A Mixed-Method Proposal for Traffic Hotspots Mapping in African Cities using Raw Satellite Imagery

Abstract

Road traffic fatalities disproportionately affect low-and middle-income countries. This research provides a method that helps cities in developing countries to use their limited resource to control accident-prone locations with satellite data insights. The proposed method is a mixed approach from both transport and the emerging machine learning discipline. In the first step, accident spots labeled using the Weighted Severity Index (WSI) with 14 risk factors that potentially influence the occurrence of an accident. Then, the computer is trained to look for blackspots using the labeled geoinformation data obtained from the WSI analysis. This cuttingedge method is called transfer learning with Convolutional Neural Networks (CNNs), which is the knowledge gained from previous training uses to identify a similar problem to a new location. The method is an inexpensive and reliable blackspot identifying solutions that extract data insights from freely available satellite imagery and open-source data.