Statistical and Econometric Methods for Transportation Data Analysis, Third Edition

Overview

Transportation systems generate vast amounts of data—traffic counts, crash records, travel times, mode choices, and more. Turning that data into meaningful insight requires rigorous statistical and econometric methods. This third edition offers a substantially updated and expanded treatment of the quantitative techniques needed to analyze transportation data effectively.

The authors guide readers from core concepts in statistical inference through advanced modeling approaches, always with an eye toward real-world transportation applications. The book balances theoretical underpinnings with practical model building, interpretation, and diagnostic assessment.

What’s Inside

The material is organized into three main sections:

• Fundamentals: Descriptive statistics, interval estimation, hypothesis testing, and nonparametric methods lay the groundwork for later chapters.
• Continuous Dependent Variable Models: Linear regression, regression assumption violations, simultaneous equations, panel data, time series exploration, ARIMA forecasting, principal components, structural equation modeling, and hazard-based duration models.
• Count and Discrete-Dependent Variable Models: Poisson and negative binomial regression, logistic regression, multinomial and nested logit models, and ordered probability models.

Practical, Applied Focus

Each technique is presented with attention to when and why it is appropriate, how to estimate parameters, and how to check whether the model assumptions hold. The authors emphasize interpretation of coefficients, statistical evaluation, and the pitfalls of misspecification—skills that matter as much as software proficiency when working with transportation data.

Who Will Find It Useful

Graduate students in transportation engineering, civil engineering, urban planning, and economics will appreciate the systematic progression. Researchers and practitioners who analyze safety, travel demand, traffic operations, or transportation policy data will find the advanced methods valuable for moving beyond basic summaries and simple regressions.

Why the Third Edition Matters

This edition incorporates recent methodological developments, including random parameter models, latent class models, stochastic volatility, and other extensions that have become increasingly important in contemporary transportation research. The added examples and data sets support hands-on learning and reproducible analysis.