USA Banner
  U.S. flag
              An official website of the United States government Here's how you knowOfficial US Government Icon
              **Official websites use .gov**A **.gov** website belongs to an official government organization in the United States.
            Secure Site Icon
              **Secure .gov websites use HTTPS**
                  A **lock** ( A locked padlock ) or **https://** means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. 
 U.S. Department of Transportation Icon United States Department of Transportation
 Search DOT
            Search
                    About FHWAFHWA Organization
                Headquarters Offices
                Field Offices
                Strategic Plan
                Business Opportunities
                Staff Directories
                Careers
                Highway History
                Contact UsProgramsAcquisition Management
                Civil Rights
                Federal Lands Highway
                Infrastructure
                Innovative Program Delivery
                Operations
                Planning, Environment, and Realty
                Policy
                Research, Development, and Technology
                Safety
                Browse by TopicsResourcesCore Highway Topics
                Federal Aid Essentials
                Automation
                Complete Streets
                Laws and Regulations
                Policy & Guidance Center
                Section 508
                Publications and Statistics
                Professional Development
                National Highway Institute
                Resource Center
                Accessibility Resource Library
                FHWA Research LibraryNewsroomPress Releases
                Speeches & Testimony
                Photos
                Videos
                Media ContactsMenu OFFICE OF RESEARCH, DEVELOPMENT, AND TECHNOLOGY AT THE TURNER-FAIRBANK HIGHWAY RESEARCH CENTER Home
                        Research


            
            In This Section
        Explore Research and Technology
                HSIS Home
                Program History
                                Background
                Participating States
                Laboratory
                PBCAT App
                                Pedestrian and Bicycle Crash Analysis Tool
                Getting Started
                Crash Typing
                Features
                User Guide
                Support Images
                Products
                                Product Overview
                HSIS Summaries
                Research Reports
                Technical Summaries
                Journal Citations
                Safety Analysis Tools
                Other Products
                Data
                                Data Overview
                State Statistics
                Data Elements
                Guidebooks
                Resources
                Ongoing Research
                Contact
                Liaisons
                Site Map
                
                Predicting Motor Vehicle Collisions Using Bayesian Neural Network Models: An Empirical Analysis
Publication Information
              Publication External Link:Link not available
              Publication Type:Article
                      Abstract:Statistical models have frequently been used in highway safety studies. They can be utilized for various purposes, including establishing relationships between variables, screening covariates and predicting values. Generalized linear models (GLM) and hierarchical Bayes models (HBM) have been the most common types of model favored by transportation safety analysts. Over the last few years, researchers have proposed the back-propagation neural network (BPNN) model for modeling the phenomenon under study. Compared to GLMs and HBMs, BPNNs have received much less attention in highway safety modeling. The reasons are attributed to the complexity for estimating this kind of model as well as the problem related to over-fitting the data. To circumvent the latter problem, some statisticians have proposed the use of Bayesian neural network (BNN) models. These models have been shown to perform better than BPNN models while at the same time reducing the difficulty associated with over-fitting the data. The objective of this study is to evaluate the application of BNN models for predicting motor vehicle crashes. To accomplish this objective, a series of models was estimated using data collected on rural frontage roads in Texas. Three types of models were compared: BPNN, BNN and the negative binomial (NB) regression models.

The results of this study show that in general both types of neural network models perform better than the NB regression model in terms of data prediction. Although the BPNN model can occasionally provide better or approximately equivalent prediction performance compared to the BNN model, in most cases its prediction performance is worse than the BNN model. In addition, the data fitting performance of the BPNN model is consistently worse than the BNN model, which suggests that the BNN model has better generalization abilities than the BPNNmodel and can effectively alleviate the over-fitting problem without significantly compromising the nonlinear approximation ability. The results also show that BNNs could be used for other useful analyses in highway safety, including the development of accident modification factors and for improving the prediction capabilities for evaluating different highway design alternatives.
              Publishing Date:September 2007
                                      Author(s):Lord,
    
          Dominique
              Zhang,
    
          Wei
                      FHWA Program(s):Research
              Safety
              AMRP Program(s):Safety Data and Analysis
              FHWA Activities:Highway Safety Information System
              Subject Area:Safety and Human Factors U.S. DEPARTMENT OF TRANSPORTATION

**Federal Highway Administration**

1200 NEW JERSEY AVENUE, SE

WASHINGTON, DC 20590

202-366-4000Subscribe to email updates
                    About
  

        
              About FHWA
                Careers
                Org Chart
                Staff Directories
                Work with UsPrograms
  

        
              Acquisition Management
                Civil Rights
                Federal Lands Highway
                Infrastructure
                Innovative Program Delivery
                Operations
                Planning, Environment, and Realty
                Policy
                Research, Development, and Technology
                SafetyPolicies, Rights, Legal
  

        
              About DOT
                Accessibility
                Budget and Performance
                Civil Rights
                FOIA
                Information Quality
                No FEAR Act
                Office of Inspector General
                Privacy Policy
                USA.gov
                Web Policies and Notices
                Web StandardsNews & Events
  

        
              Newsroom
                Press Releases
                Speeches & Testimony
                Media Contacts
                Connect with Us