Accident Prediction By Using Poisson Regression Models For Unsignalised Junction Of Khulna Metropolitan City, Bangladesh

In this study, the Poisson regression models were used in the large area of application and obviously promote the quality and important for the engineering site of accident prevention in Khulna metropolitan city. A wide range of accident data for each junction for the fifteen-year period (2000-2015) was used in the model development process. In this model, number of accident is used as an outcome variable and it notify the accidents number occurred in different types of junctions in specified year, total accident is used as a continuous predictor variable and parameter is a categorical predictor variable with this three types of junction. The result shows the developed model evaluate 1. 80 times more dangerous at crossing junctions than at T junctions. For lacking of available dedicated left turning lane and standard deviation of traffic spot speeds vehicle on the roads generally related with accident severity at all the three junctions.

Introduction

Road traffic accident is one of the most undesirable situations to occur to a road user resulting harmful to the people and damages to the valuable properties. Now road traffic accident is the global phenomenon to the almost all countries of the world seriously concerned about the increasing number of people killed and injured on their highways. According to the World Health Organization (WHO), more than 1. 25 million people lives are cut short for the result of road traffic accident every year. More people between 20 and 50 million suffer simple or non- fatal injuries, with many incurring a disability as a result of their injury. For Bangladesh, accident severity and fatality in road accident is now an alarming issue. In Bangladesh Khulna metropolitan city is the 3rd largest city with an area of 45. 65 square kilometers and more than 1. 022 million people live here. In this city, Total numbers of roads are 1215 with a total length of 356. 65 kilometers. Recently significant numbers of killed and injured people are increasing enormously in Khulna metropolitan city. For transportation safety, Road traffic accident prediction models are very necessary tools given their effective for evaluating both the frequency of accident frequency and the contributing factors that could then be determined by transportation methods. To determine the accident frequency, accident severity level and different factors which are generally responsible for road traffic accident, transportation authority and many research institute are often want to accident data and factor to identify the most vulnerable road environment site. The different regression model including Poisson regression model helps to explain the relationship between accident occurrence and responsible risk factor. Though multiple linear regression models are widely used for prediction, it has been found that the Poisson regression model can often be better fitted for prediction accident occurrence. The development of generalized theories concerning highway safety, road traffic accident prediction models can also keep important contribution. Poisson regression has been often applied for count road traffic and different transportation policies including frequency.

Generally Poisson regression has more advantages than other regression also related to different discrete distribution and constraint to predicted value to non-negative integer number. In accident frequency analysis for different platform, almost all the researcher has normally applied Poisson regression as a beginning point for analysis, they have often found that the model data exhibit over and under dispersion that make the application of Poisson regression model problematic (Lord and Mannering, 2010). As accidents are not occurred frequently, categorical style of accident data is more efficient for expression of accident reaction and using this categorized accident data Poisson regression model make the taste significance as fixed value.

The situation of road accident in Khulna metropolitan city is actually frightful and the death of lives and infrastructure damages are desired to continue if necessary corrective measures are not taken accordingly by applying proper engineering measures through proper research. These situations for all metropolitan cities in Bangladesh are very dangerous. About 20 percent of road accident occurred in metropolitan cities viz. Dhaka, Chittagong, Khulna and Rajshahi. So, it is more important for Khulna city to prediction of road accident considering the basic factors including different types of junctions of those reasons accident in this area.

Methodology

Five police stations: Khulna sadar, Daulatpur, Khalishpur, Khanjahan Ali and Sonadanga were used for accident data collection with the year 2000 to 2015 from the Khulna metropolitan police (KMP) head quarter and Accident Research Institute (ARI), BUET.

IBM SPSS statistics 22 software is used for the development of Poisson regression model. In this study, number of accidents is used as the outcome variable and it indicates the number of accidents occurred in different types of junction in a year, total accidents is used as a continuous predictor variable and parameter is used as predictor variable with three type of junctions.

The performance of different statistical model for given data is measured by Akaike’s Information Criterion. Akaike’s Information Criterion (AIC) compares the models quality relative to each other for their collective data. Therefore, AIC is widely used for the selection of better model. Smallest value of AIC indicates the best model.

Bayesian Information Criterion (BIC) is generally used for model selection among finite data sets of different model. BIC is the exponential function and small parts of a likelihood function are closely related to Akaike’s Information Criterion (AIC). Smallest value of Bayesian Information Criterions (BIC) indicates the best model.

Results and Discussion

The Poisson regression model was developed together for X-junction, Crossing junction and T-junction. These junctions were to conform that the models would satisfy accurately for different types of accident and risks associated to different highway design construction. Therefore, it is trying to maximum; a secondary better models were choose and presented to qualify comparison.

In the year 2000 to 2015 total 335 accidents were recorded: the total 231 accidents were recorded at sites of Not junctions, the total 40 accidents were recorded at the sites of T junctions and total 60 accidents were recorded at the sites of crossing junction in the Khulna metropolitan city. This model can be used only for approximate determinates of accident severity of Khulna city, as only junction type is used and most of the parameter and variable are not included for developing this model. Each variable contains 16 legal observations and from their distributions it indicates perfectly reasonable. The outcome variable for unconditional mean and variance are not immensely different. These values and predictor variable for different condition will be equal by the model assumption.

It was also found that the Akaike’s Information Criterion (AIC) of this model 55. 669 and Bayesian Information Criterions (BIC) equal to 58. 760. Model also evaluates the deviance 20. 20 as Chi-square distributed with the 12 degrees of freedom. Lowest value of both AIC and BIC indicates the developed model is the best model.

The regression co coefficient for total accident was found 0. 082. This indicates that the expected increase in the log count for one unit increase in total accident is. 082. It was observed from the model effects that the outputs of this study completely appropriate. The developed model evaluated 2. 19 times more dangerous for road accident at Not junctions than at T junctions and 1. 80 times more dangerous at Crossing junctions than at T junctions. So Not Junction is more dangerous for occurring accident among these three junctions in Khulna metropolitan city.

This Poisson regression model is the similar as that used in ordinary regression model except that the random component is the Poisson distribution. The Poisson Regression model prediction is now and then mentioned as a Poisson Log linear model. Most of the regression model provides better for over dispersed data while Poisson regression model provides better for equal- dispersion data.

Conclusions

The study of this examination was carried out the ability of Poisson regression model for prediction of accident in Khulna metropolitan city, Bangladesh. This paper developed the traffic accident prediction models to gather a better knowledge by using this useful systems for predict the road traffic accidents including their hazard factors. From the results it was found that the developed model evaluated 2. 19 times more dangerous for road accident at Not junctions than at T junctions and 1. 80 times more dangerous at Crossing junctions than at T junctions. So Not Junction is more dangerous for occurring accident among these three junctions in Khulna metropolitan city. Finally this model can able to predict the road accident at unsignalised junction of Khulna metropolitan city, Bangladesh.