TRAFFIC INFORMATION INTERPOLATION METHOD BASED ON TRAFFIC FLOW EMERGENCE USING SWARM INTELLIGENCE
Abstract:
Traffic congestion has become a major problem in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. Accurate traffic information is essential for effective traffic management and route planning. However, obtaining real-time traffic data from every road segment is often challenging due to limited sensor coverage and high deployment costs.
In this study, we propose a novel method for interpolating traffic information based on traffic flow emergence using swarm intelligence. The proposed method leverages the collective behavior of a swarm of intelligent agents to estimate traffic conditions in areas with limited or no sensor coverage. Swarm intelligence is a collective behavior exhibited by decentralized systems, where simple agents interact locally to achieve complex global behavior.
The method begins by deploying a swarm of virtual agents in the road network. Each agent is equipped with local sensing capabilities, which enable them to observe and interact with their immediate surroundings. The agents communicate with each other, sharing information about traffic flow and congestion levels they observe. Through local interactions and information exchange, the swarm collectively builds an understanding of the traffic conditions across the network.
To interpolate traffic information in areas with limited sensor coverage, the swarm intelligently propagates the observed traffic information to neighboring road segments. The propagation is guided by the principle of flow emergence, which states that traffic congestion tends to spread from congested to adjacent road segments. By leveraging this principle, the swarm infers traffic conditions in areas without direct sensor coverage based on the observed congestion patterns in nearby segments.
To evaluate the effectiveness of the proposed method, we conducted experiments using real-world traffic data from a metropolitan area. The results demonstrate that the swarm-based interpolation method outperforms traditional interpolation techniques in terms of accuracy and robustness. The method effectively captures traffic dynamics and provides reliable traffic information, even in scenarios with sparse sensor coverage.
The proposed traffic information interpolation method based on traffic flow emergence using swarm intelligence offers a promising approach to address the limitations of traditional traffic data collection methods. By leveraging the collective intelligence of a swarm of agents, the method enables accurate estimation of traffic conditions in areas with limited sensor coverage. This information can assist traffic management authorities and individual drivers in making informed decisions and optimizing their routes, ultimately leading to improved traffic flow and reduced congestion in urban areas.
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