View Proposal

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Proposer

Muhammad Zubai

Title

Emergency Response Decision Support System for Smart Aktobe

Goal

To develop a decision-support system that predicts emergency demand and optimizes emergency response planning.

Description

Smart and connected cities require effective emergency response systems that can support authorities in making timely and evidence-based decisions. Emergency response efficiency depends on several interconnected factors, including the location and frequency of incidents, road conditions, hospital accessibility, traffic conditions, and weather. This project will investigate the development of a decision-support system for emergency response in Aktobe. The system will analyse historical and, where available, real-time emergency incident data together with road network information, hospital locations, and weather conditions to identify high-risk areas and estimate emergency demand. The project will develop predictive models to identify patterns in emergency incidents and forecast demand across different locations and time periods. Optimization techniques will then be investigated to recommend efficient emergency vehicle dispatch strategies and routes. The system will provide decision-support information that can help emergency authorities determine where resources should be positioned and how they can be dispatched more efficiently. The project will also explore the integration of IoT and smart-city data sources to support continuous monitoring and improve the responsiveness of the system. The performance of the proposed approach will be evaluated using appropriate metrics, such as prediction accuracy, response-time reduction, resource utilization, and routing efficiency. The project is particularly relevant to the development of a Smart and Connected Aktobe, where data-driven decision support can contribute to improved public safety and more efficient use of emergency resources.

Resources

1) Zhang, H., Zhang, R. & Sun, J. Developing real-time IoT-based public safety alert and emergency response systems. Sci Rep 15, 29056 (2025). https://doi.org/10.1038/s41598-025-13465-7 2)Ghani ur Rehman, Anwar Ghani, Muhammad Zubair, Muhammad Imran Saeed, Dhananjay Singh, “SOS: Socially omitting selfishness in IoT for smart and connected communities”, International Journal of Communication Systems, https://doi.org/10.1002/dac.4455, 2023; 36(1), 3) González-Villa, J., Cuesta, A., Spagnolo, M. et al. Decision-support system for safety and security assessment and management in smart cities. Multimed Tools Appl 83, 61971–61994 (2024). https://doi.org/10.1007/s11042-023-16020-6

Background

Smart and Connected Aktobe / Digital Kazakhstan initiatives and related smart-city and emergency-response research.

Url

External Link

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

1

Supervisor

Muhammad Zubai

Keywords

emergency response, decision support system, smart city, aktobe, machine learning, iot, optimization, predictive analytics, intelligent transportation, public safety

Degrees

Bachelor of Science in Computational Sciences and Software Engineering