Center for Safety Equity in Transportation

rural • isolated • tribal • indigenous

Machine Learning-based Fusion Convolutional Neural Network Approaches for Driver Injury Severity Prediction Using Highway Single-Vehicle Crash Data in RITI Communities

  • Active

    CSET Project #: 2003

    Project Funding: UH

  • Start Date: August 2020

    End Date: July 2021

    Budget: $44984

Principal Investigator(s)

Guohui Zhang

The PIs has emphasized their research on crash severity formulation, analysis, and mitigation in the transportation program at the University of Hawaii. Zhang has conducted several relevance projects: 1) Alcohol Influenced Driver Injury Severity Mitigation in Intersection-Related Crashes in New Mexico; 2) Exploratory Multinomial Logit Regression Model-based Teenage and Adult Driver Injury Severity Analyses in Rear-End Crashes; and 3) Mixed Logit Model-based Driver Injury Severity Investigations in Single- Vehicle and Multi-Vehicle Crashes on Two-lane Rural Highways. The first project provides valuable insights in driver behavior analysis under the influence of alcohol in intersection-related crashes. Findings of the second project can be beneficial to better understand the difference between teenage and adult drivers, and their specific attributes in rear-end crashes. The third project enhances our understanding of single-vehicle and multi-vehicle involved crashes and advances crash severity research methodology. All these projects would provide solid contributions to the proposed project. The modeling approaches used in those three projects can be helpful to this study. Our experiences and findings from these three projects will make the proposed project start at a higher level. Guided by the USDOT’s priorities to promote the safe, efficient and environmentally sound movement of goods and people, this project will develop crash record database and research findings are helpful for transportation agencies to develop cost-effective solutions to reduce crash severities and improve traffic safety performance in RITI communities. 

Panos Prevedouros

Panos D. Prevedouros, PhD is a Professor of Transportation and Chairman of the Department of Civil Engineering at the University of Hawaii at Manoa where and he developed and manages UH’s Traffic and Transportation Laboratory. He’s a Subcommittee Chair of the Transportation Research Board (TRB), a unit of the National Academies. Prevedouros is a registered Professional Engineer in the European Union, a Court-qualified Traffic and Transportation Engineering expert in Hawaii and Illinois, and an Envision Sustainability Professional (ENV SP). Prevedouros has expertise in urban road network management, traffic safety including incident management, traffic flow simulation, traffic signal optimization, intelligent transportation systems, demand forecasting and alternatives analysis, sustainable infrastructure including transportation, energy, policies and regulation. As of September 2017, Dr. Prevedouros has published 49 Technical Reports, 48 Academic Journal Papers, 45 Conference Refereed Papers, 35 Proceedings Papers, and co-authored the 2nd and 3rd editions of internationally adopted textbook Transportation Engineering and Planning (Prentice Hall, 1993 and 2001.) He has pioneered effective traffic solutions for Honolulu such as traffic underpasses and reversible flow lanes. He’s also developed a realistic plan for Hawaii’s energy future. He blogs on Hawaii’s infrastructure challenges at

David Ma

  • Structural Health Monitoring
  • Nondestructive Testing
  • Seismic- and Wind- Structural Control
  • Structural Dynamics
  • Linear and Nonlinear System Identification

Project Summary

It was reported that more than 36,000 people lost their lives, 4.5 million people were injured, and 24 million vehicles were damaged in motor vehicle crashes in the United States in 2018. The economic costs of these crashes totaled $242 billion accounting for 1.6% of the U.S. gross domestic product. Frequent crashes with severe injuries and fatalities become more problematic in Rural, Isolated, Tribal, or Indigenous (RITI) communities. Significant research efforts have been devoted to developing better approaches to formulate driver injury severities and their impact factors in the past decades. For example, binary discrete models, such as binary logit and probit models, have been applied in many early studies. Many variations were then proposed to overcome the limitations of traditional binary logit and probit models, such as single injury outcome and unobserved effects of impact factors. Besides these statistical techniques, some researchers also applied machine learning approaches in traffic crash analyses. Abdelwahab et al. have applied multi-layer neural network models for vehicle injury severity classification. Li et al. predicted motor vehicle crashes using support vector machine models. Recent advances in artificial intelligence provided an opportunity to formulate multi-hidden-layer learning structures, i.e., deep neural network (DNN), which is capable to learn effective representations of data within unstructured data [8]. DNN approaches have been extensively studied in transportation research, such as short-term traffic flow prediction, traffic demand estimation, and traffic crash forecast. In this study, we propose a fusion convolutional neural network model with random term (FCNN-R) for driver injury severity analyses. More specifically, we plan to deploy fusion convolutional neural networks to investigate the relationships between impact factors and driver injury severities in RITI communities. The unobserved heterogeneity across different crash records is illustrated using a random error term with zero mean. A modified pseudo elasticity analysis is applied to uncover the essentiality of each variables for driver injury severities. The research findings are helpful for transportation agencies to develop cost-effective countermeasures to mitigate rural crash severities and minimize the rural crash risks and severities in the States of Alaska, Washington, Idaho, and Hawaii.