Influence of Pavement Conditions on Commercial Motor Vehicle Crashes

Abstract:

Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary logistic regression to explore relationships between injury occurrence after a CMV crash and multiple independent variables. Additionally, Artificial Neural Network (ANN) models were developed to classify CMV crash injury severity. Importantly, the inclusion of pavement condition variables (International Roughness Index and Pavement Condition Index) substantially enhanced the accuracy of the logistic regression model, increasing predictability from 0.8% to 41%. The study also demonstrates the potential of Artificial Neural Network models in predicting CMV crash injury severity, achieving an accuracy of 60% and an F-measure of 0.52. These results highlight the importance of considering road pavement conditions in road safety policies and interventions. The study provides valuable insights for policymakers and stakeholders aiming to enhance road safety for CMVs in the District of Columbia and showcases the potential of machine learning techniques in understanding the complex interplay between road conditions and CMV crash occurrences.

Publications:

Influence of Pavement Conditions on Commercial Motor Vehicle Crashes (Full Report)

Research Brief

Additional Resources:

Project 2343-Dataset

Authors:

STEPHEN ARHIN, PHD, PE, PTOE, PMP, CRA, FELLOW ITE

Dr. Stephen Arhin is a Professor and the Chair of the Civil and Environmental Engineering Department at Howard University. He has more than 27 years of experience in all facets of traffic and transportation engineering. He is a registered Professional Engineer (P.E.) in DC, DE, MD, and VA, and a registered Professional Traffic Operations Engineer (PTOE). He is also a Certified Research Administrator (CRA), a certified Project Management Professional (PMP), and a Fellow of the Institute of Transportation Engineers (ITE). He has extensive experience in working with state and local transportation agencies on a wide variety of safety, operations, and design projects in addition to private industry. Dr. Arhin has authored and co-authored several project reports, published articles in peer-reviewed journals, and presented at conferences on such topics as pedestrian and bicycle safety, countdown pedestrian traffic signals, intelligent transportation technologies, pavement condition monitoring, crash data analysis, traffic volume trends, mitigation of reflective cracking in composite highway pavements, truck weight enforcement, and red-light violation. He is a member of ITE, TRB, and ASCE, and serves as a Program Evaluator for ABET.

BABIN MANANDHAR, EIT

Babin Manandhar is an Engineer in Training (EIT) and a research engineer at the Howard University Transportation Research and Data Center. He has a Master of Engineering degree in Civil and Environmental Engineering from Howard University.

ADAM GATIBA, PE

Adam Gatiba is a Professional Engineer who has worked for various engineering consulting firms, assisting federal, state, and local agencies in providing solutions to transportation engineering problems. He also supports the HUTRC in numerous data-driven transportation engineering research projects. Adam holds a Master of Science degree in Civil Engineering from Howard University.

Published:

December 2023

Keywords:

ANNs (artificial neural networks)

Commercial Motor Vehicles

Confusion Matrix

Road Safety

Pavement Condition