Study on Factors Affecting Pedestrian Crashes in Visakhapatnam Urban Area Using GIS

Abstract

In the past few years, pedestrian crashes have been are increasing in most of the developing countries, including India. Because of the quick urbanization increment in the vehicular rush hour gridlock, people on foot in urban areas need to search for space for development on streets, especially while crossing at mid squares. There are many factors that cause pedestrian crashes. Among that, roadway and traffic conditions, vehicle and driver characteristics, physical environment conditions, pedestrian behavior, other surrounding variables etc. By analysing these factors, we can reduce the number of accidents. The present study is to identify the factors which are influencing the mid-block pedestrian crashes and to develop regression models, which predict the pedestrian crashes. The crash data is collected for the different mid-blocks of the selected segments. Data was collected for the years
2014-2016 from Visakhapatnam city police stations in terms of total number of pedestrian crashes, fatal pedestrian crashes, number of non-fatal crashes, number of day time and night time crashes. This thesis also describes the effective usage of traffic police data in a Geographical Information System (GIS) analysis and interpretation. The road network of the city National Highway(NH)16 stretch from Tagarapuvalasa to Lankelapalem was created by using Google earth for Visakhapatnam city and then imported as a shape file in ArcGIS. Later with the help of Arc Map, the final location of accident has been determined. Every possible attempt has been made to locate the accident spot in terms of latitude and longitude as accurate as possible. These maps are output from ArcGIS software and
these maps also helped to decide whether the crash is on mid-block or on intersection by GIS query. After that Traffic safety audit was conducted in order to identifying the factors affecting pedestrian crashes. In this audit all the midblock sections of entire stretch was surveyed by observing number of bus stops, number of pedestrian crossings, type of land use, speed limits, number of side access roads etc. These data are analysed, and a Multiple Linear Regression model, Poisson model is developed by using various mid-block crash parameters. The model was to developed to show the relation between crash rate and various mid-bock parameters from regression analysis.