A New Method for Identifying Traffic Accident Black Spots Avoiding Statistical Influence

Road safety is the main problem in developing countries. Every year, millions of people die in road traffic accidents, resulting in huge losses of humankind and the economy. This study focuses on the road traffic accident analysis and identification of black spots on the Lahore-Islamabad Highway M-2. Official data of road traffic accidents were collected from National Highway and Highway Police (NH & MP) Pakistan. The data was digitized on MS Excel and Origin Pro. The accident Point weightage (APW) method was employed to identify the black spots and rank of the top ten black spots. The analysis shows that the trend of road traffic accidents on M-2 was characterized by a high rate of fatal accidents of 35.3%. Human errors account for 66.8% as the major contributing factors in road traffic accidents, while vehicle errors (25.6%) and environmental factors (7.6%) were secondary and tertiary contributing factors. The main causes of road traffic accidents were the dozing on the wheel (27.9%), the careless driving (24.6%), tyre burst (11.7%), and the brakes failure (7.4%). Kallar Kahar (Salt Range) was identified as a black spot (223 km, 224 km, 225 km, 229 km, and 234 km) due to vehicle brake failure. The human error was a major contributory factor in road traffic accidents, therefore public awareness campaign on road safety is inevitable and use of the dozen alarm to overcome dozing on the wheel. Doi: 10.28991/cej-2020-03091629 Full Text: PDF

Download Full-text

A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods

Transportation Research Part A Policy and Practice ◽

10.1016/j.tra.2017.05.031 ◽

2017 ◽

Vol 103 ◽

pp. 106-117 ◽

Cited By ~ 13

Author(s):

Mehmet Ali Dereli ◽

Saffet Erdogan

Keyword(s):

Statistical Methods ◽

Traffic Accident ◽

New Model ◽

Black Spots

Download Full-text

Identification of Black Spots Based on Reliability Approach

PROMET - Traffic&Transportation ◽

10.7307/ptt.v25i6.1423 ◽

1970 ◽

Vol 25 (6) ◽

pp. 525-532 ◽

Cited By ~ 1

Author(s):

Ahmadreza Ghaffari ◽

Ali Tavakoli Kashani ◽

Sayedbahman Moghimidarzi

Keyword(s):

Reliability Analysis ◽

Traffic Safety ◽

Hot Spots ◽

Simulated Data ◽

New Method ◽

Empirical Bayesian ◽

Black Spots ◽

Engineering Problems ◽

The Mean ◽

Probabilistic Nature

Identifying crash “black-spots”, “hot-spots” or “high-risk” locations is one of the most important and prevalent concerns in traffic safety and various methods have been devised and presented for solving this issue until now. In this paper, a new method based on the reliability analysis is presented to identify black-spots. Reliability analysis has an ordered framework to consider the probabilistic nature of engineering problems, so crashes with their probabilistic na -ture can be applied. In this study, the application of this new method was compared with the commonly implemented Frequency and Empirical Bayesian methods using simulated data. The results indicated that the traditional methods can lead to an inconsistent prediction due to their inconsider -ation of the variance of the number of crashes in each site and their dependence on the mean of the data.

Download Full-text

PENINGKATAN KESELAMATAN JALAN PADA BLACK SPOT JALAN PROVINSI DI KABUPATEN SLEMAN

Jurnal Transportasi ◽

10.26593/jtrans.v21i2.5159.109-122 ◽

2021 ◽

Vol 21 (2) ◽

pp. 109-122

Author(s):

One Sigit Hermanto ◽

Agus Taufik Mulyono ◽

Latif Budi Suparma

Keyword(s):

Road Safety ◽

Traffic Management ◽

Traffic Accident ◽

Traffic Accidents ◽

Safety Evaluation ◽

Black Spot ◽

The Road ◽

Technical Requirements ◽

Black Spots ◽

Rumble Strip

Abstract The fatality rate of traffic accidents in Sleman Regency is increasing every year. This study aims to identify black spots and set priorities for repairing road infrastructure components needed to improve road safety on 3 provincial roads in Sleman Regency. The black spot is determined using the Accident Equivalence Number Method and the Upper Control Limit. The evaluation carried out resulted in the 3 worst segments on each observed road segment. The results of the road safety evaluation show that the technical implementation of traffic management and engineering, the technical use of road components, and the technicality of road equipment are the 3 technical requirements of the road with the lowest level of application. To improve road safety, this study recommends adding rumble strips, adding signs, relocating roadside hazards, and adding sidewalks and crossing zones. Keywords: fatality; black spots; traffic accident; road; road safety. Abstrak Tingkat fatalitas kecelakaan lalu lintas di Kabupaten Sleman meningkat setiap tahun. Penelitian ini bertujuan untuk mengidentifikasi black spot dan menetapkan prioritas perbaikan komponen infrastruktur jalan yang diperlukan untuk meningkatkan keselamatan jalan di 3 ruas jalan provinsi di Kabupaten Sleman. Black spot ditentukan dengan menggunakan Metode Angka Ekivalensi Kecelakaan dan Batas Kontrol Atas. Evaluasi yang dilakukan menghasilkan 3 segmen terburuk pada setiap ruas jalan yang diamati. Hasil evaluasi keselamatan jalan menunjukkan bahwa teknis penyelenggaraan manajemen dan rekayasa lalu lintas, teknis pemanfaatan bagian-bagian jalan, dan teknis perlengkapan jalan merupakan 3 persyaratan teknis jalan dengan tingkat penerapan terendah. Untuk meningkatkan keselamatan jalan, studi ini merekomendasikan penambahan rumble strip, penambahan rambu, merelokasi hazard yang terdapat di tepi jalan, serta penambahan trotoar dan zona penyeberangan. Kata-kata kunci: fatalitas; black spot; kecelakaan lalu lintas; jalan; keselamatan jalan.

Download Full-text

A Traffic Accident Predictive Model Based on Neural Networks Algorithm and Rough Set Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.947 ◽

2011 ◽

Vol 97-98 ◽

pp. 947-951 ◽

Cited By ~ 2

Author(s):

Qiao Ru Li ◽

Liang Chen ◽

Chang Guang Cheng ◽

Yue Xiang Pan

Keyword(s):

Neural Network ◽

Set Theory ◽

Rough Set ◽

Bp Neural Network ◽

Traffic Accident ◽

Rough Set Theory ◽

Road Traffic ◽

Black Spot ◽

Black Spots ◽

Neural Network Algorithm

The most important and critical step to improve road traffic safety is prediction and identification of traffic accident black spot. A new prediction model of traffic accident black spots is proposed based on GA-BP neural network algorithm and rough set theory. First of all, the traffic accident statistics of Jinwei Road in Tianjin are analyzed. With consideration of static road conditions, the samples of road accident black spots are obtained by the GA-BP neural network algorithm. Furthermore, an effective road traffic accident black spot prediction model is established by utilizing rough set theory with consideration of the impact of real time dynamic conditions. Finally, a numerical example is illustrated. Experimental results show that the proposed model with the combination of these two theories can reduce the hybrid and burdensome amount of data, lower the false alarm rate and improve the forecasting accuracy of accident black spots.

Download Full-text