Evaluating the Spatial Impacts of Environmental Factors on the Frequency of Urban Crashes using the Spatial Bayes Method based on Euclidean Distance and Contiguity

Document Type : Original Article


1 Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 . Assistant Professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

3 Faculty of Civil Engineering and Environment, Tarbiat Modares University,tehran,iran



The occurrence of urban accidents depends on many human and environmental factors, so identifying the important factors influencing accidents and their spatial effects on each other is of great importance. The main goal of this study is to evaluate the spatial effects of environmental factors on the frequency of accidents in the city of Shiraz, Iran at the TAZ level. In the first step of the study, using component analysis models, important environmental factors affecting the accident were identified and composite indicators were produced as independent variables. In the second step, in order to control the effect of correlation and heterogeneity of model variables, spatial statistical models based on Euclidean distance such as geographically weighted Poisson regression (GWPR), geographically weighted negative binomial distribution (GWNBR) as well as Poisson and distribution models Negative binomial based on neighbor distance is used in spatial Bayes method with INLA approach. The results of the study showed that models based on distance and contiguity in order to evaluate the spatial effects of accident data and the factors affecting it at the TAZ level have higher accuracy than geographic weighted regression models, as well as indicators of land use diversity and access to The public transportation system is effective and in TAZs where this index is high, there is a higher probability of an accident.


