Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation. (English) Zbl 07778558
Summary: Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network. A Bayesian methodology using Integrated nested Laplace approximations with stochastic partial differential equations (SPDE) has been applied in the modeling process. As a novelty, we have introduced SPDE network triangulation to estimate the spatial autocorrelation restricted to the linear network. The resulting risk maps provide information to identify safe routes between source and destination points, and can be useful for accident prevention and multi-disciplinary road safety measures.
MSC:
| 62-XX | Statistics |
References:
| [1] | Abdel-Salam, A., Guo, F., Flintsch, A., Arafeh, M., and Rakha, H., Linear regression crash prediction models, Efficient Transportation and Pavement Systems, CRC Press, 2008. |
| [2] | Adarabioyo, M. I.; Ipinyomi, R. A., Comparing zero-inflated poisson, zero-inflated negative binomial and zero-inflated geometric in count data with excess zero, Asian J. Probab. Stat., 4, 1-10 (2019) |
| [3] | Aghajani, M. A.; Dezfoulian, R. S.; Arjroody, A. R.; Rezaei, M., Applying GIS to identify the spatial and temporal patterns of road accidents using spatial statistics (case study: Ilam Province, Iran), Transp. Res. Procedia, 25, 2126-2138 (2017) |
| [4] | Appelhans, T., Detsch, F., Reudenbach, C., and Woellauer, S., mapview - Interactive viewing of spatial data in R, EGU General Assembly Conference Abstracts, 2016. Article EPSC2016-1832, EPSC2016-1832. |
| [5] | Ashraf, I.; Hur, S.; Shafiq, M.; Park, Y.; Guo, Y., Catastrophic factors involved in road accidents: Underlying causes and descriptive analysis (Y. Guo, Ed.), PLoS. ONE., 14, e0223473 (2019) |
| [6] | Azuike, E.; Okafor, K.; Okojie, P., The causes and prevalence of road traffic accidents amongst commercial long distance drivers in Benin city, Edo state, Nigeria, Nig. Hosp. Pract., 26, 220-230 (2017) |
| [7] | Bakka, H.; Rue, H.; Fuglstad, G.; Riebler, A.; Bolin, D.; Illian, J.; Krainski, E.; Simpson, D.; Lindgren, F., Spatial modeling with R-INLA: A review, WIREs Comput. Stat., 10, e1443 (2018) · Zbl 07910834 |
| [8] | Bhawkar, A., Severe traffic accidents in United Kingdom, Tech. rep., National College of Ireland, 2018. Available at https://www.researchgate.net/publication/330676135_SevereTraffic_Accidents_in_United_Kingdom. |
| [9] | Blangiardo, M.; Cameletti, M., Spatial and Spatio-temporal Bayesian Models with R-INLA (2015), John Wiley & Sons, Ltd: John Wiley & Sons, Ltd, London · Zbl 1318.62001 |
| [10] | Briz-Redón, Á.; Martínez-Ruiz, F.; Montes, F., Identification of differential risk hotspots for collision and vehicle type in a directed linear network, Accid. Anal. Preven., 132 (2019) |
| [11] | Cameletti, M.; Lindgren, F.; Simpson, D.; Rue, H., Spatio-temporal modeling of particulate matter concentration through the SPDE approach, AStA Adv. Stat. Anal., 97, 109-131 (2013) · Zbl 1443.62401 |
| [12] | Cantillo, V.; Garcés, P.; Márquez, L., Factors influencing the occurrence of traffic accidents in urban roads: A combined GIS-Empirical Bayesian approach, DYNA, 83, 21-28 (2016) |
| [13] | Castro, M.; Paleti, R.; Bhat, C. R., A latent variable representation of count data models to accommodate spatial and temporal dependence: Application to predicting crash frequency at intersections, Transp. Res. Part B: Methodol., 46, 253-272 (2012) |
| [14] | Chaudhuri, S.; Moradi, M.; Mateu, J., On the trend detection of time-ordered intensity images of point processes on linear networks, Commun. Stat. - Simul. Comput., 1-13 (2021) · Zbl 07713865 · doi:10.1080/03610918.2021.1881116 |
