| An Agent-Based Traffic Regulation System for Roadside Air Quality Control | |
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MSRDG International Journal of Computer Scientific Technology & Electronics Engineering
© 2026 by MSRDG IJCSTEE Journal Volume 2 Issue 1
Year of Publication: 2026 |
Paper Download Article ID MSRDG-IJCSTEE-V2I1P102 DOI https://doi.org/10.66037/MSRDG-IJCSTEE/V2I1P102 |
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Abstract: Rapid urban expansion and the consequent increase in vehicular traffic have elevated roadside concentrations of particulate matter and gaseous pollutants to levels that pose considerable public health risks in many Indian cities. Conventional traffic signal systems operate on fixed-cycle or isolated sensor-based schemes that neither respond dynamically to real-time pollution events nor coordinate across multiple intersections. This paper proposes an Agent-Based Traffic Regulation (ABTR) system that integrates a multi-agent architecture with heterogeneous roadside sensor networks to achieve simultaneous optimisation of traffic flow and air quality. The system deploys four specialised agent categories—Monitoring Agents, Analysis Agents, Negotiation Agents, and Control Agents—interacting through an asynchronous message-passing protocol within a Belief–Desire–Intention (BDI) cognitive framework. When the measured Air Quality Index (AQI) at a monitored intersection exceeds a defined threshold, the agent ensemble coordinates adaptive signal timing, dynamic rerouting, and, where applicable, emission-based access restrictions. Simulation experiments conducted on a calibrated SUMO-based urban network with embedded CALINE4 dispersion modelling demonstrate that the proposed system reduces peak-hour PM2.5 concentrations by 46–48% and PM10 by 47%, decreases intersection wait time by 21%, and raises network throughput by 24.9% compared with an uncontrolled baseline. The system outperforms rule-based, reactive-agent, and fuzzy logic alternatives across all evaluated performance dimensions. These findings establish a viable pathway for deploying intelligent, scalable air quality management solutions in heavily congested urban corridors. |
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| Keywords: Multi-agent systems · Air quality management · Adaptive traffic control · BDI agents · Roadside pollution · Urban mobility · PM2.5 mitigation | |
