| [1] |
Bunn TL, Liford M, Turner M, Bush A. 2022. Driver injuries in heavy vs. light and medium truck local crashes, 2010–2019. |
| [2] |
Yu C, Hua W, Yang C, Fang S, Li Y, et al. 2024. From sky to road: incorporating the satellite imagery into analysis of freight truck-related crash factors. |
| [3] |
McDonald N, Yuan Q, Naumann R. 2019. Urban freight and road safety in the era of e-commerce. |
| [4] |
Yang C, Chen M, Yuan Q. 2021. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: an exploratory analysis. |
| [5] |
Sexton D. 2008. Modifiable areal unit problem (MAUP). In Wiley StatsRef: Statistics Reference Online, eds Balakrishnan N, Colton T, Everitt B, Piegorsch W. Hoboken, NJ: Wiley. doi: 10.1002/9781118445112.stat03806 |
| [6] |
Briz-Redón Á, Martínez-Ruiz F, Montes F. 2019. Investigation of the consequences of the modifiable areal unit problem in macroscopic traffic safety analysis: a case study accounting for scale and zoning. |
| [7] |
Li C, Chen L. 2025. Exploring the impact of built environment on crash risks at transportation hubs. |
| [8] |
Parsa AB, Movahedi A, Taghipour H, Derrible S, Mohammadian AK. 2020. Toward safer highways: application of XGBoost and SHAP for real-time accident detection and feature analysis. |
| [9] |
Ziakopoulos A, Yannis G. 2020. A review of spatial approaches in road safety. |
| [10] |
Soliani RD, Argoud ARTT, Santiago F, Lopes AVB, Emekwuru N. 2024. Catastrophic causes of truck drivers' crashes on Brazilian highways: Mixed method analyses and crash prediction using machine learning. |
| [11] |
Haghani M, Behnood A, Dixit V, Oviedo-Trespalacios O. 2022. Road safety research in the context of low- and middle-income countries: macro-scale literature analyses, trends, knowledge gaps and challenges. |
| [12] |
U. S. Department of Transportation, Bureau of Transportation Statistics. 2023. National transportation statistics: Table 1-50 – U. S. ton-miles of freight by mode, 1960–2023. Washington, DC: BTS. Available at www.bts.gov |
| [13] |
Economic Commission for Latin America and the Caribbean (ECLAC). 2024. Freight transport and logistics statistics yearbook: Brazil profile 2024. United Nations-ECLAC, Santiago, Chile. Available at https://statistics.cepal.org/yearbook/2024/statistics.html?lang=en&theme=economic |
| [14] |
Lopes AS, Cavalcante CB, Vale DS, Loureiro CFG. 2020. Convergence of planning practices towards LUT integration: seeking evidences in a developing country. |
| [15] |
Pinheiro CDP, Gonzalez-Feliu J, Bertoncini BV. 2025. Addressing spatial heterogeneity and MAUP in urban transport geography: a multi-scale analysis of accessibility and warehouse location. |
| [16] |
Openshaw S, Taylor PJ. 1979. A million or so correlation coefficients: Three experiments on the modifiable areal unit problem. In Statistical Applications in Spatial Sciences, ed. Wrigley N. London: Pion. pp. 127–144 |
| [17] |
Reda AK, Tavasszy L, Gebresenbet G, Ljungberg D. 2023. Modelling the effect of spatial determinants on freight (trip) attraction: a spatially autoregressive geographically weighted regression approach. |
| [18] |
Zhai X, Sze NN, Lee JJ, Xu P, Huang H. 2025. Multi-scale approaches to cope with scale effect issues in macroscopic crash analysis. |
| [19] |
Xu P, Huang H, Dong N. 2018. The modifiable areal unit problem in traffic safety: basic issue, potential solutions and future research. |
| [20] |
Dong C, Clarke DB, Richards SH, Huang B. 2014. Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis. |
| [21] |
Yang C, Chen M, Yuan Q. 2021. The geography of freight-related accidents in the era of E-commerce: evidence from the Los Angeles metropolitan area. |
| [22] |
Zou W, Wang X, Zhang D. 2017. Truck crash severity in New York City: an investigation of the spatial and the time of day effects. |