| [15] | Curiel, R. P.; Ramírez, H. G.; Bishop, S. R., A novel rare event approach to measure the randomness and concentration of road accidents (Y. Deng, Ed.), PLoS. ONE., 13, e0201890 (2018) |
| [16] | Curran-Everett, D., Explorations in statistics: The analysis of ratios and normalized data, Adv. Physiol. Educ., 37, 213-219 (2013) |
| [17] | de Fortuny, E. J.; Martens, D.; Provost, F., Predictive modeling with big data: Is bigger really better?, Big. Data., 1, 215-226 (2013) |
| [18] | Demasi, F.; Loprencipe, G.; Moretti, L., Road safety analysis of urban roads: Case study of an Italian municipality, Safety, 4, 58 (2018) |
| [19] | Deublein, M.; Schubert, M.; Adey, B. T.; Köhler, J.; Faber, M. H., Prediction of road accidents: A Bayesian hierarchical approach, Accid. Anal. Preven., 51, 274-291 (2013) |
| [20] | Farmer, C. M., Temporal factors in motor vehicle crash deaths, Inj. Prev., 11, 18-23 (2005) |
| [21] | Galgamuwa, U., Bayesian spatial modeling to incorporate unmeasured information at road segment levels with the INLA approach: A methodological advancement of estimating crash modification factors, J. Traff. Transp. Eng. (English Edition), 2019. |
| [22] | Gelman, A., Bayesian data analysis, 2014. [OCLC: 909477393]. · Zbl 1332.62426 |
| [23] | Guo, Y.; Osama, A.; Sayed, T., A cross-comparison of different techniques for modeling macro-level cyclist crashes, Accid. Anal. Preven., 113, 38-46 (2018) |
| [24] | Hu, M.-C.; Pavlicova, M.; Nunes, E. V., Zero-Inflated and hurdle models of count data with extra zeros: Examples from an HIV-Risk reduction intervention trial, Am. J. Drug. Alcohol. Abuse., 37, 367-375 (2011) |
| [25] | Huang, J.; Malone, B. P.; Minasny, B.; McBratney, A. B.; Triantafilis, J., Evaluating a Bayesian modelling approach (INLA-SPDE) for environmental mapping, Sci. Total Environ., 609, 621-632 (2017) |
| [26] | Jegede, F. J., Spatio-temporal analysis of road traffic accidents in Oyo state, Nigeria, Accid. Anal. Prev., 20, 227-243 (1988) |
| [27] | Juan, P.; Mateu, J.; Saez, M., Pinpointing spatio-temporal interactions in wildfire patterns, Stoch. Environ. Res. Risk. Assess., 26, 1131-1150 (2012) |
| [28] | Karacasu, M.; Ergül, B.; Yavuz, A. A., Limited. estimating the causes of traffic accidents using logistic regression and discriminant analysis, Int. J. Inj. Contr. Saf. Promot., 21, 305-313 (2013) |
| [29] | Karaganis, A., A Spatial Point Process for Estimating the Probability of Occurrence of a Traffic Accident, European Regional Science Association, ERSA conference papers, 2006. |
| [30] | Khulbe, D., Modeling Severe Traffic Accidents With Spatial And Temporal Features, ICONIP, 2019. |
| [31] | Lambert, D., Zero-Inflated poisson regression, with an application to defects in manufacturing, Technometrics, 34, 1-14 (1992) · Zbl 0850.62756 |
| [32] | Lindgren, F.; Rue, H.; Lindström, J., An explicit link between Gaussian fields and Gaussian Markov random fields: The stochastic partial differential equation approach, J. R. Stat. Soc.: Ser. B (Stat. Methodol.), 73, 423-498 (2011) · Zbl 1274.62360 |
| [33] | Liu, C., Exploring spatio-temporal effects in traffic crash trend analysis, Anal. Meth. Accid. Res., 16, 104-116 (2017) |
| [34] | Liu, C.; Zhang, S.; Wu, H.; Fu, Q., A dynamic spatiotemporal analysis model for traffic incident influence prediction on urban road networks, ISPRS Int. J. Geo-Inform., 6, 362 (2017) |
| [35] | Loo, B.P.Y., Yao, S., and Wu, J., Spatial point analysis of road crashes in Shanghai: A GIS-based network kernel density method, 2011 19th International Conference on Geoinformatics, 2011. |
| [36] | Lopez-Quilez, A. and Munoz, F., Review of spatio-temporal models for disease mapping, Tech. rep., 2009. Available at https://www.uv.es/famarmu/doc/Euroheis2-report.pdf. · Zbl 1185.86027 |
