- Al-Aamri, A. K., Hornby, G., Zhang, L.-C., Al-Maniri, A. A., & Padmadas, S. S. (2021). Mapping road traffic crash hotspots using GIS-based methods: A case study of Muscat Governorate in the Sultanate of Oman. Spatial Statistics, 42, 100458.
- Almasi, S. A., & Behnood, H. R. (2022). Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran. Accident Analysis & Prevention, 168, 106576.
- Almasi, S. A., Behnood, H. R., & Arvin, R. (2021). Pedestrian crash exposure analysis using alternative geographically weighted regression models. Journal of advanced transportation, 2021.
- Álvarez, P., Fernández, M. A., Gordaliza, A., Mansilla, A., & Molinero, A. (2020). Geometric road design factors affecting the risk of urban run-off crashes. A case-control study. PLoS one, 15(6), e0234564.
- Bao, J., Liu, P., & Ukkusuri, S. V. (2019). A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis & Prevention, 122, 239-254.
- Bao, J., Yang, Z., Zeng, W., & Shi, X. (2021). Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data. Journal of transport geography, 94, 103118.
- Barmoudeh, L., Baghishani, H., & Martino, S. (2022). Bayesian spatial analysis of crash severity data with the INLA approach: Assessment of different identification constraints. Accident Analysis & Prevention, 167, 106570.
- Chen, P., & Zhou, J. (2016). Effects of the built environment on automobile-involved pedestrian crash frequency and risk. Journal of Transport & Health, 3(4), 448-456.
- Chen, T., Sze, N., Chen, S., Labi, S., & Zeng, Q. (2021). 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, 154, 106089.
- Congiu, T., Sotgiu, G., Castiglia, P., Azara, A., Piana, A., Saderi, L., & Dettori, M. (2019). Built environment features and pedestrian accidents: An Italian retrospective study. Sustainability, 11(4), 1064.
- Da Silva, A. R., & Fotheringham, A. S. (2016). The multiple testing issue in geographically weighted regression. Geographical Analysis, 48(3), 233-247.
- Delclòs-Alió, X., & Miralles-Guasch, C. (2018). Looking at Barcelona through Jane Jacobs’s eyes: Mapping the basic conditions for urban vitality in a Mediterranean conurbation. Land Use Policy, 75, 505-517.
- Ewing, R., & Dumbaugh, E. (2009). The built environment and traffic safety: a review of empirical evidence. Journal of Planning Literature, 23(4), 347-367.
- Fuentes, L., Truffello, R., & Flores, M. (2022). Impact of Land Use Diversity on Daytime Social Segregation Patterns in Santiago de Chile. Buildings, 12(2), 149.
- Galgamuwa, U., Du, J., & Dissanayake, S. (2021). 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), 8(1), 95-106.
- Gschlößl, S., & Czado, C. (2008). Modelling count data with overdispersion and spatial effects. Statistical papers, 49(3), 531-552.
- Guo, F., & Lu, D. (2022). How many crashes does cellphone use contribute to? Population attributable risk of cellphone use while driving. Journal of safety research, 82, 385-391.
- Guo, Y., Li, Z., Liu, P., & Wu, Y. (2019). Exploring risk factors with crashes by collision type at freeway diverge areas: accounting for unobserved heterogeneity. IEEE Access, 7, 11809-11819.
- Hezaveh, A. M., Arvin, R., & Cherry, C. R. (2019). A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level. Accident Analysis & Prevention, 131, 15-24.
- Huang, H., Zeng, Q., Pei, X., Wong, S., & Xu, P. (2016). Predicting crash frequency using an optimised radial basis function neural network model. Transportmetrica A: transport science, 12(4), 330-345.
- Intini, P., Berloco, N., Fonzone, A., Fountas, G., & Ranieri, V. (2020). The influence of traffic, geometric and context variables on urban crash types: A grouped random parameter multinomial logit approach. Analytic methods in accident research, 28, 100141.
- Iyanda, A. E., Adeleke, R., Lu, Y., Osayomi, T., Adaralegbe, A., Lasode, M., Osundina, A. M. (2020). A retrospective cross-national examination of COVID-19 outbreak in 175 countries: a multiscale geographically weighted regression analysis (January 11-June 28, 2020). Journal of infection and public health, 13(10), 1438-1445.
