Ethiopian Journal of Police Studies

Policing for Disaster Risk Management in Ethiopia

Sintayehu Legesse, Menkir Aklilu, Eyob Worku, Nadew Bekele and Tatek Geremew — 2026-04-23

This study examines effectiveness of Ethiopian police in disaster risk management. Data was collected from 384 respondents utilizing a mixed-methods triangulation design. Both Quantitative and qualitative data has been collected from community members, police officers, and disaster management personnel by using questioners and interview. Quantitative analysis has been done by using descriptive statistics and multiple linear regression model to assess the effectiveness of police in disaster risk management. Findings has indicted that police involvement in disaster risk management such as in emergency planning, preparedness and prevention is very low. Twenty six percent (26%) of respondents stated that police made inadequate participation in identifying vulnerable populations and evaluating risk related development strategies. Additionally, 33% thought that police involvement in the creation of early warning systems is very low. Furthermore, 40.1% reported as police was "very ineffective" and 29.43% of respondents stated as they were "ineffective," which means that almost 70% were unhappy with police disaster risk management practice. The results of regression analysis indicate that police disaster risk management practice has significant effect on the overall effectiveness of police disaster management. R² value of 0.95 implies that 95% of police effectiveness in disaster management variation is explained in disaster risk management phases. Interview results also confirmed that police actions are still reactive. This is due to inadequate inter-agency coordination, reactive engagement in formulation of strategies and resources constraints, and limited preparedness. Therefore, it is recommended to improve disaster training, resources mobilizations, inter-agency coordination, and proactive preparedness culture to improve police effectiveness in disaster risk managements. Findings of this research will be used for further research, education and policy reform.

Keywords: - Disaster risk management practice, police Challenge, police effectiveness in Disaster Management

Practices and Challenges of Road Traffic Management in the Selected Areas in Ethiopia

2026-04-23

The study was conducted to assess practices and challenges of road traffic management in Oromia, Amhara, SNNP, and two Federal City Administrations (Addis Ababa and Dire Dawa). To do so, data were collected through self-administered questionnaires and key informants’ interviews apart from the analysis of secondary data. Hence, the research found out that the federal government and regional states have separate road traffic management systems. Even the existing rules and regulations are not well integrated in a way to track drivers who have two or more driving licenses and cars with two or more plate numbers. This could also be revealed that organizational integrations among and between federal city administration and regional states are not friendly. The study has also identified that regional states and federal city administrations separately conducted several awareness creation programs about road traffic and the causes and consequences of road traffic accidents accordingly. Moreover, all federal and regional city administrations recruited assistant civil traffic officers from the community. Particularly, in Addis Ababa, the duties of handing over painting roads and monitoring and controlling road traffic management are not yet demarked. However, despite all the tasks these have done, road traffic accident is the leading causes of death and material causality across the study areas. Thus, researchers have recommended that both the federal government and regional states endorse uniform road traffic management policies and strong organization ties with friendly users of modern road traffic management systems.

Advancing Forensic Science through Artificial Intelligence: A Systematic Review

Hagos Yisak , Gadisa Adamu, Dereba Workineh and Messay Asgedom — 2026-04-23

Abstract

AI is the ability of a system to think based on experience. It simulates human reasoning, which is currently transforming forensic science and the criminal justice system. This systematic review was conducted to estimate the increasing trend of incorporating AI in the improvement of forensic sciences. The findings of the present study indicated that contemporary forensic science faces numerous challenges, including overwhelming volumes of data, trace evidence embedded in chaotic and complex environments, traditional laboratory structures and limited specialist knowledge. Together, these factors can hinder investigations and increase the risk of miscarriages of justice. Moreover, machine learning and deep learning are two types of AI that can help address these challenges. In a variety of forensic fields, neural networks and case-based reasoning are used to provide errorless, objective, and reproducible results. AI has recently been integrated into almost every major field of forensic sciences, using various approaches such as data analysis, pattern recognition, image processing, computer vision, data mining, statistical analysis and probabilistic methods, computational and mathematical methods, and graphical modeling. Furthermore, AI assists forensic experts and investigators by articulating logical evidence in Deoxyribonucleic acid (DNA) analysis, fingerprint analysis, footprint analysis, drug analysis, ballistics analysis, voice analysis, facial recognition to identify individuals in images and videos, three-dimensional (3D) reconstructions of crime scenes, autopsy, and cause of death determination. As a result, despite its hurdles, the incorporation of AI into forensic analysis constitutes a significant leap in the field of crime investigation, opening up new avenues for efficiency, accuracy, and scalability in solving complicated cases and providing justice.