INVESTIGATING THE PERCEPTION, BEHAVIOUR AND SAFETY OF PEDESTRIANS NEAR URBAN BUS STOPS

Abstract

The transportation facilities are used for the efficient transfer of goods and services. Even though the safety is the major concern for a transportation engineer, there are many conflicts occurs creating severity to pedestrians and vehicular users due to lack of proper safety measures. Among the road users the pedestrians are the ones more vulnerable to conflicts. When conflicts occur, pedestrians came to direct impact without any protection rather than other road users such as vehicle users and passengers having some means of protection. Many studies have been done related to safety of pedestrians near intersections where the violation behaviour is more. But there is lack of study in midblock locations which was identified as the gap for study. The study mainly aimed to analyse the perception of pedestrians towards bus stop, crosswalk and sidewalk and to find out the violation behaviours while using these facilities followed by model creation to find out the safety of pedestrians near urban bus stops. The data collected through questionnaire and video were used for analysis of perception of pedestrians towards each facility, violation behaviour and model creation. The modelling was done with 2 machine learning techniques Regression and Classification using various algorithms at initial stage to find out the best model with more performance. The attributes selected for the model creation for predicting PET (Post Encroachment Time) includes the perception data, behavioural data and vehicular data with a total count of 33 variables. The initial stage analysis done by separating 4 regression variables and 29 classification variables. The analysis indicates 9 variables are influencing PET, based on which best machine learning models were created using Random Forest giving R2 Value of 0.84 and MSE of 0.55 for regression and Accuracy of 0.82 and better F1 Score for classification. Also, the weightage of factors was derived using the best model and used for development of Safety Index, which indicates the safety level of bus stop with help of K-means Clustering in SPSS. Also, the required recommendations for improving the safety level of bus stops were given for selected 10 bus stops for the study