Spatial predictions of harsh driving events using statistical and machine learning methods

[1]  Apostolos Ziakopoulos.  Spatial analysis of harsh driving behavior events in urban networks using high-resolution smartphone and geometric data. , 2021, Accident; analysis and prevention.

[2]  Kun Xie,et al.  Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters. , 2021, Accident; analysis and prevention.

[3]  George Yannis,et al.  Combined impact of road and traffic characteristic on driver behavior using smartphone sensor data. , 2020, Accident; analysis and prevention.

[4]  George Yannis,et al.  Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior , 2020, Sensors.

[5]  Tomer Toledo,et al.  Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast , 2020 .

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

[7]  George Yannis,et al.  A critical overview of driver recording tools. , 2020, Journal of safety research.

[8]  Eleni I. Vlahogianni,et al.  Driving safety efficiency benchmarking using smartphone data , 2019 .

[9]  Helai Huang,et al.  Integrating macro- and micro-level safety analyses: a Bayesian approach incorporating spatial interaction , 2019 .

[10]  George Yannis,et al.  Analysis of driver behaviour through smartphone data: The case of mobile phone use while driving , 2019, Safety Science.

[11]  Eleni I. Vlahogianni,et al.  Identifying driving safety profiles from smartphone data using unsupervised learning , 2019, Safety Science.

[12]  Alexander Brenning,et al.  Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data , 2019, Ecological Modelling.

[13]  Yiik Diew Wong,et al.  A feature learning approach based on XGBoost for driving assessment and risk prediction. , 2019, Accident; analysis and prevention.

[14]  S. M. Sohel Mahmud,et al.  Micro-simulation modelling for traffic safety: A review and potential application to heterogeneous traffic environment , 2019, IATSS Research.

[15]  Jaeyoung Lee,et al.  Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data , 2019, International journal of environmental research and public health.

[16]  Xiaoqi Zhai,et al.  Boundary crash data assignment in zonal safety analysis: An iterative approach based on data augmentation and Bayesian spatial model. , 2018, Accident; analysis and prevention.

[17]  Nicolas Saunier,et al.  Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models. , 2018, Accident; analysis and prevention.

[18]  Mohamed Abdel-Aty,et al.  Developing a grouped random parameters multivariate spatial model to explore zonal effects for segment and intersection crash modeling , 2018, Analytic Methods in Accident Research.

[19]  Ali Ufuk Peker,et al.  Prediction of Risk Generated by Different Driving Patterns and Their Conflict Redistribution , 2018, IEEE Transactions on Intelligent Vehicles.

[20]  Behram Wali,et al.  Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity. , 2017, Accident; analysis and prevention.

[21]  Juneyoung Park,et al.  Crash modeling for intersections and segments along corridors: a Bayesian multilevel joint model with random parameters , 2017 .

[22]  Flávio Cunto,et al.  Geographically weighted negative binomial regression applied to zonal level safety performance models. , 2017, Accident; analysis and prevention.

[23]  Eleni I. Vlahogianni,et al.  Driving analytics using smartphones: Algorithms, comparisons and challenges , 2017 .

[24]  Mohamed Abdel-Aty,et al.  Intersection crash prediction modeling with macro-level data from various geographic units. , 2017, Accident; analysis and prevention.

[25]  Mohammed Quddus,et al.  Crash data quality for road safety research: Current state and future directions. , 2017, Accident; analysis and prevention.

[26]  Ismail Elkhrachy,et al.  Vertical accuracy assessment for SRTM and ASTER Digital Elevation Models: A case study of Najran city, Saudi Arabia , 2017, Ain Shams Engineering Journal.

[27]  Jonathan Aguero-Valverde,et al.  Bayesian Spatial Models of Crash Frequency at Highway–Railway Crossings , 2017 .

[28]  A. Stewart Fotheringham,et al.  Geographically weighted regression and multicollinearity: dispelling the myth , 2016, J. Geogr. Syst..

