Freeway travel time prediction based on clustering method with data mining

被引：0

作者：

Xing X. ^{[1
,2
]}

Yu D. ^{[2
,3
,4
]}

Tian X. ^{[2
]}

Cheng Z. ^{[2
]}

机构：

[1] College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin

[2] College of Transportation, Jilin University, Changchun

[3] State Key Laboratory of Automobile Dynamic Simulation, Jilin University, Changchun

[4] Jilin Province Key Laboratory of Road Traffic, Jilin University, Changchun

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2016年 / 44卷 / 08期

关键词：

Data mining; Freeway; K-means method; Networking toll data; Predict strength; Travel time prediction;

D O I：

10.13245/j.hust.160808

中图分类号：

学科分类号：

摘要：

Model chooses freeway history data was set as the research object to predict freeway travel time. Historical data of traffic travel time was categorized into different sample types using cluster analysis. Traffic data of travel time was classified and identified by actual characteristics of historical data. Freeway travel time prediction model was structured using data mining. The instance data was the real data recorded from toll stations of Shandong province. Analysis used the instance data forecast model and calculated the mean absolute percentage error of algorithm. To illustrate the validity of the mode, actual test set was applied to a variety of algorithms of travel time prediction. The comparison between the prediction errors of the algorithms was given. The results show that the modified k-means algorithm mentioned in the paper improves the accuracy of prediction. Model decreases the cost of data acquisition, and provides reliable prediction of travel time for the information service. The model provides powerful decision basis for travelers. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.

引用

页码：36 / 40

页数：4

共 6 条

[1]

Hollander Y., Liu R., Estimation of the distribution of travel times by repeated simulation, Transportation Research: Part C: Emerging Technologies, 16, 2, pp. 212-231, (2008)

[2]

Van L., Van G., Et al., Travel time unreliability on freeways: Why measures based on variance tell only half the story, Transportation Research, Part A: Policy and Practice, 42, 1, pp. 258-277, (2008)

[3]

Yeon J., Elefteriadou L., Travel time estimation on a freeway using discrete time Markov chains, Transportation Research, Part B: Methodological, 42, 4, pp. 325-329, (2008)

[4]

Wosyka J., Pribyl P., Real-time travel time estimation highways using loop detector data and license plate recognition, Proceedings of 9th International Conference, pp. 391-394, (2012)

[5]

Gultekin Cetiner B., Sari M., Borat O., A neural network based traffic-flow prediction model, Mathematical and Computauional Applications, 15, 2, pp. 269-278, (2010)

[6]

Sun D.Z., Wang Q.X., Jiang X.Q., The application of support vector machines(SVM) for traffic condition prediction using ITS data, Proceedings of the Conference on Traffic and Transportation Studies, pp. 299-307, (2010)

← 1 →