifying eligible buildings and sustainable transportation and storage of captured CO2. Collaborating with engineering experts and developers of a DAC unit that can be co-located with cooling towers of existing commercial buildings, this study explores the systems-level implications of UrbanDAC using a geographically explicit multi-decision criteria analysis (MCDA) framework. By considering various infrastructure and environmental factors, network analysis and geospatial techniques are applied to identify optimal building candidates for distributed DAC units within Knoxville, Tennessee, USA, as a representative mid-size city. The selected outputs of the MCDA are used to explore a scenario that assumes a CO2 collection and transport route for 20 high-ranking candidate buildings; total carbon emissions, EV energy consumption, and net carbon dioxide removal (CDR) are then calculated. Results suggest that the spatial variation of optimal candidates between thriving commercial areas is an important planning consideration. Examining the feasibility of UrbanDAC at an urban planning level provides valuable insights into the barriers and enabling conditions for CDR in cities, where the vast majority of CO2 emissions are produced, and supports decision-making processes for the implementation of decarbonization initiatives. Through this initial assessment, this research acts as a pilot study for an emerging technology that highlights the importance of distributed DAC technologies in addressing climate change and emphasizes the need for further research and exploration in this domain.

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“…Network science methods to tackle the assessment of route restoration strategies typically focus on identifying critical road segments with connectivity measures. The work in Aydin et al (2019) develops an origin-destination betweenness metric to evaluate post-disaster road performance to assist in decision-making during the recovery process. A different evaluation strategy is proposed in Zhou et al (2019) with a percolation-based connectivity model used to assess post-earthquake recovery of road networks.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust post-disaster route restoration

2020

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Route restoration is considered to be a task of foremost priority in disaster relief. In this paper, we propose a robust optimization approach for post-disaster route restoration under uncertain restoration times. We present a novel decision rule based on restoration time ordering that yields optimal restoration sequencing and propose conditions for complexity reduction in the model and prove probability bounds on the satisfaction of these conditions. We implement our models in a realistic study of the 2015 Gorkha earthquake in Nepal.

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Identifying Changes in Critical Locations for Transportation Networks Using Centrality

Cited by 9 publications

References 27 publications

Assessing the evolution of educational accessibility with self-avoiding random walk: insights from Helsinki

Assessing the evolution of educational accessibility with self-avoiding random walk: insights from Helsinki

Initial Systems-Level Assessment of a Distributed Direct Air Capture System Concept at the Urban-scale (UrbanDAC)

Robust post-disaster route restoration

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