| [23] |
Ahmed S, Hossain MA, Ray SK, Bhuiyan MMI, Sabuj SR. 2023. A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance. |
| [24] |
Yue H. 2024. Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning. |
| [25] |
Chang I, Park H, Hong E, Lee J, Kwon N. 2022. Predicting effects of built environment on fatal pedestrian accidents at location-specific level: application of XGBoost and SHAP. |
| [26] |
Scarano A, Sadeghi M, Mauriello F, Riccardi MR, Aghabayk K, et al. 2025. Cyclist crash severity modeling: a hybrid approach of XGBoost-SHAP and random parameters logit with heterogeneity in means and variances. |
| [27] |
Dong S, Khattak A, Ullah I, Zhou J, Hussain A. 2022. Predicting and analyzing road traffic injury severity using boosting-based ensemble learning models with SHAPley Additive exPlanations. |
| [28] |
Instituto Brasileiro de Geografia e Estatística. 2022. IBGE Cities database. Brasília: IBGE. Available at: https://cidades.ibge.gov.br/brasil/ce/Fortaleza/panorama |
| [29] |
Pinheiro CDP, Gonzalez-Feliu J, Bertoncini BV. 2025. A novel comprehensive spatial accessibility indicator to capture the latent nature of accessibility in logistic warehouses. |
| [30] |
Pinheiro CDP. 2024. Evaluating accessibility and transportation justice in urban freight transport: implementation, spatial dynamics and policy implications. PhD thesis. Universidade Federal do Ceará, Fortaleza. https://repositorio.ufc.br/handle/riufc/81139 |
| [31] |
Autarquia Municipal de Trânsito e Cidadania. 2025. Traffic incidents recorded in the municipality of Fortaleza, Brazil. Fortaleza: AMC. https://dados.fortaleza.ce.gov.br/organization/amc |
| [32] |
Ducret R, Lemarié B, Roset A. 2016. Cluster analysis and spatial modeling for urban freight. Identifying homogeneous urban zones based on urban form and logistics characteristics. |
| [33] |
Regal-Ludowieg A, Gonzalez-Feliu J, Rodríguez M. 2022. Delivery bay location and dimensioning for city logistics uses: an interactive modelling approach. In Production and Operations Management, eds Florez JV, de Brito R Junior, Leiras A, Alberto Paz Collado S, Alvarez MDG, et al. vol 391. Cham: Springer. pp. 475–481 doi: 10.1007/978-3-031-06862-1_35 |
| [34] |
Lambert D. 1992. Zero-inflated Poisson regression, with an application to defects in manufacturing. |
| [35] |
Chen T, Guestrin C. 2016. XGBoost: a scalable tree boosting system. |
| [36] |
Lundberg SM, Lee SI. 2017. A unified approach to interpreting model predictions. |
| [37] |
Shi Z, Wang Y, Guo D, Jiao F, Zhang H, Sun F. 2025. The Urban Intersection Accident Detection Method Based on the GAN-XGBoost and Shapley Additive Explanations Hybrid Model. |
| [38] |
Laphrom W, Se C, Champahom T, Jomnonkwao S, Wipulanusatd W, et al. 2024. XGBoost-SHAP and unobserved heterogeneity modelling of temporal multivehicle truck-involved crash severity patterns. |
| [39] |
Wang Y, Kockelman KM. 2013. A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods. |
| [40] |
Ukkusuri S, Miranda-Moreno LF, Ramadurai G, Isa-Tavarez J. 2012. The role of built environment on pedestrian crash frequency. |
| [41] |
Lord D, Washington S, Ivan JN. 2007. Further notes on the application of zero-inflated models in highway safety. |
| [42] |
DeSantis SM, Lazaridis C, Ji S, Spinale FG. 2014. Analyzing propensity matched zero-inflated count outcomes in observational studies. |
| [43] |
García J, Suárez MJ. 2023. The relevance of specification assumptions when analyzing the drivers of physical activity practice. |
| [44] |
Evans TG, Castellino F, Dobczyk MK, Tucker G, Walley AM, et al. 2024. Assessment of CD8+ T-cell mediated immunity in an influenza A(H3N2) human challenge model in Belgium: a randomized phase 2 study. |