Forecasting spatiotemporal impact of traffic incidents for next-generation navigation systems

Abstract

The advances in sensor technologies enable real-time collection of high-fidelity spatiotemporal data on transportation networks of major cities. In this paper, using two real-world transportation datasets: (1) incident and (2) traffic data, we address the problem of predicting and quantifying the impact of traffic incidents. Traffic incidents include any nonrecurring events on road networks, such as accidents, weather hazard or road construction. By analyzing archived incident data, we classify incidents based on their features (e.g., time, location, type of incident). Subsequently, we model the impact of each incident class on its surrounding traffic by analyzing the archived traffic data at the time and location of the incidents. Consequently, in real-time, if we observe a similar incident (from real-time incident feeds), we predict and quantify its impact on the surrounding traffic using our models. This information, in turn, can help drivers to effectively avoid impacted areas in real-time. To be useful for such real-time navigation application, and unlike current approaches, we study the dynamic behavior of incidents and model the impact as a quantitative time varying spatial span. In addition, we study a novel approach that improves our classification method by analyzing traffic density around the incident area and the initial behavior of the incident. We evaluated our approach with very large traffic and incident datasets collected from Los Angeles County and the results show by utilizing our impact prediction approach in the navigation system, precision of the travel time calculation can be improved by up to 67 %.