Spatial Association Between Urban Neighbourhood Characteristics ‎‎and ‎‎‎Child Pedestrian–Motor Vehicle Collision‎s

Soroori, Emad; Kiani, Behzad; Ghasemi, Soraya; Mohammadi, Alireza; Shabanikiya, Hamidreza; Bergquist, Robert; Kiani, Farhoush; Tabatabaei‐Jafari, Hossein

doi:10.1007/s12061-023-09519-w

Cited by 3 publications

(1 citation statement)

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“…Street intersections [16], the density of two-lane roads [17,18], the massive number of vehicles [19] travelling at excessive speeds due to ineffective speed limits [20], the lack of clear crossing routes for pedestrians and the pavement dysconnectivity are all important variables with respect to accidents [21][22][23]. Finally, socioeconomic factors such as population density, youth density, and residential areas are associated with the number of PTAs in each neighbourhood [14,[24][25][26].…”

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confidence: 99%

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

et al. 2023

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This study utilised multi-year data from 5354 incidents to predict pedestrian–road traffic accidents (PTAs) based on twelve socioeconomic and built-environment factors. The research employed the logistic regression model (LRM) and the fuzzy-analytical hierarchy process (FAHP) techniques to evaluate and assign weights to each factor. The susceptibility map for PTAs is generated using the “Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)”. Subsequently, the probability of accidents in 2020 was predicted using real multi-year accident data and the Markov chain (MC) and cellular automata Markov chain (CA-MC) models, with the prediction accuracy assessed using the Kappa index. Building upon promising results, the model was extrapolated to forecast the probability of accidents in 2023. The findings of the LRM demonstrated the significance of the selected variables as predictors of accident likelihood. The prediction approaches identified areas prone to high-risk accidents. Additionally, the Kappa for no information (KNO) statistical value was calculated for both the MC and CA-MC models, which yielded values of 0.94 and 0.88, respectively, signifying a high level of accuracy. The proposed methodology is generalizable, and the identification of high-risk locations can aid urban planners in devising appropriate preventive measures.

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Section: Introductionmentioning

confidence: 99%

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

et al. 2023

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Associations between streetscape characteristics at Chinese adolescents’ activity places and active travel patterns on weekdays and weekends

Wang

Liu

Yao

et al. 2023

Journal of Transport & Health

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Mastering geographically weighted regression: key considerations for building a robust model

Kiani,

Sartorius,

Lau

et al. 2024

Geospat Health

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Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...]

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Spatial Association Between Urban Neighbourhood Characteristics ‎‎and ‎‎‎Child Pedestrian–Motor Vehicle Collision‎s

Cited by 3 publications

References 40 publications

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

Pedestrian Road Traffic Accidents in Metropolitan Areas: GIS-Based Prediction Modelling of Cases in Mashhad, Iran

Associations between streetscape characteristics at Chinese adolescents’ activity places and active travel patterns on weekdays and weekends

Mastering geographically weighted regression: key considerations for building a robust model

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