Characteristics of Pedestrians in Bangladesh Who Did Not Receive Public Education on Road Safety

Abstract

The safety of pedestrians, such as workers who largely walk to and from work, has not been given sufficient attention, especially in the area of traffic safety in developing countries, including Bangladesh. Although the National Road Safety Strategy has a very strong emphasis on road safety education and publicity campaigns, the road safety knowledge may not have reached these vulnerable road users who most needed them. Moreover, little is known about the penetration rate of these campaigns and who have benefited or not benefited from them. On the other hand, the developing country, like Bangladesh, is heavily dependent on its Readymade Garment (RMG) workers for earning foreign currency, and walking is one of the major mode of transports of those workers. The objective of this study is to identify those who are not reach by the safety education. Results from a survey of 1020 RMG workers around Dhaka identified several socioeconomic, demographic, travel characteristics and accident experience that affect the most vulnerable segments who are left out of the system. The findings of this study would help the policy makers to arrange necessary road safety education for the most vulnerable cohorts of pedestrians to encourage the continued use of this sustainable mode of commute.

Correlation Analysis on Accident Injury and Risky Behavior of Vulnerable Road Users Based on Bayesian General Ordinal Logit Model

“…Natarajan P [8] also used MCA to study vehicle-pedestrian accidents from the Road Accident Data Management System (RADMS) database in 2016 in Chennai, summarizing patterns and correlations leading to accidents. Tanvir Chowdhury et al [9] carried out a survey of 1020 Bangladesh workers in Dhaka to identify the characteristics of vulnerable road users. Moreover, Lei Yang et al [10] performed a comparative analysis of different models using Bayesian Optimization for pedestrian fatality prediction, trying to improve traffic safety of pedestrians from the approach of machine learning.…”