  1. M. Zafri & A. Khan, “A spatial regression modeling framework for examining relationships between the built environment and pedestrian crash occurrences at macroscopic level: a study in a developing country context,” Geography and sustainability, vol. 3, no. 4, pp.312-324, (2022).
  2. Umair, I.A. Rana & R.H Lodhi, “The impact of urban design and the built environment on road traffic crashes: a case study of Rawalpindi, Pakistan,” Case studies on transport policy, vol. 10, no. 1, pp.417-426, (2022).
  3. Chen & J. Zhou, “Effects of the built environment on automobile-involved pedestrian crash frequency and risk,” Journal of Transport & Health, vol. 3, no. 4, pp. 448-456, (2016).
  4. M. Zafri, A.A. Prithul, I. Baral & M. Rahman, “Exploring the factors influencing pedestrian-vehicle crash severity in Dhaka, Bangladesh,” International journal of injury control and safety promotion, vol. 27, no. 3, pp. 300-307, (2020).
  5. A Almasi & H.R. Behnood, “Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran,” Accident Analysis & Prevention, pp. 168-179, Apr 4-11, (2022).
  6. Fuentes, R. Truffello & M. Flores, “Impact of Land Use Diversity on Daytime Social Segregation Patterns in Santiago de Chile,” Buildings, vol. 12, no. 2, pp.149, (2022).
  7. D. Kang , “The S+ 5Ds: Spatial access to pedestrian environments and walking in Seoul, Korea,” Cities, vol. 77, no. 3, pp. 130-141, (2018).
  8. Lake & L. Ferreira, “Towards a methodology to evaluate public transport projects”, 2002.
  9. H. Yoon, Y.W. Kim & Y.G. Ji, “The effects of takeover request modalities on highly automated car control transitions,” Accident Analysis & Prevention, vol. 123, no. 1, pp. 150-158, (2019).
  10. Chen, N. Sze, S. Chen, S. Labi & Q. Zeng, “Analysing the main and interaction effects of commercial vehicle mix and roadway attributes on crash rates using a Bayesian random-parameter Tobit model,” Accident Analysis & Prevention, vol. 154, no. 1, pp. 8-15,(2021).
  11. P. Tarko, “Maximum likelihood method of estimating the conflict-crash relationship,” Accident Analysis & Prevention, vol. 179, no. 1, pp. 12-19, (2023).
  12. H. Rashidi, S. Keshavarz, P. Pazari, N. Safahieh & A. Samimi, “Modeling the accuracy of traffic crash prediction models,” IATSS Research, vol. 46, no. 3, pp. 345-352, (2022).
  13. Moomen, M. Rezapour, M. Raja & K. Ksaibati, “Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming,” IATSS Research, 44(2), vol. 44, no. 2, pp. 94-102, (2020).
  14. Tang & E.T. Donnell, “Application of a model-based recursive partitioning algorithm to predict crash frequency,” Accident Analysis & Prevention, vol. 132, no. 1, pp. 105-116, (2019).
  15. Huang, Q. Zeng, X. Pei, S. Wong & P. Xu, “Predicting crash frequency using an optimised radial basis function neural network model,” Transportmetrica A: transport science, vol. 12, no. 2, pp. 330-345, (2016).
  16. Zarei, B. Hellinga & P. Izadpanah, “CGAN-EB: A non-parametric empirical Bayes method for crash frequency modeling using conditional generative adversarial networks as safety performance functions,” International Journal of Transportation Science and Technology, vol. 1, no. 1, pp. 35-46, (2022).
  17. A. Almasi, H.R. Behnood & R. Arvin, “Pedestrian crash exposure analysis using alternative geographically weighted regression models,” Journal of advanced transportation, vol. 2021, no. 1, pp. 50-63, (2021).
  18. K. Al-Aamri, G. Hornby, L.C. Zhang, A.A. Al-Maniri & S.S. Padmadas, “Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman,” Spatial Statistics, 42, vol. 42, no. 1, pp. 78-85, (2021).
  19. Liu, A. Hainen, X. Li, Q. Nie & S. Nambisan, “Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach,” Accident Analysis & Prevention, vol. 132, no. 3, pp. 105-117, (2019).
  20. E. Iyanda, R. Adeleke, Y. Lu, T. Osayomi, A. Adaralegbe, M. Lasode & A. M. Osundina, “A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis,” Journal of infection and public health, vol. 13, no. 10, pp.1438-1445, (2020).
  21. Zhou, X. Jiang, C. Fu, H. Liu & G. Zhang, “Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks,” Accident Analysis & Prevention, vol. 174, no. 3, pp. 103-110, (2022).
  22. Galgamuwa, J. Du & S. Dissanayake, “Bayesian spatial modeling to incorporate unmeasured information at road segment levels with the INLA approach: A methodological advancement of estimating crash modification factors,” Journal of traffic and transportation engineering (English edition), vol. 8, no. 1, pp.95-106, (2021).
  23. Saha, P. Alluri, A. Gan & W. Wu, “Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models,” Accident Analysis & Prevention, vol. 118, no. 1, pp. 166-177, (2018).
  24. M. Peera, R.S. Shekhawat & C. Prasad, “Traffic analysis zone level road traffic accident prediction models based on land use characteristics,” International journal for traffic and transport engineering (Belgrade), vol. 9, no. 4, pp. 376-386, (2019).
  25. Sung, S. Lee, S. Cheon & J. Yoon, “Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes,” Sustainability, vol. 14, no. 2, pp.646, (2022).
  26. Guo & D. Lu, “How many crashes does cellphone use contribute to? Population attributable risk of cellphone use while driving,” Journal of safety research, vol. 82, no. 4, pp. 385-391, (2022).
  27. Rahmani, A. Lotfata, E. Zebardast, S. Rastegar, T.W. Sanchez, B.A. Goharrizi & S. Landi, “Land use suitability assessment for economic development at the provincial level: The case study of Yazd Province, Iran,” Sustainable Cities and Society, vol. 87, no. 1, pp. 104-112, (2022).
  28. Delclòs-Alió & C. Miralles-Guasch, “Looking at Barcelona through Jane Jacobs’s eyes: Mapping the basic conditions for urban vitality in a Mediterranean conurbation,” Land Use Policy, vol. 75, no. 1, pp. 505-517, (2018).
  29. Olsson, “Generalized linear models: an applied approach: Lund,” Student litteratur,) 2002(.
  30. R. da Silva & A.S. Fotheringham, “The multiple testing issue in geographically weighted regression,” Geographical Analysis, 48(3), vol. 48, no. 3, pp.233-247, (2016).
  31. Barmoudeh, H. Baghishani & S. Martino, “Bayesian spatial analysis of crash severity data with the INLA approach: Assessment of different identification constraints,” Accident Analysis & Prevention, vol. 167, no. 1, pp. 106-118, (2022).
  32. Mukherjee, K.R. Rao & G. Tiwari, “Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi,” International journal of injury control and safety promotion, vol. 30, no. 2, pp.185-194, (2022).
  33. Su, N. Sze & L. Bai, “A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics,” Accident Analysis & Prevention, vol. 150, no. 1, pp. 105-113, (2021).
  34. Rojas-Rueda, M.J. Nieuwenhuijsen, H. Khreis & H. Frumkin, “Autonomous vehicles and public health,” Annual review of public health, vol. 41, no. 1, pp.329-345, (2020).
  35. Stipancic, L. Miranda-Moreno, N. Saunier & A. Labbe, “Network screening for large urban road networks: using GPS data and surrogate measures to model crash frequency and severity,” Accident Analysis & Prevention, vol. 125, no. 2, pp. 290-301, (2019).
  36. Álvarez, M. A. Fernández, A. Gordaliza, A. Mansilla & A. Molinero, “Geometric road design factors affecting the risk of urban run-off crashes,” A case-control study. PLoS one, vol. 15, no. 6, pp.50-64, (2020).
  37. Lee, M. Abdel-Aty & I. Shah, “Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling,” Accident Analysis & Prevention, vol. 130, no. 1, pp. 91-98, (2019).