IncidentResponseGPT: Generating Traffic Incident Response Plans with Generative Artificial Intelligence

TL;DR

IncidentResponseGPT leverages generative AI to create tailored traffic incident response plans, aiming to improve decision-making speed and effectiveness in urban traffic management through region-specific recommendations.

Contribution

This paper introduces IncidentResponseGPT, a novel AI system that synthesizes region-specific incident response plans and ranks them using TOPSIS for optimized traffic management.

Findings

Generated response plans effectively reduce incident resolution times.

The system's ranking aligns well with human expert solutions.

Traffic impact is minimized through optimized rerouting and resource allocation.

Abstract

The proposed IncidentResponseGPT framework - a novel system that applies generative artificial intelligence (AI) to potentially enhance the efficiency and effectiveness of traffic incident response. This model allows for synthesis of region-specific incident response guidelines and generates incident response plans adapted to specific area, aiming to expedite decision-making for traffic management authorities. This approach aims to accelerate incident resolution times by suggesting various recommendations (e.g. optimal rerouting strategies, estimating resource needs) to minimize the overall impact on the urban traffic network. The system suggests specific actions, including dynamic lane closures, optimized rerouting and dispatching appropriate emergency resources. We utilize the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank generated response plans based on criteria like impact minimization and resource efficiency based on their proximity to an human-proposed solution.