Road Traffic Accident Analysis and Identification of Black Spot Locations on Highway

Abstract

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

Public acceptance of driver state monitoring for automated vehicles: Applying the UTAUT framework

“…Since 'human error' is the main cause of road traffic collisions, there have been attempts to minimize unfavorable driver behaviour in vehicles. In one example, researchers used more than a dozen alarms to prevent drivers from becoming overly fatigued (e.g., falling asleep) (Iqbal et al, 2020). These same lessons can likely be applied to ensure the safe operation of partially and conditionally automated vehicles.…”

Risks for bicycle-related injuries in Al Ain city, United Arab Emirates

“…Nevertheless, these questionnaires usually report desirable behaviors, and there is the risk of recall bias which limits their objectivity [7] . Other new methods of observation of bicyclists, as yet not used in the UAE context, include monitoring by moving aerial cameras, video-based observation, or black spot identification [6,41,42] . However, such approaches would have the same limitations as our current methods.…”

“…[7] Other new methods of observation of bicyclists, as yet not used in the UAE context, include monitoring by moving aerial cameras, video-based observation, or black spot identification. [6,41,42] However, such approaches would have the same limitations as our current methods. We do not have access to detailed traffic injury police data, accordingly, we were unable to assess the relationship between cycling-related injuries and unsafe cycling behaviors.…”

Identify Discriminatory Factors of Traffic Accidental Fatal Subtypes using Machine Learning Techniques

“…The most relevant factors related with driver injury severity were identified using a Multinomial Logit model [16]. Human error was highlighted as a major contributory element in road traffic accidents by Asad Iqbal et al (2020), and the Salt Range was classified as a black spot-on account of vehicle braking failure [17]. Minglei Song et al suggested a road accident prediction model based on joint probability density feature extraction from big data in 2019 [18].…”