Traffic Volume and Accidental Analysis of Blackspot Chogitti Intersection: A Quantitative and Qualitative Approach

Abstract

There is increase in number of accidents worldwide especially at accident-prone areas known as ‘Blackspots’. The specific case taken here is of Jalandhar city’s blackspot Chogitti intersection. The research intent to find out what are the reasons that contribute in formation of blackspots alongside ways to eliminate them, which forms central question/problem. There is need of this study because of alarming rate of increase of accidents, which is evident from the fact that by year 2030 accidents will be the fifth major cause of deaths around the world. The study is done by hybrid quantitative and qualitative sequential design approach. Among quantitative analysis traffic volume study is conducted by human enumerators. In qualitative part of analysis level of service and traffic flow is analyzed. This hybrid approach is supported by peculiar Human Error Assessment and Reduction Technique (HEART), which illuminates the probability of human error occurrence during data collection in quantitative phase. The question about reasons of accidents is answered, as there just about exist a causal relationship among traffic volume and accidents. Reasons that are observed include violation of traffic guideline and norms by Indian Road Congress (IRC), Highway Capacity Manual (HCM); below average quality, Level of Service (LOS); increase in volume causing increase in accidents. This research is unique as it touches qualitative aspect of accidents and try to combine its effect with the available traffic volume, which has never done before and yields better result due to widened horizon of data input, form and analysis approach.

Keywords: Human error assessment, reduction technique, blackspot Chogitti Jalandhar, traffic volume analysis, accidental studies, causation relationship

Cite this Article Malind Kumar, Ipandeep Singh, Harpreet Singh. Traffic Volume and Accidental Analysis of Blackspot Chogitti Intersection: A Quantitative and Qualitative Approach. Journal of Open Source Developments. 2020; 7(1): 24–32p.