Spatiotemporal modeling and mapping of traffic crashes using R programming and GIS: a case study of Bole sub city, Addis Ababa, Ethiopia – Nature

Report on Spatiotemporal Analysis of Traffic Crashes in Bole Sub-City, Addis Ababa, Ethiopia, in the Context of the Sustainable Development Goals

Executive Summary

Road traffic crashes represent a significant global public health crisis and a major obstacle to achieving the Sustainable Development Goals (SDGs), particularly in developing nations like Ethiopia. This report presents a spatiotemporal and explanatory analysis of 17,285 traffic incidents in Bole Sub-City, Addis Ababa, from 2020 to 2022. The findings are framed to provide evidence-based insights for interventions aligned with SDG 3 (Good Health and Well-being), specifically Target 3.6, which aims to halve road traffic deaths and injuries, and SDG 11 (Sustainable Cities and Communities), which calls for safe and sustainable transport systems. The analysis identifies high-risk locations, times, and demographic groups, revealing that crashes are concentrated at intersections, peak during summer months and weekends, and disproportionately involve young, male drivers. Vehicle-to-vehicle collisions causing property damage are most common. Recommendations focus on improving data systems, upgrading infrastructure in hotspots, and implementing targeted educational campaigns to advance road safety and contribute to the global SDG agenda.

1.0 Introduction: Road Safety as a Global Development Challenge

Road traffic crashes are a critical impediment to sustainable development, inflicting severe human and economic costs worldwide. The World Health Organization (WHO) reports 1.19 million annual fatalities, with 90% occurring in low- and middle-income countries (LMICs). This disparity undermines progress towards SDG 3 (Good Health and Well-being) and highlights a global equity issue. In Ethiopia, the fatality rate of 4,984.3 deaths per 100,000 vehicles starkly illustrates this challenge. Addis Ababa, the nation’s capital, accounts for over 60% of these incidents, with Bole Sub-City emerging as a critical crash-prone zone due to its role as a major commercial and transport hub. A lack of localized, data-driven analysis has hindered the development of effective safety interventions. This study addresses this gap by providing a comprehensive analysis of crash patterns in Bole Sub-City, aiming to inform policies that support SDG 11.2 by creating safer, more accessible, and sustainable transport systems for all road users.

2.0 Methodology for Evidence-Based Intervention

To generate actionable insights for achieving SDG targets, a comprehensive methodological approach was employed, integrating spatial, temporal, and explanatory analyses.

2.1 Data Sources and Preparation

Secondary data on 17,285 traffic crashes from 2020-2022 were obtained from the Bole Sub-City Police Department. The dataset included both spatial (crash locations, road networks) and non-spatial (date, time, severity, driver demographics) attributes. Data preprocessing involved cleaning, standardizing, and geocoding crash locations to create a unified dataset suitable for robust analysis. This systematic data handling is foundational for monitoring progress towards SDG 3.6.

2.2 Analytical Framework

The analysis was structured to identify key risk factors and patterns:

• Explanatory Analysis: Examined relationships between crash severity and driver demographics (age, gender, education) to identify vulnerable groups, informing targeted educational strategies under SDG 4 (Quality Education).
• Temporal Analysis: Assessed yearly, monthly, daily, and hourly crash trends to identify high-risk periods for focused enforcement and resource allocation.
• Spatial Analysis: Utilized Kernel Density Estimation (KDE) in ArcGIS to identify crash hotspots, providing critical data for infrastructure improvements aligned with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11.

3.0 Results and Discussion: Aligning Findings with SDG Targets

3.1 Overall Crash Trends and Demographics

A statistically significant decline in total crashes was observed from 6,450 in 2020 to 5,128 in 2022. However, the burden remains high, directly impacting public health (SDG 3).

• Gender Disparity: Male drivers were involved in approximately 93.9% of crashes. This highlights the need for gender-sensitive analysis and targeted awareness campaigns, contributing to the broader goals of SDG 5 (Gender Equality) by addressing specific risk behaviors.
• Education Level: Drivers with higher education levels were paradoxically associated with a higher proportion of fatal crashes (0.57), suggesting that conventional education does not guarantee safer driving. This points to a need for specialized road safety education as part of lifelong learning initiatives (SDG 4).

3.2 Crash Characteristics and Severity

Understanding the nature of crashes is vital for designing effective prevention strategies.

1. Crash Types: Vehicle-to-vehicle collisions were predominant (over 86%), reflecting challenges in managing traffic flow in a dense urban environment. A decline in vehicle-to-pedestrian crashes suggests positive returns from infrastructure investments, supporting SDG 11.2. However, an increase in vehicle-to-inanimate object crashes points to potential issues with road design and driver distraction.
2. Severity Levels: Property damage-only crashes constituted the majority of incidents (78.7%). While serious injuries declined, minor injuries increased, and fatal crashes fluctuated without a clear downward trend. This underscores the ongoing challenge in meeting the ambitious targets of SDG 3.6.

