Nível de estresse de ciclistas e geoprocessamento de dados abertos combinados para a definição de redes cicloviárias em cidades de pequeno porte

Monari, Marcelo

doi:10.11606/t.18.2022.tde-05092022-145612

Cited by 1 publication

(2 citation statements)

References 28 publications

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“…In this context, the results of the case study suggest some strengths regarding policy implications arising from the application of the proposed method, as it is easy to apply and benefits only from open data and free software. Furthermore, while appearing to have no immediate practical effects, one-off cycling projects, such as providing isolated infrastructure in locations that would benefit from better LTS classifications, can gradually evolve to "low-stress cycling networks" (Moran et al, 2018;Monari, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…Distance or travel time are decisive factors in cyclists' route choice (Menghini et al, 2010). Identifying the shortest path between an origin-destination pair is a process that benefits from Dijkstra's (1959) algorithm (based on graph theory), which has often been applied to GIS-assisted cycling planning to identify routes that minimize the sum of impedances (a term used in technical literature that refers to the "resistance" imposed by network links to cycling) associated with the BCI (Klobucar and Fricker, 2007), the BLOS (Lowry et al, 2012) or the LTS (Monari, 2022).…”

Section: Cycling Routesmentioning

confidence: 99%

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Comparing distance-based and stress-based centralities to rank priority locations for cycling infrastructure investments in small-sized cities

Monari,

Segantine,

Rodrigues da Silva

et al. 2024

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The lack of technical guidelines to define investment priority locations is one of the barriers to cycling in emerging countries, limiting the preparation of urban mobility plans even when legally required. The objective of this paper is to propose and compare two approaches, with and without considering the cyclists’ perception of stress (assessed with the LTS, or Level of Traffic Stress), to determine the relative importance of road segments in the network and to rank priority locations for investments in cycling infrastructure. A case study was conducted in the city of Bariri (Brazil), for which the overall contribution of each network link to the identified cycling routes was mapped and ranked according to both criteria. The spatial distribution of differences between homologous ranks (i.e., ranks of the same network link according to different criteria) was also mapped, and the spatial autocorrelation between these differences was assessed by the Local Moran’s Index, allowing the identification of road segments of greater similarity and dissimilarity between the proposed approaches for resource allocation.

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Section: Discussionmentioning

confidence: 99%