Predicting the next turn at road junction from big traffic data
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Smart city is an emerging research field nowadays, with emphasis of using big data to enhance citizens’ quality of life. One of the prevalent smart city projects is to use big traffic data collected from road users over time, for road planning, traffic light scheduling, traffic jam relief, and public security. In particular, being able to know a road user’s current location and predict his/her next move is important in today’s intelligent transportation systems. Trajectory prediction has become a prudential research study direction, by which many algorithms have been published before. In this paper, we present a simple probabilistic model which predicts road users’ next locations based on the “concept of segments” abstracted from historical trails which the users have taken and accumulated over time in some data archive. Given a trajectory and a current location, the road user’s next move in terms of road direction can be predicted at the junction. It is found that each road user would have his/her unique travel pattern hidden in the aggregate big traffic data. These patterns could be modeled from connected segments for simplicity. With the longer the trail and more frequently this trail was travelled, the more accurate that the next turn can be predicted. Simulation experiment was conducted based on summing up the segments from empirical trajectory data that was used in trajectory data mining by Microsoft. The results of our alternative model in contrast to the state of the arts demonstrated good efficacy.
KeywordsLocation prediction Trajectory mining GPS Trajectory analysis
The authors of this paper are thankful to the financial supports of the grant offered with code: MYRG2015-00024, called “Building Sustainable Knowledge Networks through Online Communities,” by RDAO, University of Macau. We acknowledge and thank you for the intellectual and technical contributions from Mr. Jia Cong Mao who is a software engineer of DACC laboratory and MSc graduate from the University of Macau.
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