|Table of Contents|

Recognition method of road speed limit information based on data mining of traffic trajectory(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2015年05期
Page:
118-126
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Recognition method of road speed limit information based on data mining of traffic trajectory
Author(s):
LIAO Lu-chao1; 2;  JIANG Xin-hua1; 2;  LIN Ming-zhen3;  ZOU Fu-min2
1. School of Information Science and Engineering, Central South University, Changsha 410075, Hunan, China; 2. Fujian Key Laboratory for Automotive Electronics and Electric Drive, Fujian University of Technology, Fuzhou 350108, Fujian, China; 3. School of Software, Central South University, Changsha 410075, Hunan, China
Keywords:
road speed limit;  trajectory data mining;  floating car data;  traffic flow;  map matching;  K-nearest neighbor algorithm
PACS:
U491
DOI:
-
Abstract:
The spatiotemporal variability of speed limit information was analyzed, and an automatic recognition method of road speed limit information was proposed based on the mining technique of trajectory data. To fast process the massive traffic trajectory data, the pretreatment algorithms such as rapid map matching and data cleaning were researched. The speed distribution features of traffic trajectory data and the maximum speed limit index were analyzed. Based on the speed features at road section, a road feature vector model was constructed to rapid extract the latent characteristics information from the massive trajectory data was achieved. In order to implement a rapid recognition of speed limit information, a classification algorithm based on multi-voting K-nearest neighbor(MV-KNN)algorithm was proposed for the training and learning process of data feature. The training, learning and cross-validation experiments were completed by using the sample sets constructed by actual floating car trajectory data and traffic network in Fuzhou City. Experimental result indicates that the highest system recognition accuracy of proposed method is up to 93% by using 1 200 samples in the training process, and the system recognition accuracy is 75% by using only 150 samples. The near-linear processing performance of proposed method is revealed, and the system operating time is only 46 ms in processing 1 000 000 samples of road speed limit information. 1 tab, 12 figs, 29 refs.

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Last Update: 2015-10-20