A Accident Impact Prediction

For the first time, real-time high-fidelity spatiotemporal data on the transportation networks of major cities have become available. This gold mine of data can be utilized to learn about the behavior of traffic congestion at different times and locations, potentially resulting in major savings in time and fuel, the two important commodities of the twenty-first century. According to FASANA Motion report (Report 2012), approximately 50 % of the freeway congestions are caused by nonrecurring issues, such as traffic accidents, weather hazard, special events, and construction zone closures. Hence, it is fairly important to quantify and predict the impact of traffic incidents on the surrounding traffic. This quantification can alleviate the significant financial and time losses attributed to traffic incidents, for example, it can be used by city transportation agencies for providing evacuation plan to eliminate potential congested grid locks, for effective dispatching of emergency vehicles, or even for long-term policy-making. Moreover, the predictive information can be either used by a driver directly to avoid potential gridlocks or consumed by a predictive route-planning algorithm (e.g., Demiryurek et al. 2011) to ensure a driver to select the best route from the start. The McKinsey report (McK 2011) predicts a worldwide consumer saving of more than $600 billion annually by 2020 for location-based services, where the biggest single consumer benefit will be from time and fuel savings from navigation services tapping into real-time traffic data. Therefore, let us consider a navigation system utilizing predictive route-planning algorithm as a next-generation consumer navigation system (incar or on smartphone). We notate such systems as ClearPath, as a motivating application, which can help drivers to effectively plan their routes in real time by avoiding the incidents’ impact areas. That is, suppose an accident is reported in real time (by crowdsourcing (WAZE 2014) or through agency reports or SIGALERTS (2013)) in front of a driver, but the accident is 20 min away. If we can effectively predict the impact of the accident, ClearPath would know that this accident would be cleared in the next 10 min. Thereby, ClearPath would guide the driver directly toward the accident because it knows that by the time the driver arrives in the area, there would be no accident.