3.3 Temporal and Spatial Patterns

3.3.1 High-Risk Time Periods

Temporal analysis revealed distinct periods of elevated crash risk:

• Time of Day: The highest crash frequencies occurred during morning (6 AM–12 PM) and afternoon (12 PM–6 PM) peak traffic hours.
• Day of the Week: Weekdays, particularly Fridays, saw the highest number of incidents, correlating with commuter and commercial activity.
• Month of the Year: Crashes peaked during the summer months (August-September), a period associated with heavy rainfall in Ethiopia, which can impair visibility and road conditions.

3.3.2 Spatial Hotspot Identification

Spatial analysis confirmed that traffic crashes are not randomly distributed but are highly concentrated in specific areas. Major intersections, commercial centers, and transportation hubs such as Bole Michael and Gerji were identified as critical hotspots. This geographic concentration provides a clear roadmap for prioritizing infrastructure upgrades and targeted enforcement, essential for building safer and more resilient cities as envisioned in SDG 11.

4.0 Conclusion and Recommendations for Sustainable Road Safety

4.1 Conclusion

This study confirms that traffic crashes in Bole Sub-City exhibit predictable spatial, temporal, and demographic patterns. The identification of hotspots at major intersections, high-risk periods during peak traffic hours and weekends, and the disproportionate involvement of young male drivers provides a clear evidence base for targeted interventions. Addressing these challenges is not merely a transportation issue but a fundamental component of achieving sustainable urban development, public health, and social equity, directly contributing to SDG 3, SDG 11, and related goals.

4.2 Recommendations

Based on the findings, the following recommendations are proposed to enhance road safety and advance the Sustainable Development Goals in Bole Sub-City:

1. Enhance Infrastructure for Safer Cities (SDG 9 & SDG 11): Prioritize infrastructure upgrades in identified hotspots, including the installation of modern traffic signals, improved pedestrian crossings, and clear signage to reduce conflicts between road users.
2. Strengthen Data Systems for Monitoring (SDG 17): Establish a centralized, digitized traffic crash database to enable continuous monitoring of trends and data-driven evaluation of interventions, fostering partnerships between law enforcement and urban planners.
3. Implement Targeted Education and Awareness Campaigns (SDG 4): Develop and deploy road safety campaigns specifically targeting high-risk groups, such as young and male drivers, focusing on defensive driving and risk awareness.
4. Intensify and Optimize Law Enforcement: Increase traffic law enforcement during identified high-risk periods (weekends and peak hours) to deter unsafe driving behaviors.
5. Promote Sustainable and Safe Mobility: Integrate findings into urban planning to promote safer modes of transport, such as improved public transit and dedicated infrastructure for pedestrians and cyclists, directly supporting the vision of SDG 11.2.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article on traffic crashes in Addis Ababa, Ethiopia, directly and indirectly addresses several Sustainable Development Goals (SDGs). The primary focus on road safety as a public health issue, combined with its analysis of urban infrastructure, data systems, and driver behavior, connects to the following goals:

• SDG 3: Good Health and Well-being: The article frames road traffic crashes as a “significant public health concern globally,” citing high fatality and injury rates. It discusses the “loss of life, injuries, and significant economic harm” caused by these incidents, directly aligning with the goal of ensuring healthy lives and promoting well-being.
• SDG 11: Sustainable Cities and Communities: The study is centered on an urban area (Bole Sub-City, Addis Ababa) and analyzes the impact of “rapid urban growth, dense traffic, and expanding road networks.” It highlights the need for safe transport infrastructure, identifies crash hotspots at intersections, and considers the safety of “vulnerable road users” like pedestrians, which are all central themes of making cities inclusive, safe, resilient, and sustainable.
• SDG 9: Industry, Innovation, and Infrastructure: The article’s recommendations focus on building resilient infrastructure. It calls for “upgrading road infrastructure in high-risk areas” and implementing technological solutions like “real-time monitoring and adaptive signal control systems.” This connects to the goal of developing quality, reliable, and sustainable infrastructure to support well-being.
• SDG 4: Quality Education: The analysis explores the relationship between crash involvement and driver demographics, including “education level.” Furthermore, a key recommendation is to implement “educational campaigns to promote safer driving behaviors, particularly among vulnerable groups” such as young and inexperienced drivers, linking directly to the goal of providing inclusive and equitable quality education and promoting lifelong learning opportunities.
• SDG 16: Peace, Justice, and Strong Institutions: The article underscores the need for effective governance and strong institutions to manage road safety. It recommends “stricter enforcement of traffic regulations” and highlights the importance of evidence-based policymaking by calling for “improving data collection systems for continuous monitoring” and establishing a “centralized and digitized traffic accident database.” These points relate to building effective, accountable, and transparent institutions.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the issues discussed, several specific SDG targets can be identified:

1. SDG 3: Good Health and Well-being

   • Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidents.
     
