JDAIP  Vol.2 No.4 , November 2014
Data Based Calibration System for Radar Used by Vehicle Activated Signs
Abstract: The accurate measurement of a vehicle’s velocity is an essential feature in adaptive vehicle activated sign systems. Since the velocities of the vehicles are acquired from a continuous wave Doppler radar, the data collection becomes challenging. Data accuracy is sensitive to the calibration of the radar on the road. However, clear methodologies for in-field calibration have not been carefully established. The signs are often installed by subjective judgment which results in measurement errors. This paper develops a calibration method based on mining the data collected and matching individual vehicles travelling between two radars. The data was cleaned and prepared in two ways: cleaning and reconstructing. The results showed that the proposed correction factor derived from the cleaned data corresponded well with the experimental factor done on site. In addition, this proposed factor showed superior performance to the one derived from the reconstructed data.
Cite this paper: Jomaa, D. , Yella, S. , Dougherty, M. and Edvardsson, K. (2014) Data Based Calibration System for Radar Used by Vehicle Activated Signs. Journal of Data Analysis and Information Processing, 2, 106-116. doi: 10.4236/jdaip.2014.24013.

[1]   Jomaa, D., Yella, S. and Dougherty, M. (2013) Review of the Effectiveness of Vehicle Activated Signs. Journal of Transportation Technologies, 3, 123-130.

[2]   R?m?, P. and Kulmala, R. (2000) Effects of Variable Message Signs for Slippery Road Conditions on Driving Speed and Headways. Transportation Research Part F: Traffic Psychology and Behaviour, 3, 85-94.

[3]   Jendzurski, J. and Paulter, N.G. (2009) Calibration of Speed Enforcement Down—The Road Radars. Journal of Research of the National Institute of Standards and Technology, 114, 137-148.

[4]   Grakovski, A., Ovchinnikov, V. and Kamenchenko, S. (2010) Acoustic Signals Processing and Appliance for the Problem of Traffic Flow Monitoring. Transport and Telecommunication, 11, 4-10.

[5]   Goodson, M.E. (1985) Technical Shortcomings of Doppler Traffic Radar. Journal of Forensic Sciences, JFSCA, 30, 1186-1193.

[6]   Dujmich, L.C. (1980) Radar Speed Detection: Homing in on New Evidentiary Problems. Fordham Law Review, 48, 1138-1164.

[7]   Fernandez, J.R.O., Briso-Rofriguez, J.C., Calvo-Gallego, J., Burgos-Garcia, M., Perez-Martines, F. and Arana-Pulido, V.A. (2012) Doppler Radar Calibration System. IEEE A&E Systems Magazine, 27, 20-28.

[8]   Weil, C.M., Camell, D., Novotny, D.R. and Johnk, R.T. (2005) Across the Road Photo Traffic Radars: New Calibration Techniques. Proceedings on 15th International Conference on Microwaves, Radar and Wireless Communications MIKON, 3, 889-892.

[9]   Sattibabu, G., Sridevi, C.H., Siva, S.P.T. and Ganika Sridevi, S. (2012) Design of Velocity-Measuring System with Doppler Radar Concept and FFT Algorithm Based on ARM Processor for Traffic Safety. International Journal of Advanced Research in Electronics and Communication Engineering, 1, 22-27.

[10]   Zhang, J. (2009) The Traffic-Flow Detection Based on Pseudo-Random Coded Radar. Proceedings on International Conference on Measuring Technology and Mechatronics Automation, 9, 630-632.

[11]   Roy, A., Gale, N. and Hong, L. (2011) Automated Traffic Surveillance Using Fusion of Doppler Radar and Video Information. Mathematical and Computer Modelling: An International Journal archive, 54, 531-543.

[12]   Jiang, Z., Li, S.B. and Liu, X.Q. (2012) Parameters Calibration of Traffic Simulation Model Based on Data Mining. Journal Transportation System Engineering and Information Technology, 12, 28-33.

[13]   Kant, D.S., Balaji, P. and Shriniwas, A.S. (2012) Time Gap Modeling Using Mixture Distributions under Mixed Traffic Conditions: A Statistical Analysis. Journal Transportation System Engineering and Information Technology, 12, 72-84.

[14]   Daili, N. (2008) Numerical Approach to Fixed Point Theorems. International Journal of Contemporary Mathematical Sciences, 3, 675-682.

[15]   Dion, F. and Rakha, H. (2003) Estimating Dynamic Roadway Travel Times Using Automatic Vehicle Identification Data for Low Sampling Rates. Transportation Research Part B, 40, 745-766.

[16]   Wei, S., Jian, W., Bai-gen, C. and Qin, Y. (2003) A Novel Vehicle Detection Method Based on Wireless Magneto-Re- sistive Sensor. Proceedings on 3rd International Symposium on Intelligent Information Technology Application, 3, 484-487.

[17]   Armstrong, S.J. and Collopy, F. (1992) Error Measures for Generalizing about Forecasting Methods: Empirical Comparisons. International Journal of Forecasting, 8, 69-80.