Multivariate hierarchical analysis of car crashes data considering a spatial network lattice.

Road traffic casualties represent a hidden global epidemic, demanding evidence-based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5862 crashes of different severities were recorded over an eight-year period (2011-2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects, to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the North of the city. We analyse the Modifiable Areal Unit Problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify hotspots on the road network and to inform effective local interventions.

[1]  Francisco Palmi-Perales,et al.  Bayesian Multivariate Spatial Models for Lattice Data with INLA , 2021, J. Stat. Softw..

[2]  Leonhard Held,et al.  Posterior and Cross-validatory Predictive Checks: A Comparison of MCMC and INLA , 2010 .

[3]  D J Spiegelhalter,et al.  Approximate cross‐validatory predictive checks in disease mapping models , 2003, Statistics in medicine.

[4]  Murray Mackay.  Traffic accidents—a modern epidemic , 1972 .

[5]  K. Mardia.  Multi-dimensional multivariate Gaussian Markov random fields with application to image processing , 1988 .

[6]  R. Dubin.  Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms , 1988 .

[7]  Robert Haining,et al.  Statistics for spatial data: by Noel Cressie, 1991, John Wiley & Sons, New York, 900 p., ISBN 0-471-84336-9, US $89.95 , 1993 .

[8]  Tilman M. Davies,et al.  Fast Kernel Smoothing of Point Patterns on a Large Network using Two‐dimensional Convolution , 2019, International Statistical Review.

[9]  Haavard Rue,et al.  Bayesian Computing with INLA: A Review , 2016, 1604.00860.

[10]  I. Thomas.  Spatial data aggregation: exploratory analysis of road accidents. , 1996, Accident; analysis and prevention.

[11]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[12]  Satish V. Ukkusuri,et al.  The role of built environment on pedestrian crash frequency , 2012 .

[13]  V. Gómez‐Rubio.  Bayesian Inference with INLA , 2020 .

[14]  Shaw-Pin Miaou,et al.  Bayesian ranking of sites for engineering safety improvements: decision parameter, treatability concept, statistical criterion, and spatial dependence. , 2005, Accident; analysis and prevention.

[15]  Finn Lindgren,et al.  Bayesian Spatial Modelling with R-INLA , 2015 .

[16]  Xuesong Wang,et al.  Macro-level safety analysis of pedestrian crashes in Shanghai, China. , 2016, Accident; analysis and prevention.

[17]  Sybil Derrible,et al.  A protocol to convert spatial polyline data to network formats and applications to world urban road networks , 2016, Scientific Data.

[18]  Marta Blangiardo,et al.  A space–time multivariate Bayesian model to analyse road traffic accidents by severity , 2017 .

[19]  M. Reich,et al.  The neglected epidemic: road traffic injuries in developing countries , 2002, BMJ : British Medical Journal.

[20]  M. Quddus,et al.  A spatially disaggregate analysis of road casualties in England. , 2004, Accident; analysis and prevention.

[21]  Paul P Jovanis,et al.  Analysis of Road Crash Frequency with Spatial Models , 2008 .

[22]  George Yannis,et al.  A review of spatial approaches in road safety. , 2020, Accident; analysis and prevention.

[23]  Martin Tomko,et al.  Street Network Studies: from Networks to Models and their Representations , 2018, Networks and Spatial Economics.

[24]  Mohamed Abdel-Aty,et al.  Geographical unit based analysis in the context of transportation safety planning , 2013 .

[25]  B. Carlin,et al.  On the Precision of the Conditionally Autoregressive Prior in Spatial Models , 2003, Biometrics.

[26]  S. Miaou.  The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. , 1994, Accident; analysis and prevention.

[27]  Klaus Nordhausen,et al.  Statistical Analysis of Network Data with R , 2015 .

[28]  Chao Wang,et al.  Impact of traffic congestion on road accidents: a spatial analysis of the M25 motorway in England. , 2009, Accident; analysis and prevention.

[29]  F Mannering,et al.  Effect of roadway geometrics and environmental factors on rural freeway accident frequencies. , 1995, Accident; analysis and prevention.

