Last Updated: April 4, 2026
Welcome to the companion website to Highway Safety Analytics and Modeling. Below, you will find all the supplemental materials related to the content of the textbook. We hope the material will be useful for the readers of the textbook. Please let us know if you have questions or comments. Thank You. Dom
Where to buy the textbook
The 2nd Edition of the textbook is now available at Elsevier.
Link to Amazon.
Review Quotes for the 2nd Edition
Hi Dominique, I just purchased your book: Highway Safety Analytics and Modeling (2026, second edition). It is amazing! Congratulations on your amazing work!
Best, Jenny C. (Feb. 28, 2026)
Just got the Highway Safety Analytics and Modeling book 2nd Edition from Elsevier by Dominique Lord, Xiao Qin, Srinivas R. Geedipally
A master reference in traffic crash modeling for the whole Road infrastructure Safety Management process Ahmed K. (March 20, 2026)
Corrections and Updates
Errata Sheet (Feb. 25, 2026)
Compilation of Papers on Sample Size Requirements for Discrete Choice Models
Sample_Size_Discrete_Choice_Models (April 4, 2026)
This manuscript summarizes more than 65 papers and textbooks on the importance of using an adequate sample size (i.e., minimum sample size) to estimate discrete choice models, such as the logistic, multinomial, and mixed-logit (random parameters) models. Those models are used to develop crash-severity models in highway safety. As shown in these papers and textbooks, the issues related to small sample sizes are not caused by the misspecification of the model. Making decisions about selecting the required sample size should not be assessed using the likelihood ratio test, which is itself dependent on the sample size. Crash-severity models need to be assessed using statistical tests such as the calibrating slopes, Briar score, Nagelkerke R-squared, and Cox-Snell R-squared among others. These tests can be used to identify or quantify the level overfitting (i.e., too many variables for the number of observations) in crash-severity models. Overfitting is an important issue that can lead to unreliable parameter estimates, as shown in all the papers included in the manuscript. Many of these tests are described in the textbook, but the full panoply of all these test and methods are described the compilation. This compilation serves as a supplement to the textbook and is free to be downloaded.
Pate et al. (2023) have provided all the R Codes for evaluating the required sample sizes for a multinomial logit model: Project 8 Multinomial Sample Size
Pate, A., R.D. Riley, G.S. Collins, M. van Smeden, B. Van Calster, J. Ensor, G.P. Martin, 2023. Minimum sample size for developing a multivariable prediction model using multinomial logistic regression. Statistical Methods in Medical Research. Vol. 32, No. 3, 555–571. https://doi.org/10.1177/09622802231151220
Gehringer et al. (2024) have provided the step-by-step procedure with an example to estimate the required sample size to estimate and validate multinomial logit models as supplemented material. The material can be found on this website: https://ars.els-cdn.com/content/image/1-s2.0-S0895435624002373-mmc1.docx
Gehringer, C.K., G.P. Martin, B. Van Calster, K.L. Hyrich, S.M.M. Verstappen, J.C. Sergeant, 2024. How to develop, validate, and update clinical prediction models using multinomial logistic regression. Journal of Clinical Epidemiology, Vol. 174, 111481. https://doi.org/10.1016/j.jclinepi.2024.111481
CURE Plots in Excel (end of Chapter 2)
Appendix D – Datasets
For the datasets below, just disregard the number at the end of the file, which represents the uploading attempts. I will try to correct them soon.
Exercise_3.1_Michigan_Rural_Two-Lane_Highways_Dataset
Exercise_3.3_South_Korean_Dataset
Exercise_3.4_Texas_Rural_Divided_Multilane_Highways_Dataset
Exercise_4.1-4.2-4.4-4.5_Large_Trucks_Dataset (minus 4.3)
Exercise_4.3_Large_Trucks_Mixed_Logit_Dataset_Part_1
Exercise_4.3_Large_Trucks_Mixed_Logit_Dataset_Part_2 (copy all columns and put them at column BQ in Part 1)
Exercises_6.1-6.3_Texas_Undivided_Highways_Dataset
Exercises_7.1-7.3_Before-After_Dataset
Exercises_8.1-8.8_Texas_Sample_Intersections_Dataset
Exercise_10.1_Inductive_Loop_Detector_Dataset
Exercise_10.1_Inductive_Loop_Detector_Dataset
Exercise_11.1-11.2_Car-following_Dataset
End of Chapter Datasets
For the datasets below, just disregard the number at the end of the file, which represents the uploading attempts. I will try to correct them soon.
