Incident congestion propagation prediction using incident reports

Accurate predictions of how congestion propagates are essential for mitigating its effects on traffic and the urban environment. However, the vast majority of state-of-the-art traffic prediction models focus on regular traffic scenarios and struggle to adapt to the conditions following incidents. This is particularly problematic since the irregular periods after incidents are arguably when traffic predictions are most critical. Current traffic models struggle with non-recurring congestion for two reasons: they lack inputs alerting them an incident has happened, and traffic data containing incident information is scarce. We create two new such datasets: one by simulating incidents and their congestion in an open-source microscopic simulator and another by fusing real-world traffic flow data with incident reports. We then propose a framework that integrates incident reports into deep learning models for congestion propagation prediction. Our framework utilizes the recent traffic flow data and fuses it with information from incident reports. We perform a detailed empirical comparison between recurrent and graph-based models utilizing incident reports against baselines. Our study demonstrates that our framework significantly outperforms state-of-the-art graph-based models that do not account for incident reports.