Highway safety analytics and modeling:
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam, Netherlands
Elsevier
[2021]
|
| Online-Zugang: | DE-91 DE-91 |
| Beschreibung: | 1 Online-Ressource (xiii, 488 Seiten) Illustrationen, Diagramme |
| ISBN: | 9780128168196 |
Internformat
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| 100 | 1 | |a Lord, Dominique |e Verfasser |0 (DE-588)1187680613 |4 aut | |
| 245 | 1 | 0 | |a Highway safety analytics and modeling |c Dominique Lord, Xiao Qin, Srinivas R. Geedipally |
| 264 | 1 | |a Amsterdam, Netherlands |b Elsevier |c [2021] | |
| 300 | |a 1 Online-Ressource (xiii, 488 Seiten) |b Illustrationen, Diagramme | ||
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| 505 | 8 | |a Front Cover -- Highway Safety Analytics and Modeling -- Highway Safety Analytics and Modeling -- Copyright -- Dedication -- Contents -- Preface -- 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 -- 1 - Theory and background -- 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 | |
| 505 | 8 | |a 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 R2a -- 2.7.2.8 Cumulative residuals -- 2.8 Heuristic methods for model selection -- 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 -- 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 | |
| 505 | 8 | |a 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 -- 2 - Highway safety analyses -- 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 | |
| 505 | 8 | |a 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 -- 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 -- 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 | |
| 505 | 8 | |a 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 -- 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 | |
| 505 | 8 | |a 8.7.3 Continuous risk profile | |
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| author | Lord, Dominique Qin, Xiao Geedipally, Srinivas R. |
| author_GND | (DE-588)1187680613 |
| author_facet | Lord, Dominique Qin, Xiao Geedipally, Srinivas R. |
| author_role | aut aut aut |
| author_sort | Lord, Dominique |
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| contents | Front Cover -- Highway Safety Analytics and Modeling -- Highway Safety Analytics and Modeling -- Copyright -- Dedication -- Contents -- Preface -- 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 -- 1 - Theory and background -- 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 R2a -- 2.7.2.8 Cumulative residuals -- 2.8 Heuristic methods for model selection -- 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 -- 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 -- 2 - Highway safety analyses -- 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 -- 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 -- 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 -- 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 |
| ctrlnum | (ZDB-30-PQE)EBC6501455 (ZDB-30-PAD)EBC6501455 (ZDB-89-EBL)EBL6501455 (OCoLC)1319631278 (DE-599)BVBBV047694929 |
| discipline | Bauingenieurwesen Verkehrstechnik |
| discipline_str_mv | Bauingenieurwesen Verkehrstechnik |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000zc 4500</leader><controlfield tag="001">BV047694929</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230301</controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">220119s2021 xx a||| o|||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780128168196</subfield><subfield code="9">978-0-12-816819-6</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC6501455</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC6501455</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL6501455</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1319631278</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV047694929</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BAU 873</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">BAU 823</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Lord, Dominique</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1187680613</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Highway safety analytics and modeling</subfield><subfield code="c">Dominique Lord, Xiao Qin, Srinivas R. Geedipally</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam, Netherlands</subfield><subfield code="b">Elsevier</subfield><subfield code="c">[2021]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xiii, 488 Seiten)</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Front Cover -- Highway Safety Analytics and Modeling -- Highway Safety Analytics and Modeling -- Copyright -- Dedication -- Contents -- Preface -- 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 -- 1 - Theory and background -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 R2a -- 2.7.2.8 Cumulative residuals -- 2.8 Heuristic methods for model selection -- 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 -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 -- 2 - Highway safety analyses -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 -- 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 -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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 -- 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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">8.7.3 Continuous risk profile</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qin, Xiao</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Geedipally, Srinivas R.</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">978-0-12-816818-9</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-4-NLEBK</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033078922</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6501455</subfield><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">TUM_Einzelkauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2464408</subfield><subfield code="l">DE-91</subfield><subfield code="p">ZDB-4-NLEBK</subfield><subfield code="q">TUM_PDA_EBSCOBAE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047694929 |
| illustrated | Illustrated |
| index_date | 2024-07-03T18:57:28Z |
| indexdate | 2024-12-06T15:18:03Z |
| institution | BVB |
| isbn | 9780128168196 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033078922 |
| oclc_num | 1319631278 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xiii, 488 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-30-PQE ZDB-4-NLEBK ZDB-30-PQE TUM_Einzelkauf ZDB-4-NLEBK TUM_PDA_EBSCOBAE_Kauf |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Elsevier |
| record_format | marc |
| spelling | Lord, Dominique Verfasser (DE-588)1187680613 aut Highway safety analytics and modeling Dominique Lord, Xiao Qin, Srinivas R. Geedipally Amsterdam, Netherlands Elsevier [2021] 1 Online-Ressource (xiii, 488 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Front Cover -- Highway Safety Analytics and Modeling -- Highway Safety Analytics and Modeling -- Copyright -- Dedication -- Contents -- Preface -- 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 -- 1 - Theory and background -- 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 R2a -- 2.7.2.8 Cumulative residuals -- 2.8 Heuristic methods for model selection -- 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 -- 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 -- 2 - Highway safety analyses -- 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 -- 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 -- 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 -- 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 Qin, Xiao Verfasser aut Geedipally, Srinivas R. Verfasser aut Erscheint auch als Druck-Ausgabe 978-0-12-816818-9 |
| spellingShingle | Lord, Dominique Qin, Xiao Geedipally, Srinivas R. Highway safety analytics and modeling Front Cover -- Highway Safety Analytics and Modeling -- Highway Safety Analytics and Modeling -- Copyright -- Dedication -- Contents -- Preface -- 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 -- 1 - Theory and background -- 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 R2a -- 2.7.2.8 Cumulative residuals -- 2.8 Heuristic methods for model selection -- 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 -- 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 -- 2 - Highway safety analyses -- 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 -- 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 -- 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 -- 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 |
| title | Highway safety analytics and modeling |
| title_auth | Highway safety analytics and modeling |
| title_exact_search | Highway safety analytics and modeling |
| title_exact_search_txtP | Highway safety analytics and modeling |
| title_full | Highway safety analytics and modeling Dominique Lord, Xiao Qin, Srinivas R. Geedipally |
| title_fullStr | Highway safety analytics and modeling Dominique Lord, Xiao Qin, Srinivas R. Geedipally |
| title_full_unstemmed | Highway safety analytics and modeling Dominique Lord, Xiao Qin, Srinivas R. Geedipally |
| title_short | Highway safety analytics and modeling |
| title_sort | highway safety analytics and modeling |
| work_keys_str_mv | AT lorddominique highwaysafetyanalyticsandmodeling AT qinxiao highwaysafetyanalyticsandmodeling AT geedipallysrinivasr highwaysafetyanalyticsandmodeling |