ML-Powered Incident Detection via Map-Matching: A Technical Review

Abstract

Traffic incident detection represents a critical challenge in intelligent transportation systems, where traditional methods suffer from significant latency and scalability constraints that limit their effectiveness in dynamic urban environments. This technical review presents an innovative machine learning framework that harnesses real-time GPS trajectory data integrated with sophisticated map-matching algorithms to identify anomalies indicative of traffic accidents, roadblocks, and sudden congestion events. The proposed system integrates Hidden Markov Models with Graph Neural Networks to enhance localization precision while employing a streaming data pipeline powered by Apache Flink for low-latency processing across distributed systems. Context-aware anomaly detection mechanisms utilize historical traffic patterns and environmental factors to improve robustness and reduce false positives. The framework demonstrates superior performance compared to traditional baseline methods through a comprehensive evaluation on urban GPS datasets. Key innovations include unified real-time processing pipelines, multi-source data integration capabilities, and graph-based spatial relationship analysis. The system's versatility enables deployment across multiple transportation management scenarios, from smart city traffic control centers to emergency dispatch systems and connected vehicle platforms, ultimately contributing to enhanced public safety, reduced environmental impact, and improved urban mobility efficiency.