Accident Prediction Models for Urban Unsignalized Intersections in British Columbia

1999 ◽  
Vol 1665 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Tarek Sayed ◽  
Felipe Rodriguez
Keyword(s):  
British Columbia ◽  
Prediction Models ◽  
Accident Prediction ◽  
Unsignalized Intersections

Calibration and Transferability of Accident Prediction Models for Urban Intersections

2002 ◽  
Vol 1784 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Bhagwant Persaud ◽  
Dominique Lord ◽  
Joseph Palmisano
Keyword(s):  
Functional Form ◽  
Prediction Models ◽  
Research Effort ◽  
Safety Performance ◽  
Accident Prediction ◽  
Unsignalized Intersections ◽  
Safety Performance Functions ◽  
Safety Design ◽  
Accident Data ◽  
Single Calibration

Accident prediction models, also known as safety performance functions, have several important uses in modern-day safety analysis. Unfortunately, calibration of these models is not straightforward. A research effort was undertaken that demonstrates the complexity of calibrating these models for urban intersections. These complexities relate to the specification of the functional form, the accommodation of the peculiarities of accident data, and the transferability of models to other jurisdictions. Toronto data were used to estimate models for three- and four-legged signalized and unsignalized intersections. Then the performance of these models was compared with that of models for Vancouver and California that were recalibrated for Toronto using a procedure recently proposed for the application in the Interactive Highway Safety Design Model (IHSDM). The results of this transferability test are mixed, suggesting that a single calibration factor as is currently specified in the IHSDM procedure may be inappropriate and that a disaggregation by traffic volume might be preferable.

Accident prediction models with random corridor parameters

2009 ◽  
Vol 41 (5) ◽  
pp. 1118-1123 ◽  
Author(s):  
Karim El-Basyouny ◽  
Tarek Sayed
Keyword(s):  
Prediction Models ◽  
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.

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

Evaluation of accident prediction for rural highways

2008 ◽  
Vol 35 (6) ◽  
pp. 647-651 ◽  
Author(s):  
Eric Hildebrand ◽  
Karen Robichaud ◽  
Hong Ye
Keyword(s):  
New Brunswick ◽  
Prediction Models ◽  
Retrospective Evaluation ◽  
Rural Highways ◽  
Accident Prediction ◽  
Predictive Variables ◽  
Accident Rates ◽  
Historical Accident

This paper evaluates the accuracy of three commonly used models that predict accidents on two-lane, rural, arterial highways. The retrospective evaluation compared model outputs with empirical collision results for a sample of highway sections in the Province of New Brunswick. The analysis determined historical accident rates, identified key predictive variables, and compared the observed results with estimates from each safety model. All three models were found to significantly overestimate accident frequencies on the highway sections under study. The model generally employed in New Brunswick, MicroBENCOST, was found to yield the highest errors in estimated collisions. These findings suggest that the benefits from accident reduction are generally overestimated on highway improvement projects analyzed with these accident prediction models.

Accident prediction models for roads with minor junctions

1996 ◽  
Vol 28 (6) ◽  
pp. 695-707 ◽  
Author(s):  
Linda Mountain ◽  
Bachir Fawaz ◽  
David Jarrett
Keyword(s):  
Prediction Models ◽  
Accident Prediction

Comprehensive Safety Assessment Model of Highway Section Based on the Accident Rate Prediction

2014 ◽  
Vol 1065-1069 ◽  
pp. 3372-3376
Author(s):  
E Lu ◽  
Jing Liang Xu
Keyword(s):  
Prediction Model ◽  
Safety Assessment ◽  
Prediction Models ◽  
Influence Factors ◽  
Assessment Model ◽  
Accident Rate ◽  
Analytic Hierarchy ◽  
Accident Prediction ◽  
Rate Prediction ◽  
Hierarchy Process

At home and abroad research, accident rate prediction model for highway safety assessment contains various influence factors, so it is difficult to find a comprehensive accident prediction model considering so much of them. In this article, we select appropriate accident prediction models through comprehensive literature research respectively about the highway alignment conditions, traffic conditions, interchange spacing conditions, then determine the weights of three accident prediction models using the analytic hierarchy process (AHP), finally obtain the comprehensive safety assessment model after the consistency check. Its’ feasibility is proved by a practical example.

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