     Explanation: The entire article is dedicated to analyzing the causes and patterns of road traffic crashes to reduce their frequency and severity. It explicitly mentions the “alarmingly high” fatality rate in Ethiopia and provides detailed statistics on deaths and injuries, making this target the most central and directly relevant one.

2. SDG 11: Sustainable Cities and Communities

   • Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations…
     
     Explanation: The study’s focus on an urban sub-city, its identification of crash hotspots at intersections and commercial centers, and its analysis of vehicle-to-pedestrian crashes directly address the need for improved road safety within urban transport systems. The mention of “vulnerable road users” aligns perfectly with the target’s emphasis on protecting those most at risk.

3. SDG 9: Industry, Innovation, and Infrastructure

   • Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure… to support economic development and human well-being, with a focus on affordable and equitable access for all.
     
     Explanation: The article identifies that crashes are “highly concentrated around squares and intersections” and recommends “physical infrastructure improvements” such as traffic lights, pedestrian crossings, and speed bumps. This directly relates to the need for developing higher quality and more resilient road infrastructure to ensure safety and well-being.

4. SDG 4: Quality Education

   • Target 4.7: By 2030, ensure that all learners acquire the knowledge and skills needed to promote sustainable development, including, among others, through education for sustainable development and sustainable lifestyles…
     
     Explanation: The finding that “younger, less experienced, and male drivers are most frequently involved in crashes” leads to the recommendation for “educational campaigns” targeting these high-risk groups. This aligns with the target of providing education that promotes sustainable lifestyles, which includes safe and responsible behavior on the road.

5. SDG 16: Peace, Justice, and Strong Institutions

   • Target 16.6: Develop effective, accountable and transparent institutions at all levels.
     
     Explanation: The study’s recommendation to establish a “centralized and digitized traffic accident database” and improve “data collection systems for continuous monitoring” is a direct call to strengthen institutional capacity. The goal is to enable evidence-based policy and transparent monitoring of road safety, which is a core component of effective institutions.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article mentions and implies several quantitative and qualitative indicators that can be used to measure progress towards the identified SDG targets:

1. For Target 3.6 (Halve road traffic deaths and injuries):

   • Indicator 3.6.1: Death rate due to road traffic injuries.
     
     Explanation: The article explicitly provides this indicator, stating the fatality rate in Ethiopia is “4,984.3 deaths per 100,000 vehicles annually.” It also provides absolute numbers of deaths (4,161 in 2021), injuries (over 10,000), and total crash incidents (17,285 over three years), which are direct measures for this target.

2. For Target 11.2 (Provide safe transport systems):

   • Implied Indicator: Number and density of traffic crashes in urban areas, particularly at hotspots.
     
     Explanation: The article uses Kernel Density Estimation (KDE) to identify and visualize “crash hotspots,” which are “highly concentrated around squares and intersections.” The number of crashes, injuries, and fatalities within these specific high-risk zones serves as a direct measure of the lack of road safety in the urban transport system.
   • Implied Indicator: Number of vehicle-to-pedestrian crashes.
     
     Explanation: The article tracks the frequency of different crash types, including “vehicle-to-pedestrian crashes.” Monitoring this specific statistic is a key indicator of safety for vulnerable road users, as mentioned in Target 11.2.

3. For Target 9.1 (Develop quality infrastructure):

   • Implied Indicator: Rate of crashes at specific infrastructure points (e.g., intersections, squares).
     
     Explanation: The finding that crashes cluster at major intersections and squares implies that these infrastructure points are of lower quality or are inadequately designed for the traffic volume. Tracking the reduction of crashes at these locations after “infrastructure improvements” would be a key performance indicator.

4. For Target 4.7 (Education for sustainable development):

   • Implied Indicator: Crash involvement rate by demographic group (age, gender, experience).
     
     Explanation: The article provides baseline data by identifying that “younger, less experienced, and male drivers are most frequently involved in crashes.” This data can be used as an indicator to measure the effectiveness of targeted “educational campaigns” by tracking changes in crash rates among these specific groups over time.

5. For Target 16.6 (Develop effective institutions):

   • Implied Indicator: Quality and accessibility of traffic crash data.
     
     Explanation: The study itself relies on data from the Bole Sub City Police Department. The recommendation to create a “centralized and digitized traffic accident database” implies that the current system is inadequate. Therefore, the completeness, accuracy, and accessibility of this data serve as an indicator of institutional effectiveness and transparency.

4. Table of SDGs, Targets, and Indicators

Source: nature.com