- Kang, C.-D. (2018). The S+ 5Ds: Spatial access to pedestrian environments and walking in Seoul, Korea. Cities, 77, 130-141.
- Lee, J., Abdel-Aty, M., & Shah, I. (2019). Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling. Accident Analysis & Prevention, 130, 91-98.
- Liu, C., & Sharma, A. (2017). Exploring spatio-temporal effects in traffic crash trend analysis. Analytic methods in accident research, 16, 104-116.
- Liu, J., Hainen, A., Li, X., Nie, Q., & Nambisan, S. (2019). Pedestrian injury severity in motor vehicle crashes: an integrated spatio-temporal modeling approach. Accident Analysis & Prevention, 132, 105272.
- Moomen, M., Rezapour, M., Raja, M., & Ksaibati, K. (2020). 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), 94-102.
- Mukherjee, D., & Mitra, S. (2021). Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model. Accident Analysis & Prevention, 163, 106469.
- Mukherjee, D., Rao, K. R., & Tiwari, G. (2022). Built-environment risk assessment for pedestrians near bus-stops: a case study in Delhi. International journal of injury control and safety promotion, 1-10.
- Munira, S., Sener, I. N., & Dai, B. (2020). A Bayesian spatial Poisson-lognormal model to examine pedestrian crash severity at signalized intersections. Accident Analysis & Prevention, 144, 105679.
- Musa, I. J., & Moses, A. O. (2014). An analysis of the effect of land use on road traffic accidents in Zaria. International Journal of Development and Sustainability, 3(3), 520-529.
- Mutiso, F., Pearce, J. L., Benjamin-Neelon, S. E., Mueller, N. T., Li, H., & Neelon, B. (2022). Bayesian negative binomial regression with spatially varying dispersion: Modeling COVID-19 incidence in Georgia. Spatial Statistics, 100703.
- Nadifar, M., Baghishani, H., Fallah, A., & Rue, H. (2019). Statistical modeling of groundwater quality assessment in Iran using a flexible Poisson likelihood. arXiv preprint arXiv:1908.02344.
- Obelheiro, M. R., da Silva, A. R., Nodari, C. T., Cybis, H. B. B., & Lindau, L. A. (2020). A new zone system to analyze the spatial relationships between the built environment and traffic safety. Journal of transport geography, 84, 102699.
- Olsson, U. (2002). Generalized linear models: an applied approach: Lund, Studentlitteratur.
- Pal, C., Hirayama, S., Narahari, S., Jeyabharath, M., Prakash, G., & Kulothungan, V. (2018). An insight of World Health Organization (WHO) accident database by cluster analysis with self-organizing map (SOM). Traffic injury prevention, 19(sup1), S15-S20.
- Papa, E., Silva, C., Te Brömmelstroet, M., & Hull, A. (2016). Accessibility instruments for planning practice: A review of European experiences. Journal of transport and land use, 9(3), 57-75.
- Paul, M., & Ghosh, I. (2020). Influence of green signal countdown timer on severe crash types at signalized intersections due to red light violations. Transportation letters, 12(8), 528-539.
- Peera, K. M., Shekhawat, R. S., & Prasad, C. (2019). Traffic analysis zone level road traffic accident prediction models based on land use characteristics. International journal for traffic and transport engineering (Belgrade), 9(4), 376-386.
- Rahmani, M., Lotfata, A., Zebardast, E., Rastegar, S., Sanchez, T. W., Goharrizi, B. A., & Landi, S. (2022). Land use suitability assessment for economic development at the provincial level: The case study of Yazd Province, Iran. Sustainable Cities and Society, 87, 104163.
- Rashidi, M. H., Keshavarz, S., Pazari, P., Safahieh, N., & Samimi, A. (2022). Modeling the accuracy of traffic crash prediction models. IATSS Research.
- Saha, D., Alluri, P., Gan, A., & Wu, W. (2018). Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models. Accident Analysis & Prevention, 118, 166-177.
- Satria, R., Aguero-Valverde, J., & Castro, M. (2021). Spatial analysis of road crash frequency using Bayesian models with Integrated Nested Laplace Approximation (INLA). Journal of Transportation Safety & Security, 13(11), 1240-1262.
- Se, C., Champahom, T., Jomnonkwao, S., Chaimuang, P., & Ratanavaraha, V. (2021). Empirical comparison of the effects of urban and rural crashes on motorcyclist injury severities: A correlated random parameters ordered probit approach with heterogeneity in means. Accident Analysis & Prevention, 161, 106352.
