Development of an Accident Prediction Model for Freeways Systems

2023 ◽  
Vol 19 (4) ◽  
pp. 1
Author(s):  
Sahadev Roy ◽  
Md Faysal Kabir
Keyword(s):  
Prediction Model ◽  
Accident Prediction

scholarly journals Accident Prediction Model at Unsignalized Intersection Using Multiple Linear Regression Method

2017 ◽  
Vol 1 (2) ◽  
pp. 379-389
Author(s):  
N. K. Oghoyafedo ◽  
J. O. Ehiorobo ◽  
Ebuka Nwankwo
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Multiple Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Design Standards ◽  
Accident Prediction ◽  
Geometric Characteristics ◽  
Unsignalized Intersections ◽  
Multiple Linear Regression Method

The issue of road accidents is an increasing problem in developing countries. This could be due to increasing road traffic/vehicle occupancy, geometric characteristics and road way condition. The factors influencing accidents occurrence are to be analysed for remedies. The purpose of this research is to develop an accident prediction model as a measure for future study, aid planning phase preceding the designed intervention, enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections. Five intersections were selected randomly within Benin City and traffic count carried out at these intersections as well as geometric characteristics and roadway conditions. The prediction model was developed using multiple linear regression method and the standard error of estimate was computed to show how close the observed value is to the regression line. The model was validated using coefficient of multiple determination. The establishment of the relationship between accidents and traffic flow site characteristics on the other hand would enable improvement to be more realistically accessed. This study will also enhance the production of updated design standards to enable practitioners design unsignalized intersection for optimal safety, reduce the number of accidents at unsignalized intersections.

Evaluation of Grade Crossing Hazard Ranking Models

2017 ◽  
Author(s):  
Benjamin R. Sperry ◽  
Bhaven Naik ◽  
Jeffery E. Warner
Keyword(s):  
Literature Review ◽  
Prediction Model ◽  
Selection Process ◽  
Project Selection ◽  
Accident Prediction ◽  
Ranking Models ◽  
Ranking Model ◽  
Hazard Ranking ◽  
Grade Crossing ◽  
The U.S

Public agencies involved with highway-railroad grade crossing safety must allocate available funding to projects which are considered the most in need for improvements. Mathematical models provide a ranking of hazard risk at crossings and support the project selection process. This paper reports the results of a research study sponsored by the Ohio Rail Development Commission (ORDC) and the Ohio Department of Transportation (ODOT) examining hazard ranking models for grade crossing project selection. The goal of the research was to provide ORDC, ODOT, and other stakeholders with a better understanding of the grade crossing hazard ranking formulas and other methods used by States to evaluate grade crossing hazards and select locations for hazard elimination projects. A comprehensive literature review along with personal interviews of state DOT personnel from eight states yielded best practices for hazard ranking and project selection. The literature review found that more than three-quarters of states utilize some type of hazard ranking formula or other systematic method for project prioritization. The most commonly-used hazard ranking model in use is the U.S. DOT Accident Prediction Model; however, at least eleven states utilize state-specific hazard ranking models. Detailed evaluation of several different hazard ranking models determined that the existing hazard ranking model used in Ohio, the U.S. DOT Accident Prediction Model, should continue to be used. The research also recommends greater use of sight distance information at crossings and expanding the preliminary list of crossings to be considered in the annual program as enhancements to the existing project selection process used by the ORDC and ODOT.

Research on Traffic Accident Prediction Model for Mountainous Freeways

ICCTP 2010 ◽  
2010 ◽  
Author(s):  
Weibin Li ◽  
Xiaoduan Sun ◽  
Yulong He
Keyword(s):  
Prediction Model ◽  
Traffic Accident ◽  
Accident Prediction
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