Highway Safety Analytics and Modeling: Techniques and Methods for Analyzing Crash Data

Description

Highway Safety Analytics and Modeling comprehensively covers the key elements needed to make effective transportation engineering and policy decisions based on highway safety data analysis in a single. reference. The book includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating analysis results. It discusses the challenges of working with crash and naturalistic data, identifies problems and proposes well-researched methods to solve them. Finally, the book examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. Front-Matter_2021_Highway-Safety-Analytics-and-Modeling Highway Safety Analytics and Modeling Copyright_2021_Highway-Safety-Analytics-and-Modeling Copyright Dedication_2021_Highway-Safety-Analytics-and-Modeling Dedication Preface_2021_Highway-Safety-Analytics-and-Modeling Preface Chapter-1---Introduction_2021_Highway-Safety-Analytics-and-Modeling 1 . Introduction 1.1 Motivation 1.2 Important features of this textbook 1.3 Organization of textbook 1.3.1 Part I: theory and backbround 1.3.2 Part II: highway safety analyses 1.3.3 Part III: alternative safety analyses 1.3.4 Appendices 1.3.5 Future challenges and opportunities References Chapter-2---Fundamentals-and-data-colle_2021_Highway-Safety-Analytics-and-Mo 2 . Fundamentals and data collection 2.1 Introduction 2.2 Crash process: drivers, roadways, and vehicles 2.3 Crash process: analytical framework 2.4 Sources of data and data collection procedures 2.4.1 Traditional data 2.4.1.1 Crash data 2.4.1.2 Roadway data 2.4.1.3 Traffic flow data 2.4.1.4 Supplemental data 2.4.1.5 Other safety-related data and relevant databases 2.4.2 Naturalistic driving data 2.4.3 Disruptive technological and crowdsourcing data 2.4.4 Data issues 2.5 Assembling data 2.6 4-stage modeling framework 2.6.1 Determine modeling objective matrix 2.6.2 Establish appropriate process to develop models 2.6.3 Determine inferential goals 2.6.4 Select computational techniques and tools 2.6.4.1 The likelihood-based method 2.6.4.2 The Bayesian method 2.7 Methods for evaluating model performance 2.7.1 Likelihood-based methods 2.7.1.1 Maximum likelihood estimate 2.7.1.2 Likelihood ratio test 2.7.1.3 Likelihood ratio index 2.7.1.4 Akaike information criterion 2.7.1.5 Bayes information criterion 2.7.1.6 Deviance information criterion 2.7.1.7 Widely applicable information criterion 2.7.1.8 Bayes factors 2.7.1.9 Deviance 2.7.2 Error-based methods 2.7.2.1 Mean prediction bias 2.7.2.2 Mean absolute deviation 2.7.2.3 Mean squared prediction error 2.7.2.4 Mean squared error 2.7.2.5 Mean absolute percentage error 2.7.2.6 Pearson Chi-square 2.7.2.7 Coefficient of determination Rα2 2.7.2.8 Cumulative residuals 2.8 Heuristic methods for model selection References Chapter-3---Crash-frequency-modelin_2021_Highway-Safety-Analytics-and-Modeli 3 . Crash–frequency modeling 3.1 Introduction 3.2 Basic nomenclature 3.3 Applications of crash-frequency models 3.3.1 Understanding relationships 3.3.2 Screening variables 3.3.3 Sensitivity of variables 3.3.4 Prediction 3.3.5 Causal relationships 3.4 Sources of dispersion 3.4.1 Overdispersion 3.4.2 Underdispersion 3.5 Basic count models 3.5.1 Poisson model 3.5.2 Negative binomial model 3.5.3 Poisson-lognormal model 3.5.4 Other Poisson-mixture models 3.6 Generalized count models for underdispersion 3.6.1 Conway–Maxwell–Poisson model 3.6.2 Other generalized models 3.7 Finite mixture and multivariate models 3.7.1 Finite mixture models 3.7.2 Multivariate models 3.8 Multi-distribution models 3.8.1 Negative Binomial–Lindley model 3.8.2 Other multi-distribution models 3.9 Models for better capturing