Chapter 2 Exercise 1 CR-3_2023
Chapter 2 Exercise 1 code-sheet CR-3
Chapter 3 – End of Chapter Exercise Dataset 1
Chapter 3 – End of Chapter Exercise Dataset 2
Chapter 4 – End of Chapter Exercise Dataset 1
Chapter 4 – End of Chapter Exercise Dataset 2
Chapter 5 – End of Chapter Exercise Dataset 1
Chapter 6 – End of Chapter Exercise Dataset 1
Chapter 6 – End of Chapter Exercise Dataset 2
Chapter 7 – End of Chapter Exercise Dataset 1
Chapter 7 – End of Chapter Exercise Dataset 2
Chapter 9 – End of Chapter Exercise Dataset 1
Teaching Material
CVEN 626 – Course Syllabus Fall 2025
Topic_1_-_Crash_Characteristics
Topic_2_-_Crash_Contributing_Factors_and_Traffic_Injury_Costs
Topic_3_-_Crash_Data_Collection_and_Management
Topic_4_-_Exploratory_Analyses_of_Crash_Data
Topic_5_-_Crash_Modeling_Fundamentals
Topic_6a_-_Crash-Frequency_Models
Topic_6b_-_Crash-Severity_Models
Topic_7_-_Application of Safety Methods
Topic_8_-_Before-After Studies
Topic_10_-_Data_Mining_Machine_Learing_Artificial_Intelligence
Assignment_1_CVEN_626_Fall_2025
Assignment_2_CVEN_626_Fall_2025
Assignment_3_CVEN_626_Fall_2025
Assignment_4_CVEN_626_Fall_2025
Assignment_5_CVEN_626_Fall_2025
Assignment_6_CVEN_626_Fall_2025
All the information below is for the 1st Edition
Review Quotes for 1st edition
“This is an excellent book on highway safety (I think a must-read book who are interested to work on traffic crash data). The book has successfully enlisted all the required theory and methods to extract inference by analyzing crash data. For many years there have been a need for a single book/material which will accumulate all information relevant Read more about review stating A single material which covers all aspects for crash data collection and analysis to traffic crash data modelling. This book fills that gap…” Sumon M. 05/18/21
“This book is the most concise way of learning about highway safety analytics and modeling techniques for novice as well as for professionals. I have found this book most useful during my highway safety course. Numerous numerical examples in the book makes it much easier to understand the text. I recommend this book as a must read to all the researchers in highway safety.” GS 11/12/21
“Very informational” Clawrence 11/12/21
“For someone in the field of traffic safety, this is a must-have book. It is a unique source that includes and covers all the aspects of traffic safety, and it is among the top safety references that I have had so far.” Reihaneh 11/23/21
Corrections and Updates
July 18, 2021
Corrections to manuscript *New PDF File (This file will be updated on a regular basis as corrections are being provided)
March, 2021 Update:
This paper explains how to predict crashes (out-of-sample) using random parameters models:
Hou, Q., X. Huo b, A.P. Tarko, and J. Leng (2021) Comparative analysis of alternative random parameters count data models in highway safety. Analytic Methods in Accident Research, Vol. 30, 100158.
Note: The RP model cannot be used to predict crashes without estimating the distribution of the parameters (i.e., cannot use the mean of the parameter only). Hence, it is not suitable for applying it using the methods described in the Highway Safety Manual for example.
May, 2021 Update:
In Chapter 2, Section 2.4.2, it is important to point out that naturalistic data may suffer from self-selection bias, as the drivers who are selected to participate in such studies are likely to modify their behavior and consequently reduce the risk of crashes. The magnitude of the difference compared to the general population is currently not known. Researchers should be aware of this potential issue.