Predestination: inferring destinations from partial trajectories
UbiComp'06 Proceedings of the 8th international conference on Ubiquitous Computing
Trajectory-aware mobile search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social itinerary recommendation from user-generated digital trails
Personal and Ubiquitous Computing
Some help on the way: opportunistic routing under uncertainty
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Predicting a driver's route would be useful for warning a driver of upcoming road hazards, informing about traffic situations, and serving relevant advertising. There are many clues to a driver's future route, including their past behavior and likely destination. Another clue is the driver's turn choices at a sequence of intersections. Strung together, the turn choices form a route. This paper develops a basic algorithm, and variations, to predict the aggregate turn behavior of drivers at intersections. Given an intersection with a few different turn options, including the option to continue straight ahead, our goal is to infer the proportion of drivers who will take each option. For ground truth, we use raw turn counts gathered for government traffic studies by our local municipality at 40 different intersections. Our basic turn prediction algorithm is based on the assumption that drivers tend to choose roads that offer them more destination options. This matches our intuition that turning onto a short, dead-end road is relatively rare compared to turning onto a highway "on" ramp. The best performing algorithm predicts turn proportions with a median error of 0.142.