| [37] | Lord, D.; Persaud, B. N., Accident prediction models with and without trend: Application of the generalized estimating equations procedure, Transp. Res. Rec.: J. Transp. Res. Board, 1717, 102-108 (2000) |
| [38] | Lovelace, R.; Ellison, R., stplanr: A package for transport planning, The R Journal, 10, 7-23 (2018) |
| [39] | Martínez-Minaya, J., Lindgren, F., López-Quílez, A., Simpson, D., and Conesa, D., The Integrated nested Laplace approximation for fitting models with multivariate response, 2021. |
| [40] | Martino, S. and Rue, H., Case studies in Bayesian computation using INLA, in Complex Data Modeling and Computationally Intensive Statistical Methods, P. Mantovan, P. Secchi, eds., Springer, Milan, 2010. pp. 99-114. |
| [41] | Martins, T. G.; Simpson, D.; Lindgren, F.; Rue, H., Bayesian computing with INLA: New features, Comput. Stat. Data Anal., 67, 68-83 (2013) · Zbl 1471.62135 |
| [42] | Miaou, S.-P., The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions, Accid. Anal. Preven., 26, 471-482 (1994) |
| [43] | Miler, M.; Todić, F.; Sevrović, M., Extracting accurate location information from a highly inaccurate traffic accident dataset: A methodology based on a string matching technique, Transp. Res. Part C: Emerg. Technol., 68, 185-193 (2016) |
| [44] | Mohanty, M.; Gupta, A., Factors affecting road crash modeling, J. Transp. Lit., 9, 15-19 (2015) |
| [45] | Moradi, M.M., Spatial and spatio-temporal point patterns on linear networks, Doctoral diss., Universitat Jaume I, 2018. |
| [46] | Moradi, M. M.; Mateu, J., First- and second-order characteristics of spatio-temporal point processes on linear networks, J. Comput. Graph. Stat., 0, 1-21 (2019) |
| [47] | Moraga, P., Geospatial Health Data : Modeling and Visualization with R-INLA and Shiny (2020), CRC Press: CRC Press, London |
| [48] | NCHRP, Quality and Accuracy of Positional Data in Transportation, Transportation Research Board, 2003. |
| [49] | Oh, J.; Washington, S. P.; Nam, D., Accident prediction model for railway-highway interfaces, Accid. Anal. Preven., 38, 346-356 (2006) |
| [50] | Oo, M. C.M.; Thein, T., An efficient predictive analytics system for high dimensional big data, J. King Saud University - Comput. Inform. Sci., 34, 1521-1532 (2019) |
| [51] | Padgham, M.; Rudis, B.; Lovelace, R.; Salmon, M., OSM Data, The J. Open Source Softw., 2, 305 (2017) |
| [52] | Prasannakumar, V.; Vijith, H.; Charutha, R.; Geetha, N., Spatio-Temporal clustering of road accidents: GIS based analysis and assessment, Procedia - Soc. Behav. Sci., 21, 317-325 (2011) |
| [53] | Pulugurtha, S. S.; Sambhara, V. R., Pedestrian crash estimation models for signalized intersections, Accid. Anal. Preven., 43, 439-446 (2011) |
| [54] | Risk mapping for the TEN-T in Croatia, Greece, Italy and Spain: Update. EuroRAP 2016. Available at https://eurorap.org/risk-mapping-for-the-ten-t-in-croatia-greeceitaly-and-spain-update/. |
| [55] | Rose, C. E.; Martin, S. W.; Wannemuehler, K. A.; Plikaytis, B. D., On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data, J. Biopharm. Stat., 16, 463-481 (2006) |
| [56] | Rue, H.; Held, L., Gaussian Markov Random Fields: Theory and Applications (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) (2005), Chapman & Hall/CRC: Chapman & Hall/CRC, London · Zbl 1093.60003 |
| [57] | Rue, H.; Martino, S.; Chopin, N., Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, J. R. Stat. Soc.: Ser. B (Stat. Methodol.), 71, 319-392 (2009) · Zbl 1248.62156 |
| [58] | Rue, H., Riebler, A., Sørbye, S.H., Illian, J.B., Simpson, D.P., and Lindgren, F.K., Bayesian Computing with INLA: A Review, 2016. |
| [59] | Rue, H.; Riebler, A.; Sørbye, S. H.; Illian, J. B.; Simpson, D. P.; Lindgren, F. K., Bayesian computing with INLA: A review, Annu. Rev. Stat. Appl., 4, 395-421 (2017) |