[29]  Bo Song,et al.  Macro and micro models for zonal crash prediction with application in hot zones identification , 2016 .

[30]  Gudmundur F. Ulfarsson,et al.  Spatial regression analysis of traffic crashes in Seoul. , 2016, Accident; analysis and prevention.

[31]  Jaeyoung Lee,et al.  Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach , 2016, Neurocomputing.

[32]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[33]  Helai Huang,et al.  Support vector machine in crash prediction at the level of traffic analysis zones: Assessing the spatial proximity effects. , 2015, Accident; analysis and prevention.

[34]  Travis J. Berge.  Predicting Recessions with Leading Indicators: Model Averaging and Selection over the Business Cycle: Predicting Recessions with Leading Indicators , 2015 .

[35]  Yihui Xie,et al.  Create Interactive Web Maps with the JavaScript 'Leaflet'Library , 2015 .

[36]  Mohamed Abdel-Aty,et al.  Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level. , 2015, Accident; analysis and prevention.

[37]  Pengpeng Xu,et al.  Modeling crash spatial heterogeneity: random parameter versus geographically weighting. , 2015, Accident; analysis and prevention.

[38]  J. Broughton,et al.  Modeling road accident injury under-reporting in Europe , 2014 .

[39]  Thorsten Staake,et al.  Multivariate exposure modeling of accident risk: Insights from Pay-as-you-drive insurance data , 2014 .

[40]  Jonathan Aguero-Valverde,et al.  Direct Spatial Correlation in Crash Frequency Models: Estimation of the Effective Range , 2014 .

[41]  Duncan Lee,et al.  CARBayes: An R Package for Bayesian Spatial Modeling with Conditional Autoregressive Priors , 2013 .

[42]  Marco Dozza,et al.  Recognising safety critical events: can automatic video processing improve naturalistic data analyses? , 2013, Accident; analysis and prevention.

[43]  Martin Charlton,et al.  GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models , 2013, 1306.0413.

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

[45]  A. Shalaby,et al.  Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. , 2010, Accident; analysis and prevention.

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

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

[48]  Thomas A. Dingus,et al.  Driver distraction in long-haul truck drivers , 2005 .

[49]  Charles E. McCulloch,et al.  Generalized Linear Mixed Models , 2005 .

[50]  Peter Bonsall,et al.  Modelling safety-related driving behaviour: impact of parameter values , 2005 .

[51]  Gregory F. Cooper,et al.  Model Averaging for Prediction with Discrete Bayesian Networks , 2004, J. Mach. Learn. Res..

[52]  Michael P. Clements,et al.  Pooling of Forecasts , 2004 .

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

[54]  J. Besag,et al.  Bayesian image restoration, with two applications in spatial statistics , 1991 .

[55]  Jie Bao,et al.  A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. , 2019, Accident; analysis and prevention.

[56]  George Yannis,et al.  Innovative motor insurance schemes: A review of current practices and emerging challenges. , 2017, Accident; analysis and prevention.

[57]  Bernd Bischl,et al.  mlr: Machine Learning in R , 2016, J. Mach. Learn. Res..

[58]  Yueng-Hsiang Huang,et al.  A mediation model linking dispatcher leadership and work ownership with safety climate as predictors of truck driver safety performance. , 2014, Accident; analysis and prevention.

[59]  Omar Bagdadi,et al.  Assessing safety critical braking events in naturalistic driving studies , 2013 .

[60]  Omar Bagdadi,et al.  Development of a method for detecting jerks in safety critical events. , 2013, Accident; analysis and prevention.

[61]  Sudeshna Mitra,et al.  Spatial Autocorrelation and Bayesian Spatial Statistical Method for Analyzing Intersections Prone to Injury Crashes , 2009 .

[62]  Tarek Sayed,et al.  SURROGATE MEASURES OF SAFETY , 2009 .

[63]  Larry Head,et al.  Surrogate Safety Measures from Traffic Simulation Models , 2003 .

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