Highway Safety Analytics and Modeling Techniques and Methods for Analyzing Crash Data 1st edition by Dominique Lord, Xiao Qin, Srinivas Geedipally 0128168196 9780128168196

Highway Safety Analytics and Modeling: Techniques and Methods for Analyzing Crash Data 1st edition by Dominique Lord, Xiao Qin, Srinivas R. Geedipally – Ebook PDF Instant Download/DeliveryISBN:  0128168196 9780128168196 

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Product details:

ISBN-10 :  0128168196

ISBN-13 :  9780128168196

Author : Dominique Lord, Xiao Qin, Srinivas R. Geedipally  

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.

Highway Safety Analytics and Modeling: Techniques and Methods for Analyzing Crash Data 1st Table of contents:

Chapter 1. Introduction
1.1. Motivation
1.2. Important features of this textbook
1.3. Organization of textbook
I. Theory and background
Chapter 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
2.5. Assembling data
2.6. 4-stage modeling framework
2.7. Methods for evaluating model performance
2.8. Heuristic methods for model selection
Chapter 3. Crash–frequency modeling
3.1. Introduction
3.2. Basic nomenclature
3.3. Applications of crash-frequency models
3.4. Sources of dispersion
3.5. Basic count models
3.6. Generalized count models for underdispersion
3.7. Finite mixture and multivariate models
3.8. Multi-distribution models
3.9. Models for better capturing unobserved heterogeneity
3.10. Semi- and nonparametric models
3.11. Model selection
Chapter 4. Crash-severity modeling
4.1. Introduction
4.2. Characteristics of crash injury severity data and methodological challenges
4.3. Random utility model
4.4. Modeling crash severity as an unordered discrete outcome
4.5. Modeling crash severity as an ordered discrete outcome
4.6. Model interpretation
II. Highway safety analyses
Chapter 5. Exploratory analyses of safety data
5.1. Introduction
5.2. Quantitative techniques
5.3. Graphical techniques
Chapter 6. Cross-sectional and panel studies in safety
6.1. Introduction
6.2. Types of data
6.3. Data and modeling issues
6.4. Data aggregation
6.5. Application of crash-frequency and crash-severity models
6.6. Other study types
Chapter 7. Before–after studies in highway safety
7.1. Introduction
7.2. Critical issues with before–after studies
7.3. Basic methods
7.4. Bayesian methods
7.5. Adjusting for site selection bias
7.6. Propensity score matching method
7.7. Before–after study using survival analysis
7.8. Sample size calculations
Chapter 8. Identification of hazardous sites
8.1. Introduction
8.2. Observed crash methods
8.3. Predicted crash methods
8.4. Bayesian methods
8.5. Combined criteria
8.6. Geostatistical methods
8.7. Crash concentration location methods
8.8. Proactive methods
8.9. Evaluating site selection methods
Chapter 9. Models for spatial data
9.1. Introduction
9.2. Spatial data and data models
9.3. Measurement of spatial association
9.4. Spatial weights and distance decay models
9.5. Point data analysis
9.6. Spatial regression analysis
Chapter 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
10.7. Using traffic simulation to predict crashes
III. Alternative safety analyses
Chapter 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
11.6. Theoretical development of safety surrogate measures
11.7. Safety surrogate measures from traffic microsimulation models
11.8. Safety surrogate measures from video and emerging data sources
Chapter 12. Data mining and machine learning techniques
12.1. Introduction
12.2. Association rules
12.3. Clustering analysis
12.4. Decision tree model
12.5. Bayesian networks
12.6. Neural network
12.7. Support vector machines
12.8. Sensitivity analysis

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