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Short-term traffic prediction method on big data in highway domain
WANG Xuefei, DING Weilong
Journal of Computer Applications 2019, 39 (
1
): 87-92. DOI:
10.11772/j.issn.1001-9081.2018071665
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(
857
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Aiming at the problems that traditional short-time traffic flow prediction method in highway domain is suitable for small scale data, which limits the efficiency on massive data, and the spatio-temporal relationship of data is neglected, a short-term traffic flow prediction method for big data with combining
K
-Nearest Neighbors (
K
NN) in highway domain was proposed. Firstly, the
K
value and distance metric of model were tuned, and the model parameters were compared by using cross validation. Secondly, considering inherent spatio-temporal association of data, feature vectors based on spatio-temporal characteristics were modeled. Finally, a regression prediction model was established under big data environment, and the prediction was realized with the model of optimal parameters. The experimental results show that compared with traditional time series model, the proposed model works on all toll stations at one time, has high speed of single running and improves the efficiency by 77%. The method significantly reduces Mean Absolute Percentage Error (MAPE) and Median Absolute Percentage Error (MDAPE) and it also has good horizontal expansibility.
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Stream computing system for monitoring copy plate vehicles
QIAO Tong, ZHAO Zhuofeng, DING Weilong
Journal of Computer Applications 2017, 37 (
1
): 153-158. DOI:
10.11772/j.issn.1001-9081.2017.01.0153
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597
)
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The screening of the copy plate vehicles has timeliness, and the existing detection approaches for copy plate vehicles have slow response and low efficiency. In order to improve the real-time response ability, a new parallel detection approach, called stream computing, based on real-time Automatic Number Plate Recognition (ANPR) data stream, was proposed. To deal with the traffic information of the road on time, and plate vehicles could be timely feedback and controlled, a stream calculation model was implemented by using the threshold table of road travel time and the time sliding window, which could access real-time traffic data stream to calculate copy plate vehicles. On the platform of Storm, this system was designed and implemented. The calculation model was verified on a real-time data stream which was simulated by the real ANPR dataset of a city. The experimental results prove that a piece of license plate recognition data can be dealt with in milliseconds from the time of arrival to the calculation completion, also, the calculation accuracy is 98.7%. Finally, a display system for copy vehicles was developed based on this calculation model, which can show the copy plate vehicles from the road network on the current moment.
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