Traffic prediction using real-world transportation data

摘要

Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.

主权项

1. A method of predicting traffic on a road network in view of an event having an identified time and an identified location in the road network, the method comprising:
retrieving attributes from past events on the road network; selecting, by a processor, a subset of the attributes that are correlated with traffic parameters comprising delayed traffic speeds, affected backlogs of vehicles, and amounts of time needed to clear backlogs of vehicles; discovering, by a processor, corresponding values for the traffic parameters under all combinations of the selected attributes; matching, by a processor, current attributes for the event in the road network to previous event attributes using the corresponding values for the traffic parameters to identify a subset of the past events; using, by a processor, the identified time, the identified location, and the subset of the past events to predict (i) a delayed traffic speed for the event, (ii) an affected backlog of vehicles on one or more roads approaching the event in the road network, and (iii) an amount of time needed for the affected backlog of vehicles to be cleared in the road network; and providing prediction data to a navigation device for the road network, the prediction data comprising (i) the delayed traffic speed for the event, (ii) the affected backlog of vehicles, and (iii) the amount of time needed for the affected backlog of vehicles to be cleared in the road network.