Abstract:
Road accidents are a significant global issue leading to economic setbacks, fatalities, injuries,
and traffic congestion. The growth of urban areas and the increasing number of vehicles impact
transportation safety, with low-income countries experiencing much higher death rates compared
to high-income countries. Predicting and averting road traffic accidents (RTAs) and congestions
are crucial, particularly in developing countries. This study responds to the global challenge of
road accidents and traffic congestion, concentrating on low-income countries such as Ethiopia,
with a specific focus on Addis Ababa, where the fatality rates are disproportionately high.
The research reviews global literatures concerning road accidents and congestion, and assesses
prediction techniques aimed at improving road safety and reducing traffic congestion. It utilizes
GIS-based spatial statistics, crash rate analysis, advanced machine learning (ML) algorithms, and
Network-Based Analysis of Optimization Algorithms. The objectives are to identify existing
challenges and suggest ways to prevent road network accidents and congestion. Spatial crash rate
analysis and Getis-Ord-Gi* statistics, combined with spatial machine learning (ML) using a
random forest model (RF), are employed in this research to increase prediction accuracy. The
dataset used includes crash records from 2014 to 2019 in Addis Ababa comprising 64, 878 crash
incidents, road networks, and boundary shapefiles. Spatial network analysis is used to determine
safe routes, avoiding accident and congestion-prone road segments.
The key findings of this study include the accident hotspots identification through a combined
crash rates analysis and the successful prediction of RTAs, along with determining of safe routes
in Addis Ababa. The research maps crash locations and identifies significant RTA segments
associating them with the underlying road network. This analysis reveals notable hotspots on 10
roundabouts/squares, 33 road segments, and 3 intersections, with the gravest accident hotspot
averaging 37.5 annual number of crashes per kilometer during the 2014-2019 periods. The study
employs a spatial ML RF model, achieving 78% predictive accuracy, while spatial network
analysis determines alternative safe routes. These results demonstrate the methods strong
capability contributing to improved traffic safety by alerting drivers and travelers to potential
accident prone road segments and high traffic congestion risks.
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Results from the first method showed that hotspots in Addis Ababa are primarily located at
intersections and roundabouts in densely populated sub-cities of the downtown area, where
frequent conflicts occur between motor vehicles and other road users. The second method’s
outcomes highlight the need to determine safe routes based on crash frequencies and the
likelihood of accidents and congestion. Therefore, both the crash rate and predicted likelihood of
RTAs served to determine the safest travel route.
This study underscores the importance of integrating spatial analysis techniques, optimization
algorithms, and ML methods to reduce traffic congestion and promote road safety. Spatial and
ML predictive models are crucial in identifying accident-prone areas, while network analysis
provides recommendations to safer routes. The findings demonstrate that these methods enable
transportation authorities and stakeholders to quickly identify unsafe locations within the road
network, effectively prioritize hotspot areas, evaluate the association between traffic accidents
and congestions, and provide the safer route options to drivers. This method contributes research
on road safety and aids efforts in transport planning.
This research benefits transportation authorities, drivers, and travelers by offering practical
solutions for improving road safety, particularly in limited-resource urban areas. By prioritizing
safety measures and increasing awareness of accident-prone locations, the study aims to reduce
traffic accidents and alleviate congestion. It recommends further refining spatial and machine
learning predictive models with additional variables and real-time data sources expanding the
research to other urban areas to assess scalability, and exploring emerging technologies for data
collection and analysis to improve road safety and traffic management.