unobserved heterogeneity 3.9.1 Random-effects/multilevel model 3.9.2 Random-parameters model 3.9.2.1 Random parameters 3.9.2.2 Random parameters with means as a function of explanatory variables 3.10 Semi- and nonparametric models 3.10.1 Semiparametric models 3.10.2 Dirichlet process models 3.10.3 Nonparametric models 3.11 Model selection References Chapter-4---Crash-severity-modeling_2021_Highway-Safety-Analytics-and-Modeli 4 . Crash-severity modeling 4.1 Introduction 4.2 Characteristics of crash injury severity data and methodological challenges 4.2.1 Ordinal nature of crash injury severity data 4.2.2 Unobserved heterogeneity 4.2.3 Omitted variable bias 4.2.4 Imbalanced data between injury severity levels 4.3 Random utility model 4.4 Modeling crash severity as an unordered discrete outcome 4.4.1 Multinomial logit model 4.4.2 Nested logit model 4.4.3 Mixed logit model 4.5 Modeling crash severity as an ordered discrete outcome 4.5.1 Ordinal probit/logistic model 4.5.2 Generalized ordered logistic and proportional odds model 4.5.3 Sequential logistic/probit regression model 4.6 Model interpretation References Chapter-5---Exploratory-analyses-of-safe_2021_Highway-Safety-Analytics-and-M 5 . Exploratory analyses of safety data 5.1 Introduction 5.2 Quantitative techniques 5.2.1 Measures of central tendency 5.2.1.1 Mean 5.2.1.2 Median 5.2.1.3 Mode 5.2.2 Measures of variability 5.2.2.1 Range 5.2.2.2 Quartiles and interquartile range 5.2.2.3 Variance, standard deviation and standard error 5.2.2.4 Coefficient of variation 5.2.2.5 Symmetrical and asymmetrical data 5.2.2.6 Skewness 5.2.2.7 Kurtosis 5.2.3 Measures of association 5.2.3.1 Pearson's correlation coefficient 5.2.3.2 Spearman rank-order correlation coefficient 5.2.3.3 Chi-square test for independence 5.2.3.4 Relative risk and odds ratio 5.2.4 Confidence intervals 5.2.4.1 Confidence intervals for unknown mean and known standard deviation 5.2.4.2 Confidence intervals for unknown mean and unknown standard deviation 5.2.4.3 Confidence intervals for proportions 5.2.4.4 Confidence intervals for the population variance and standard deviation 5.2.5 Hypothesis testing 5.2.5.1 Decision errors 5.2.5.2 Two-tailed hypothesis test 5.2.5.3 One-tailed hypothesis test 5.2.5.4 Hypothesis testing for one sample 5.2.5.5 Hypothesis testing for two samples 5.2.5.6 Hypothesis testing for multiple samples 5.3 Graphical techniques 5.3.1 Box-and-whisker plot 5.3.2 Histogram 5.3.3 Bar graphs 5.3.4 Error bars 5.3.5 Pie charts 5.3.6 Scatterplots 5.3.7 Bubble chart 5.3.8 Radar/web plot 5.3.9 Heatmap 5.3.10 Contour plot 5.3.11 Population pyramid References Chapter-6---Cross-sectional-and-panel-stud_2021_Highway-Safety-Analytics-and 6 . Cross-sectional and panel studies in safety 6.1 Introduction 6.2 Types of data 6.2.1 Time-series data 6.2.2 Cross-sectional data 6.2.3 Panel data 6.3 Data and modeling issues 6.3.1 Overdispersion and underdispersion 6.3.2 Low sample mean and small sample size 6.3.3 Underreporting 6.3.4 Omitted variables bias 6.3.5 Endogenous variables 6.3.6 Unobserved heterogeneity 6.4 Data aggregation 6.5 Application of crash-frequency and crash-severity models 6.5.1 Functional form 6.5.1.1 Flow-only models 6.5.1.2 Flow-only models with CMFs 6.5.1.3 Model with covariates 6.5.2 Variable selection 6.5.3 Crash variance and confidence intervals 6.5.4 Sample size determination 6.5.5 Outlier analysis 6.5.6 Model transferability 6.6 Other study types 6.6.1 Cohort studies 6.6.2 Case-control studies 6.6.3 Randomized control trials References Chapter-7---Before-after-studies-in-highw_2021_Highway-Safety-Analytics-and- 7 . Before–after studies in highway safety 7.1 Introduction 7.2 Critical issues with before–after