Research on Front-End Fusion Processing Technology of Traffic Scenes
PDF

Keywords

Traffic scene
Data fusion
Traffic data processing

DOI

10.26689/jard.v6i2.3707

Abstract

With the intelligent development of road traffic control and management, higher requirements for the accuracy and effectiveness of traffic data have been put forward. The issue of how to collect and integrate data for traffic scenes has sought importance in this field as various treatment technologies have emerged. A lot of research work have been carried out from the theoretical aspect to engineering application. 

References

Wang M, Cheng L, 2012, Research on Floating Vehicle Map Matching Algorithm. Acta Geodaetica et Cartographica Sinica, 2012(2): 133-138.

Zhang G, Li F, 2014, 5th International Conference on Software Engineering and Service Science (ICSESS), June 27-29, 2014: Application of the KNN Algorithm Based on KD Tree in Intelligent Transportation System. IEEE, Beijing, 832-835.

He Z, She X, Zhuang L, et al., 2013, On-Line Map-Matching Framework for Floating Car Data with Low Sampling Rate in Urban Road Networks. IET Image Processing, 7(4): 404-414.

Li Q, Huang L, 2010, Map Matching Algorithm based on GPS Trajectory Data. Acta Geodaetica et Cartographica Sinica, 2010(2): 207-212.

Yu Y, 2010, Research on Key Technologies of Data Processing of Large Sample Floating Vehicle in Cloud Computing Environment, Wuhan University.

Bouillet E, Ranganathan A, 2010, Eleventh International Conference on Mobile Data Management, May 23-26, 2010: Scalable, Real-Time Map-Matching Using IBM’s System. IEEE, Kansas, 249-257.

Han X, Liu Y, Yan L, et al., 2011, International Conference on proceedings of the Remote Sensing, Environment and Transportation Engineering (RSETE), June 2011: Parallel Map Matching Algorithm Based on Multi-Core and MPI. Nanjing, 2291-2294.

Zou L, Xu J, Zhu L, et al., 2007, Travel Time Estimation Model based on Floating Vehicle Movement Detection and Induction Coil Fusion Technology. Journal of Highway and Transportation Research and Development, 24(6): 114-117.

Li R, Chen X, 2014, Research on Road Travel Time Estimation based on Multi-Source Data Fusion. Journal of Highway and Transportation Research and Development, 31(2): 99-103.

Rahmani M, Jenelius E, Koutsopoulos HN, 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC), October 6-9, 2013: Route Travel Time Estimation Using Low-Frequency Floating Car Data. IEEE, Hague, 2292-2297.

Yu D, Gao X, Yang Z, 2010, Road Travel Time Estimation based on GPS Data and Vehicle Running Characteristics. Journal of Jilin University (Engineering and Technology Edition), 2010(4): 965-970.

Fang L, Chen S, Zhao F, 2012, Average Travel Time Estimation based on Small Sample Floating Vehicle System. Computer Simulation, 2012(9): 367-370.

Li R, Ma W, 2014, Road Average Speed Fusion Method Based on BP Neural Network and D-S Evidence Theory. Journal of Traffic and Transportation Engineering, 2014(5): 111-118.

Guo JH, Xia JX, Smith BL, 2009, Kalman Filter Approach to Speed Estimation Using Single Loop Detector Measurements Under Congested Condition. Journal of Transportation Engineering, 135(12): 927-934.

Zhang C, Yang X, 2006, The Second China Intelligent Transportation Annual Conference, December 12, 2006: Road Traffic Information Acquisition System Based on Floating Vehicle. Beijing, 108-112.