Integrating Wireless Sensor Networks and Machine Learning for Proactive Safety and Operational Risk Management on the Addis Ababa–Djibouti Transport Corridor
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Abstract
The Addis Ababa–Djibouti corridor serves as Ethiopia's economic lifeline, handling approximately 95% of the nation's international trade. Despite its strategic importance, the corridor faces significant safety and operational challenges, with heavy goods vehicles involved in 6,914 crashes (14.85% of the total) in 2024, resulting in 715 fatalities. Railway operations are constrained by outdated signaling systems, limiting capacity to only two trains between stations. This study develops an integrated framework combining wireless sensor networks (WSNs) and machine learning for proactive safety monitoring and operational risk management along the corridor. The methodology employs a multi-tier WSN architecture with edge and cloud computing, utilizing gravitational search algorithms for optimal node placement. Machine learning models, Random Forest, XGBoost, and LightGBM, were trained on 2,000 accident records (2018–2023), with SMOTE addressing class imbalance. Random Forest achieved 82% accuracy (AUC-ROC: 0.87), identifying driver age (mean 44 years), nighttime on unlit roads (+62% fatal probability), and rainy conditions as key severity predictors. The proposed WSN-based railway signaling enables 3–4 trains to operate between stations simultaneously, representing a 50–100% capacity increase. Integration of sensor networks and ML enables a fundamental shift from reactive to proactive risk management, with estimated benefits including 15–25% accident reduction, 20–30% maintenance cost savings, and 15–25% extension of infrastructure lifespan. The framework directly aligns with the World Bank’s USD 500 million, Regional Economic Corridor Project and offers a scalable, replicable model for deploying intelligent transport systems across developing economies. This research contributes the first integrated sensor-ML framework for an African trade corridor, offering practical evidence for policy formulation and technology investment prioritization.
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Goshu, B. S., & Muhammad Ridwan. (2026). Integrating Wireless Sensor Networks and Machine Learning for Proactive Safety and Operational Risk Management on the Addis Ababa–Djibouti Transport Corridor. Economit Journal: Scientific Journal of Accountancy, Management and Finance, 6(3), 300-323. Retrieved from http://biarjournal.com/index.php/economit/article/view/1577
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References
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Beyene, S., Mohammed, J., Wodajo, B., & Brijs, T. (2025). Predicting car accident severity in Northwest Ethiopia: A machine learning approach leveraging driver, environmental, and road conditions. Scientific Reports, 15, Article 21913. https://doi.org/10.1038/s41598-025-XXXXX
Capital Ethiopia. (2026, March 8). Ethiopian pays more for commodities. Capital Ethiopia. https://capitalethiopia.com
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
DW. (2024, December 30). Ethiopia: At least 71 dead after truck plunges into river. DW. https://www.dw.com
ECCAS. (2026). Non tariff barriers: Galafi border corridor complaints. https://eccas.tradebarriers.africa
Engineering News. (2026, April 17). AI optimises logistics solutions. Engineering News. https://www.engineeringnews.co.za
Ethiopian Federal Police Commission. (2024). Annual crash report 2024. [Unpublished internal report].
