ACCIDENT BLACK SPOTS IDENTIFICATION AND EVALUATION OF RAWALPINDI – JHELUM NATIONAL HIGHWAYS (N-5) SECTION

Abstract

Road safety is a major issue in Pakistan. It has been estimated that every year about 6 thousand persons are killed and more than 13 thousand are injured in road accidents. Road traffic injuries causes emotional, physical and economic burden. The economic cost of road crashes and injuries is estimated to be over 100 billion rupees for Pakistan. On the other hand, as per 1998 studies, Pakistan has spent $0.07 per capita (0.015% of GDP/capita) on road safety. Road Safety can not be improved without planned and continuous measures utilizing a methodical approach. The risks can be understood and therefore can be prevented. The primary objective of the study is to identify factors that contribute to the cause of accident and the development of an accident prediction model for one of busiest road section of Pakistan National Highway (N-5) between Rawalpindi to Jhelum. For this purpose, historical road traffic accidents (RTAs) data is collected from National Highway and Motorway Police (NH & MP) from 2006 to 2009. The accident data depicts that fatal accidents account 34% of the total accidents amongst which private vehicle category contribute 50.6% towards the fatal accidents. Reckless driving is identified as the most important factor in RTAs accounting for over 35% of all accidents. Pedestrians were the second most common cause accounting for over 24% of all accidents over period of the study. Encroachments and unmanaged bus stops have deteriorated road’s efficiency and level of service forcing the pedestrians to come to high speed lanes. Slow moving motorized vehicles such as motorcycles and Quingji are mixing with the high speed traffic. These observations not only restrict the traffic flow, but also putting the road user life at a greater risk. Based on the intensity and frequency of RTAs, four places have been identified as accident blackspots. The crash patterns of these blackspots have been depicted by collision diagrams. Furthermore, based on collision diagrams, remedial measures are suggested for each case. By providing attractive overhead bridges, underpasses, speed signs, proper road markings, traffic calming devices and strict enforcements can prove to be a viable solution to reduce the number of accidents. Finally, using Multiple Linear Regression analysis, accident prediction model is developed for the specified road section. The dependent variables in the proposed model are the number of accidents, while the independent variables are traffic volume, 85th percentile speed, number of access points and gap. The statistical analysis is performed using SPSS software. The model has an R-square of 0.999, which means that 99.9% of the variation in the number of accidents has explained the regression line. It is concluded that one access point reduction can reduce accidents up to 4%, speed reduction of 5 km/h can reduce accidents up to 9%. Decrease in volume of 250 veh/h can reduce accidents up to 13% and one second increment in can reduce accidents by 4%.