An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

Area of Science:

• Intelligent Transportation Systems
• Traffic Engineering
• Computer Science

Background:

• Vehicular traffic congestion is a major urban issue, increasing travel time, fuel consumption, and pollution.
• Limited road capacity exacerbates congestion, impacting city mobility and environmental quality.
• Developing efficient traffic management strategies is crucial for sustainable urban development.

Purpose of the Study:

• To develop a dynamic approach for calculating optimal traffic routes.
• To improve traffic flow and reduce negative impacts of congestion.
• To evaluate the proposed algorithm against existing traffic routing methods.

Main Methods:

• A novel algorithm dynamically calculates optimal traffic routes using average travel speed and road length.
• Average travel speed data is sourced from smart city sensors and communicated via the Internet of Vehicles.
• The proposed method's performance is benchmarked against Simulated Annealing Weighted Sum, Simulated Annealing Technique for Order Preference by Similarity to the Ideal Solution (SA-TOPSIS), and Dijkstra's algorithm.

Main Results:

• The improved SA-TOPSIS method demonstrated an average 19.22% improvement in traffic performance (travel time, fuel consumption, CO₂ emissions) under congestion.
• Simulation results from Sheffield and Birmingham scenarios confirmed the algorithm's effectiveness.
• The proposed approach significantly outperforms traditional algorithms in congested traffic conditions.

Conclusions:

• The developed dynamic optimal route calculation method effectively mitigates traffic congestion.
• The improved SA-TOPSIS algorithm offers a superior solution for optimizing urban traffic flow and reducing environmental impact.
• Smart city infrastructure and advanced algorithms are key to addressing traffic congestion challenges.