Police emergency dispatch model based on emergency level analysis and traffic planning

Area of Science:

• Transportation Planning
• Operations Research
• Artificial Intelligence

Background:

• Emergency dispatch and rescue are critical research areas in transportation planning.
• Current cross-regional police emergency dispatch systems face efficiency challenges.

Purpose of the Study:

• To improve the efficiency of cross-regional police emergency dispatch.
• To develop a robust emergency rescue demand evaluation and dispatch model.

Main Methods:

• Constructed an emergency rescue demand evaluation system using the superior and inferior solution distance method for hierarchy analysis.
• Developed and solved a police emergency dispatch model with an improved particle swarm algorithm.
• Validated the model's performance against existing algorithms and in simulated emergency scenarios.

Main Results:

• The improved particle swarm algorithm enhanced iteration efficiency by 54.33% and reduced time overhead by 56.10% compared to other algorithms.
• Achieved high material demand satisfaction rates (over 80% overall, 100% for vegetables in emergency 1; over 91.3% overall, 100% for tents in emergency 2).
• Demonstrated the feasibility and practicality of the integrated AI and optimization approach for emergency logistics.

Conclusions:

• The proposed police emergency dispatch method effectively utilizes transportation networks for cross-regional emergency rescue.
• Integration of intelligent optimization and AI technology provides a feasible and practical solution for emergency material dispatch.
• The developed system significantly improves response efficiency and resource allocation during critical incidents.