| [60] | Salifu, M., Accident prediction models for unsignalised urban junctions in Ghana, IATSS Res., 28, 68-81 (2004) |
| [61] | Serra, L.; Saez, M.; Juan, P.; Varga, D.; Mateu, J., A spatio-temporal Poisson hurdle point process to model wildfires, Stoch. Environ. Res. Risk. Assess., 28, 1671-1684 (2013) · doi:10.1007/s00477-013-0823-x |
| [62] | Shafabakhsh, G. A.; Famili, A.; Bahadori, M. S., GIS-based spatial analysis of urban traffic accidents: Case study in Mashhad, Iran, J. Traffic Transp. Eng. (English Edition), 4, 290-299 (2017) |
| [63] | Shahid, S.; Minhans, A.; Che Puan, O.; Hasan, S. A.; Ismail, T., Spatial and temporal pattern of road accidents and casualties in peninsular Malaysia, J. Teknol., 76, 000 (2015) |
| [64] | Simpson, D.P., Rue, H., Riebler, A., Martins, T.G., and Sørbye, S.H., Penalising model component complexity: A principled, practical approach to constructing priors, preprint (2014), arXiv doi: . · Zbl 1442.62060 |
| [65] | Simpson, D.; Rue, H.; Riebler, A.; Martins, T. G.; Sørbye, S. H., Penalising model component complexity: A principled, practical approach to constructing priors, Stat. Sci., 32, 1-28 (2017) · Zbl 1442.62060 · doi:10.1214/16-sts576 |
| [66] | Smedt, T. D., Comparing MCMC and INLA for disease mapping with Bayesian hierarchical models, Arch. Public Health, 73 (2015) · doi:10.1186/2049-3258-73-s1-o2 |
| [67] | Spiegelhalter, D. J.; Best, N. G.; Carlin, B. P.; van der Linde, A., Bayesian measures of model complexity and fit, J. R. Stat. Soc.: Ser. B (Stat. Methodol.), 64, 583-639 (2002) · Zbl 1067.62010 |
| [68] | Taylor, B. M.; Diggle, P. J., INLA or MCMC? A tutorial and comparative evaluation for spatial prediction in log-Gaussian Cox processes, J. Stat. Comput. Simul., 84, 2266-2284 (2014) · Zbl 1453.62214 |
| [69] | TFL, Transport for London: Road Safety, 2019. Retrieved April 30, 2019, Available at https://tfl.gov.uk/corporate/publications-and-reports/road-safety. |
| [70] | Verdoy, P. J., Enhancing the SPDE modeling of spatial point processes with INLA, applied to wildfires. choosing the best mesh for each database, Commun. Stat. - Simul. Comput., 50, 2990-3030 (2021) · Zbl 1497.62300 |
| [71] | Wang, C.; Quddus, M. A.; Ison, S. G., Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England, Accid. Anal. Prev., 41, 798-808 (2009) |
| [72] | Wang, W.; Yuan, Z.; Yang, Y.; Yang, X.; Liu, Y.; Guo, Y., Factors influencing traffic accident frequencies on urban roads: A spatial panel time-fixed effects error model (Y. Guo, Ed.), PLoS. ONE., 14, e0214539 (2019) |
| [73] | Watanabe, S., Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory, J. Mach. Learn. Res., 11, 3571-3594 (2010) · Zbl 1242.62024 |
| [74] | WHO, Global Status Report on Road Safety 2018, World Health Organization, (2019, Jan 10). |
| [75] | Wikle, C. K.; Berliner, L. M.; Cressie, N., Hierarchical Bayesian space-time models, Environ. Ecol. Stat., 5, 117-154 (1998) |
| [76] | Williamson, A. M.; Feyer, A.-M., Causes of accidents and the time of day, Work & Stress, 9, 158-164 (1995) |
| [77] | Xu, P.; Huang, H., Modeling crash spatial heterogeneity: Random parameter versus geographically weighting, Accid. Anal. Prev., 75, 16-25 (2015) |
| [78] | Zhong-xiang, F.; Shi-sheng, L.; Wei-hua, Z.; Nan-nan, Z., Combined prediction model of death toll for road traffic accidents based on independent and dependent variables, Comput. Intell. Neurosci., 2014, 1-7 (2014) |
| [79] | Zuur, A. F., Beginners Guide to Spatial, Temporal and Spatial-Temporal Ecological Data Analysis with R-INLA: Using GLM and GLMM (Vol. 1) (2017), Highland Statistics Ltd: Highland Statistics Ltd, Scotland |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