Fenta, T. G., Tsegaye, E., & Fayaz, F. (2025). Wireless sensor network node deployment strategy in the case of Ethio-Djibouti railway. Bahir Dar University Journal of Engineering and Technology, 3(1). https://doi.org/10.20372/pjet.v3i1.1877
Grokipedia. (2026). Addis Ababa–Djibouti Railway. https://grokipedia.com
Hamdan, N., & Sipos, T. (2025). Advancements in machine learning for traffic accident severity prediction: A comprehensive review. Periodica Polytechnica Transportation Engineering, 53(3), 347–355. https://doi.org/10.3311/PPtr.40369
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
Hodge, V. J., O'Keefe, S., Weeks, M., & Moulds, A. (2014). Wireless sensor networks for condition monitoring in the railway industry: A survey. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1088–1106. https://doi.org/10.1109/TITS.2014.2368112
Husain, S. F., Tutumluer, E., & et al. (2023). Towards a wireless sensing infrastructure for smart mobility. Transportation Geotechnics, 40, 100985. https://doi.org/10.1016/j.trgeo.2023.100985
IEEE. (2024). Strategic sensor deployment in traffic networks for enhanced traffic flow and pollution analysis through global search optimization. IEEE Xplore. https://ieeexplore.ieee.org
IEEE. (2025). Linear wireless sensor networks: Innovations in topology specific MAC protocols and real world applications. IEEE Xplore. https://ieeexplore.ieee.org
IEEE. (2026). Multi class road traffic accident severity prediction using machine learning and neural networks: A comprehensive study with imbalance aware techniques. IEEE Xplore. https://ieeexplore.ieee.org
Import Global. (2024). Ethiopia import data 2024. https://importglobals.com
Kumar, I., & Maitri, V. (2026). Pathways for ITS adoption in developing economies. In Smart Freight. https://doi.org/10.4324/9781003658856-13
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767 Abstract.html
MDPI. (2017). Promoting the quality level of signaling in railway transportation system taking advantage from wireless sensor networks technology. Sensors, 17(8). https://doi.org/10.3390/s17081864
MDPI. (2025). IoT architecture for inclusive urban mobility: A design science research approach to sustainable transportation in Morocco. Smart Cities, 8(5). https://doi.org/10.3390/smartcities8050000
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742 1222240302
PLOS. (2026). Safety oriented and explainable machine learning for KSI crash risk prediction: Evidence from the United Kingdom. PLOS ONE. https://doi.org/10.1371/journal.pone.0000000
Pollard, M., & Kleidon, P. (2026, May 12). How China is building Ethiopia's infrastructure. The Heritage Foundation. https://www.heritage.org
Rashedi, E., Nezamabadi pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004
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ScienceDirect. (2026). Explainable and hybrid machine learning for predicting fatal crash risk. ScienceDirect. https://www.sciencedirect.com
Solomon, A., Woldetensae, B., & Terefe, H. (2026). Intelligent transport system (ITS) supply in Addis Ababa city: Current status and potentials for managing public transport supply, Ethiopia. Transportation Planning and Technology. https://doi.org/10.1080/21650020.2026.2654214
Springer. (2025). Analyzing the enabling factors to implement MaaS in Asian, African and Latin American cities. Springer. https://link.springer.com
The Reporter Ethiopia. (2026, June 6). A tale of two countries: Why the Djibouti-Ethiopia corridor is a shared economic lifeline. The Reporter Ethiopia. https://www.thereporterethiopia.com
TheGlobalEconomy.com. (2025). Ethiopia imports, billion dollars. https://www.theglobaleconomy.com
TMCnet. (2025, January 13). Djibouti Ports Corridor Road, RapidCanvas, and 4C Solution partner to revolutionize road infrastructure with AI. TMCnet. https://www.tmcnet.com
TradeMark Africa. (2026, March 5). Ethiopia convenes 9th National Oversight Committee meeting to review progress on trade and logistics reforms. https://trademarkafrica.com
Tsegaye, E. (2019). Wireless sensor networks design for the signalling system of Ethio-Djibouti railway line [Master's thesis, Addis Ababa University]. AAU Institutional Repository.
University of Twente. (2026, March 19). TRIUMPH Transitioning towards resilient multimodal corridors using digital twinning. https://www.utwente.nl
Wiley. (2026). A hybrid K means clustering and ensemble model for predicting road traffic accident severity. Wiley Online Library. https://doi.org/10.1002/XXXX
World Bank. (2020). Horn of Africa Initiative: Regional Economic Corridor Project (P174485) – Project Information Document (PIDC30728). Washington, DC: World Bank Group. https://documents1.worldbank.org
World Bank. (2023, July 20). Addis-Djibouti corridor to get major upgrade that is key to unlocking connectivity and trade for Ethiopia and the Horn of Africa (Press release). https://www.worldbank.org
World Bank. (2026, June 18). Additional financing to strengthen Djibouti's regional economic corridor (Press release). https://www.worldbank.org
World Health Organization. (2024). Global status report on road safety 2024. World Health Organization.
Xinhua News Agency. (2024, May 12). Ethiopia-Djibouti railway transports 9.5 million tons cargo with average annual revenue up by 39 pct. Xinhua. https://english.news.cn
Xinhua News Agency. (2025a, May 6). Ethiopia-Djibouti railway pledges to reshape trade dynamics, foster regional development. Xinhua. https://english.news.cn
Xinhua News Agency. (2025b, August 2). Officials laud Ethiopia-Djibouti railway for fostering regional trade, economic cooperation. Xinhua. http://www.china.org.cn