[30]  Håvard Rue,et al.  A note on intrinsic conditional autoregressive models for disconnected graphs. , 2017, Spatial and spatio-temporal epidemiology.

[31]  Tarek Sayed,et al.  Urban Arterial Accident Prediction Models with Spatial Effects , 2009 .

[32]  Bani K. Mallick,et al.  ROADWAY TRAFFIC CRASH MAPPING: A SPACE-TIME MODELING APPROACH , 2003 .

[33]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[34]  J. Besag,et al.  On conditional and intrinsic autoregressions , 1995 .

[35]  F. Martínez-Ruiz,et al.  Estimating the occurrence of traffic accidents near school locations: A case study from Valencia (Spain) including several approaches. , 2019, Accident; analysis and prevention.

[36]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[37]  H Lum,et al.  Modeling vehicle accidents and highway geometric design relationships. , 1993, Accident; analysis and prevention.

[38]  Sumio Watanabe,et al.  Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory , 2010, J. Mach. Learn. Res..

[39]  Aki Vehtari,et al.  Understanding predictive information criteria for Bayesian models , 2013, Statistics and Computing.

[40]  Chao Wang,et al.  Predicting accident frequency at their severity levels and its application in site ranking using a two-stage mixed multivariate model. , 2011, Accident; analysis and prevention.

[41]  A. Gelfand,et al.  Proper multivariate conditional autoregressive models for spatial data analysis. , 2003, Biostatistics.

[42]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[43]  Jorge Mateu,et al.  Inhomogeneous higher-order summary statistics for point processes on linear networks , 2020, Statistics and Computing.

[44]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[45]  Vito Latora,et al.  The network analysis of urban streets: A dual approach , 2006 .

[46]  Joachim M. Buhmann,et al.  The Balanced Accuracy and Its Posterior Distribution , 2010, 2010 20th International Conference on Pattern Recognition.

[47]  Álvaro Briz-Redón,et al.  Investigation of the consequences of the modifiable areal unit problem in macroscopic traffic safety analysis: A case study accounting for scale and zoning. , 2019, Accident; analysis and prevention.

[48]  Xiaoqi Zhai,et al.  The influence of zonal configurations on macro-level crash modeling , 2018, Transportmetrica A: Transport Science.

[49]  Bradley P. Carlin,et al.  Hierarchical Multivarite CAR Models for Spatio-Temporally Correlated Survival Data , 2002 .

[50]  P. Jovanis,et al.  Spatial analysis of fatal and injury crashes in Pennsylvania. , 2006, Accident; analysis and prevention.

[51]  Pengpeng Xu,et al.  The modifiable areal unit problem in traffic safety: Basic issue, potential solutions and future research , 2016 .

[52]  Adrian Baddeley,et al.  Analysing point patterns on networks — A review , 2020 .

[53]  M. Wall.  A close look at the spatial structure implied by the CAR and SAR models , 2004 .

[54]  K. El-Basyouny,et al.  A Full Bayesian Multivariate Count Data Model of Collision Severity with Spatial Correlation , 2014 .

[55]  Jaeyoung Lee,et al.  Exploring the Effect of Different Neighboring Structures on Spatial Hierarchical Joint Crash Frequency Models , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[56]  Robin Lovelace,et al.  stats19: A package for working with open road crash data , 2019, J. Open Source Softw..

[57]  Dominique Lord,et al.  The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. , 2011, Accident; analysis and prevention.

[58]  David S. Kirk,et al.  The implications of ridehailing for risky driving and road accident injuries and fatalities. , 2020, Social science & medicine.

[59]  D. Zappa,et al.  Assessing the Risk of Car Crashes in Road Networks , 2020, Social Indicators Research.

[60]  Miguel A. Martinez-Beneito,et al.  Disease Mapping , 2019 .

[61]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[62]  Xiaoxiang Ma,et al.  Multivariate space-time modeling of crash frequencies by injury severity levels , 2017 .

[63]  P Botella-Rocamora,et al.  Spatial moving average risk smoothing , 2013, Statistics in medicine.

[64]  Mark Padgham.  dodgr: An R package for network flow aggregation , 2019 .