Traffic analysis and driver behavior study in highly populated city of low-income economies: A statistical approach

In highly densely populated cities within low-income cultural contexts, a unique relationship emerges among free-flow speed (FFS), traffic flow, and driver behavior. These underline the impact of diverse road conditions on these dynamics. This study presents a comprehensive examination of statistical analysis, which highlights numerous factors impacting FFS and traffic flow on the national highways of Bangladesh, along with the level of service (LOS). The statistical test includes multiple linear regression, one-way ANOVA, and other correlation tests to support the features and ranks. In this research, 59,425 vehicles were observed to determine the traffic flow and LOS. Additionally, 443 (N = 443) field survey data, including FFS and driver perception, were analyzed to identify the factors influencing FFS. Employing methodologies linking FFS to driver responses, the study found features such as vehicle type, road geometry, and accidents and incidents as the top influential factors for impacting FFS. However, features like heterogeneous conditions, seasonal variations, etc., did not support the predictions but showed similar influences on the process. The regression model stands out with an R-squared of 0.698, and the analysis also indicates LOS falls from the range of “D” to “E”, reflecting moderate to high levels of congestion at the Kanchpur segment. This research suggests valuable insights into the dynamic nature of traffic over time, considering driving experience and factors with individual effects.