- Sobreira, L. T. P., & Cunto, F. (2021). Disaggregated traffic conditions and road crashes in urban signalized intersections. Journal of safety research, 77, 202-211.
- Soroori, E., Mohammadzadeh Moghaddam, A., & Salehi, M. (2021). Modeling spatial nonstationary and overdispersed crash data: Development and comparative analysis of global and geographically weighted regression models applied to macrolevel injury crash data. Journal of Transportation Safety & Security, 13(9), 1000-1024.
- Stipancic, J., Miranda-Moreno, L., Saunier, N., & Labbe, A. (2018). Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models. Accident Analysis & Prevention, 120, 174-187.
- Stipancic, J., Miranda-Moreno, L., Saunier, N., & Labbe, A. (2019). Network screening for large urban road networks: using GPS data and surrogate measures to model crash frequency and severity. Accident Analysis & Prevention, 125, 290-301.
- Su, J., & Sze, N. (2022). Safety of walking trips accessing to public transportation: a Bayesian spatial model in Hong Kong. Travel behaviour and society, 29, 125-135.
- Su, J., Sze, N., & Bai, L. (2021). A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics. Accident Analysis & Prevention, 150, 105898.
- Sung, H., Lee, S., Cheon, S., & Yoon, J. (2022). Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes. Sustainability, 14(2), 646.
- Tagar, S., & Pulugurtha, S. S. (2021). Predictor variables influencing merging speed change lane crash risk by interchange type in urban areas. Transportation research interdisciplinary perspectives, 10, 100375.
- Tang, H., & Donnell, E. T. (2019). Application of a model-based recursive partitioning algorithm to predict crash frequency. Accident Analysis & Prevention, 132, 105274.
- Tang, J., Zhao, C., Liu, F., Hao, W., & Gao, F. (2022). Analyzing travel destinations distribution using large-scaled GPS trajectories: A spatio-temporal Log-Gaussian Cox process. Physica A: Statistical Mechanics and its Applications, 599, 127305.
- Tarko, A. P. (2018). Estimating the expected number of crashes with traffic conflicts and the Lomax Distribution–A theoretical and numerical exploration. Accident Analysis & Prevention, 113, 63-73.
- Tarko, A. P. (2023). Maximum likelihood method of estimating the conflict-crash relationship. Accident Analysis & Prevention, 179, 106875.
- Tiwari, G., & Jain, D. (2012). Accessibility and safety indicators for all road users: case study Delhi BRT. Journal of transport geography, 22, 87-95.
- Umair, M., Rana, I. A., & Lodhi, R. H. (2022). The impact of urban design and the built environment on road traffic crashes: a case study of Rawalpindi, Pakistan. Case studies on transport policy, 10(1), 417-426.
- Yan, X., Feng, Y., Tong, X., Li, P., Zhou, Y., Wu, P.,. Liu, S. (2021). Reducing spatial autocorrelation in the dynamic simulation of urban growth using eigenvector spatial filtering. International Journal of Applied Earth Observation and Geoinformation, 102, 102434.
- Yasmin, S., & Eluru, N. (2016). Latent segmentation based count models: analysis of bicycle safety in Montreal and Toronto. Accident Analysis & Prevention, 95, 157-171.
- Yoon, S. H., Kim, Y. W., & Ji, Y. G. (2019). The effects of takeover request modalities on highly automated car control transitions. Accident Analysis & Prevention, 123, 150-158.
- Zafri, N. M., & Khan, A. (2022). 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, 3(4), 312-324.
- Zafri, N. M., Prithul, A. A., Baral, I., & Rahman, M. (2020). Exploring the factors influencing pedestrian-vehicle crash severity in Dhaka, Bangladesh. International journal of injury control and safety promotion, 27(3), 300-307.
- Zarei, M., Hellinga, B., & Izadpanah, P. (2022). 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.
- Zhai, X., Huang, H., Gao, M., Dong, N., & Sze, N. (2018). Boundary crash data assignment in zonal safety analysis: an iterative approach based on data augmentation and Bayesian spatial model. Accident Analysis & Prevention, 121, 231-237.
- Zhou, Y., Jiang, X., Fu, C., Liu, H., & Zhang, G. (2022). Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks. Accident Analysis & Prevention, 174, 106756.