studies 7.2.1 Regression-to-the-mean 7.2.2 Site selection bias 7.3 Basic methods 7.3.1 Simple before–after study 7.3.2 Before–after study with comparison groups 7.4 Bayesian methods 7.4.1 Empirical Bayes method 7.4.1.1 Step 1—collect data for the treatment and comparison groups 7.4.1.2 Step 2—develop a regression model from the comparison group 7.4.1.3 Step 3—estimate the EB for the before period 7.4.1.4 Step 4—calculate rtf 7.4.1.5 Step 5—estimate the predicted value for the after period 7.4.1.6 Step 6—calculate the estimated value for the after period 7.4.1.7 Step 7—calculate the variance for the predicted and estimated values 7.4.1.8 Step 8—calculate the difference and index 7.4.1.9 Step 9—calculate the variance for the difference and index 7.4.1.9.1 Caution with the EB method 7.4.2 Bayes method 7.4.2.1 Step 1—calculate Rc 7.4.2.2 Step 2—predict π 7.4.2.3 Step 3—estimate θ 7.4.2.4 Step 4—estimate δ 7.4.2.5 Step 5—determine the significance of θ and δ 7.5 Adjusting for site selection bias 7.5.1 Example application for estimating θadj 7.5.1.1 Step 1—calculate the naïve estimate 7.5.1.2 Step 2—estimate the value of the variables inside Eq. (7.45) 7.5.1.3 Step 3—calculate the adjusted safety index (Eq. 7.45) 7.6 Propensity score matching method 7.7 Before–after study using survival analysis 7.8 Sample size calculations 7.8.1 Factor influencing sample size calculations 7.8.2 Sample size estimation using known crash counts for both time periods 7.8.3 Sample size based on the variance and ratio rd (before period) References Chapter-8---Identification-of-hazardous_2021_Highway-Safety-Analytics-and-Mo 8 . Identification of hazardous sites 8.1 Introduction 8.2 Observed crash methods 8.2.1 Crash frequency method 8.2.2 Crash rate method 8.2.3 Rate quality control method 8.2.4 Equivalent property damage only method 8.2.5 Severity index method 8.2.6 Composite safety score 8.3 Predicted crash methods 8.3.1 Potential for improvement using predicted crashes 8.3.2 Level of service of safety 8.4 Bayesian methods 8.4.1 Empirical Bayes method 8.4.2 Bayes method 8.5 Combined criteria 8.6 Geostatistical methods 8.6.1 Clustering methods 8.6.1.1 K-means clustering 8.6.1.2 Ripley's K-function 8.6.1.3 Nearest neighborhood clustering 8.6.1.4 Moran's I index 8.6.1.5 Getis-Ord general G∗(d) 8.6.2 Kernel density estimation 8.7 Crash concentration location methods 8.7.1 Sliding window method 8.7.2 Peak searching method 8.7.3 Continuous risk profile 8.8 Proactive methods 8.8.1 Identify focus crash types and facility types 8.8.2 Develop risk factors 8.8.3 Screen and prioritize candidate locations 8.9 Evaluating site selection methods 8.9.1 Site consistency test 8.9.2 Method consistency test 8.9.3 Total rank differences test 8.9.4 Total score test 8.9.5 False identification test 8.9.6 Poisson mean differences References Chapter-9---Models-for-spatial-data_2021_Highway-Safety-Analytics-and-Modeli 9 . Models for spatial data 9.1 Introduction 9.2 Spatial data and data models 9.3 Measurement of spatial association 9.3.1 Global statistics for spatial association 9.3.1.1 Getis–Ord general G∗(d) 9.3.1.2 Moran's I 9.3.2 Local indicators of spatial association 9.3.2.1 Local Gi∗(d) 9.3.2.2 Local Moran's Ii 9.4 Spatial weights and distance decay models 9.5 Point data analysis 9.5.1 First- and second-order process 9.5.2 Kernel density estimation 9.5.3 Ripley's K-function 9.5.4 Cross-K function 9.6 Spatial regression analysis 9.6.1 Spatial econometrics methods 9.6.1.1 Spatial autoregressive model 9.6.1.2 Spatial error model 9.6.2 Generalized linear model with spatial correlation 9.6.2.1 Generalized linear mixed model 9.6.2.2 Hierarchical Bayesian model 9.6.3 Modeling local relationships