Abstract
Path prediction is an important issue in traffic management. In big data environment, the real-time ANPR (Automatic Number Plate Recognition) data can provide evident support for path prediction. Taking advantage of the real-time ANPR data, this study adopts and modifies a trajectory prediction model based on order-k Markov model, calculating a supplementary probability matrix to compensate for the deficiency of the prediction model by exploring the hot spot area. This model produces a five-step prediction procedure which takes at most 8 s, exhibiting the efficiency of data processing. Meanwhile, the model shows the stability when dealing with updating data which has a 20 million growth on a daily basis, and displays the compatibility in dynamically accommodating itself into the periodic trajectory changes. By implementing the deployment mode of prediction algorithm with real-time big data, the fruits of this study can meet the requirement of the traffic management. Although an improved scheme for random trajectory prediction is added to the model, the accuracy of random trajectory prediction is still far from perfect. Therefore, the study will further optimize the prediction accuracy of the order-k Markov model by adding more factors into it and upgrading it into a higher order one.
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Long, Z., Zhang, Z. (2020). Optimization and Deployment of Vehicle Trajectory Prediction Scheme Based on Real-Time ANPR Traffic Big Data. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_7
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