Route Planning with Real Time Traffic Predictions
Situation dependent route planning gathers increasing interest as cities become crowded and jammed. We present a system for individual trip planning that incorporates future traffic hazards in routing. Future traffic conditions are computed by a Spatio Temporal Random Field based on a stream of sensor readings. In addition, our approach estimates traffic flow in areas with low sensor coverage using a Gaussian Process Regression. The conditioning of spatial regression on intermediate predictions of a discrete probabilistic graphical model allows to incorporate historical data, streamed online data and a rich dependency structure at the same time. We demonstrate the system with a real world use case from Dublin city, Ireland. Resubmission of: [12] Predictive Trip Planning Smart Routing in Smart Cities. In: Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference, vol. 1133, pp. 331 338. CEUR WS.org (2014)
LWA