in crash data References Chapter-10---Capacity--mobility--and-s_2021_Highway-Safety-Analytics-and-Mod 10 . Capacity, mobility, and safety 10.1 Introduction 10.2 Modeling space between vehicles 10.3 Safety as a function of traffic flow 10.4 Characterizing crashes by real-time traffic 10.5 Predicting imminent crash likelihood 10.6 Real-time predictive analysis of crashes 10.6.1 Binary logistic regression model 10.6.2 Conditional logistic regression model 10.6.3 A note about binary logit and conditional logistic regression models 10.7 Using traffic simulation to predict crashes 10.7.1 Cell transmission model 10.7.2 Fundamental diagram calibration 10.7.3 CTM simulation algorithm 10.7.4 Crash modeling 10.7.5 Crash prediction References Chapter-11---Surrogate-safety-measur_2021_Highway-Safety-Analytics-and-Model 11 . Surrogate safety measures 11.1 Introduction 11.2 An historical perspective 11.3 Traffic conflicts technique 11.4 Field survey of traffic conflicts 11.5 Proximal surrogate safety measures 11.5.1 Collision course 11.5.2 Time- and distance-based proximal surrogate safety measures 11.5.2.1 Time to collision family 11.5.2.2 Encroachment time family 11.5.2.3 Proportion of stopping distance 11.5.2.4 Other indicators 11.6 Theoretical development of safety surrogate measures 11.6.1 Block maxima using the generalized extreme value distribution 11.6.2 Peak over threshold using the GP distribution 11.6.3 Block maxima or peak over threshold 11.7 Safety surrogate measures from traffic microsimulation models 11.8 Safety surrogate measures from video and emerging data sources References Chapter-12---Data-mining-and-machine-learni_2021_Highway-Safety-Analytics-an 12 . Data mining and machine learning techniques 12.1 Introduction 12.2 Association rules 12.3 Clustering analysis 12.3.1 K-means clustering 12.3.2 Latent class cluster 12.4 Decision tree model 12.4.1 The CART model 12.4.2 Random forest 12.4.3 Gradient boosted trees 12.5 Bayesian networks 12.6 Neural network 12.6.1 Multilayer perceptron neural network 12.6.2 Convolutional neural networks 12.6.3 Long short-term memory—recurrent neural networks 12.6.4 Bayesian neural networks 12.7 Support vector machines 12.8 Sensitivity analysis References Appendix-A---Negative-binomial-regression-model_2021_Highway-Safety-Analytic A - Negative binomial regression models and estimation methods . Probability density and likelihood functions . Poisson-gamma model NB-2 model NB-1 model . Poisson-gamma model with spatial interaction . Estimation methods Maximum likelihood estimation Monte Carlo Markov Chain estimation MCMC Poisson-gamma model MCMC Poisson-gamma-CAR model Prior distributions for MCMC Poisson-gamma-CAR References Appendix-B---Summary-of-crash-frequency-and-crash_2021_Highway-Safety-Analyt B - Summary of crash-frequency and crash-severity models in highway safety Introduction Crash-frequency modeling Crash-severity modeling Crash modeling by model type References Appendix-C---Computing-codes_2021_Highway-Safety-Analytics-and-Modeling C - Computing codes Negative binomial model SAS code R code WinBUGS code Negative binomial model with varying dispersion parameter SAS code WinBUGS code Radom effects negative binomial model SAS code WinBUGS code Random parameters negative binomial model SAS code WinBUGS code Poisson-lognormal model WinBUGS code Negative binomial-Lindley model WinBUGS code Conway–Maxwell–Poisson distribution R code Multinomial logit model SAS code Nested logit model SAS code Appendix-D---List-of-exercise-datase_2021_Highway-Safety-Analytics-and-Model D - List of exercise datasets Index_2021_Highway-Safety-Analytics-and-Modeling Index A B C D E F G H I K L M N O P Q R S T U V W Z