Data Processing Procedure for DSRC Probe-Based Advanced Traveler Information System on Signalized Arterials
Downloads
When faced with traffic congestion on the road, drivers are eager to avoid it by diverting to a less congested route using real-life traffic information. To meet the demand from the public, advanced traveler information systems (ATIS) that collect, process, and provide real-life traffic information (travel time or speed) are gaining attraction worldwide. Due to the efficiency and ability to collect link travel times, 5.8 GHz Dedicated Short-Range Communications (DSRC) probe-based ATIS has been actively deployed in South Korea. In this study, the data processing procedure to generate real-life traffic information in the DSRC probe-based ATIS on signalized suburban arterials in Korea is presented. The procedure includes methods for traffic information generation, outlier filtering, and missing data imputation. Real-life traffic information is generated in three sections―the standard, the RSE, and the information―to provide it to the drivers and to relay it to other traffic management centers. Outlying observations are filtered using a coefficient of variation logic. If real-time data are missed, a missing data imputation process is activated. Due to the practical characteristics of the methods presented herein, they could be practically referred by practitioners of probe-based systems of the kind. This article concludes with some discussions on directions towards an improvement in the algorithms.
W. Eisele, Estimating Corridor Travel Time Using Point and Probe Detector Data, Lambert Academic Publishing, 2012.
J. Jang and S. Lim, An outlier filtering algorithm for dedicated short-range communications probe data: Proc. 2013 Annu. Conf., Seoul, 2003.
Korea Institute of Civil Engineering and Building Technology, Manual on DSRC Probe Data Processing Algorithm, 2011. (In Korean)
J. Jang, Short-term travel time prediction using the Kalman filter combined with a variable aggregation interval scheme, Jour. of Eastern Asia Society for Transp. Studies, vol. 10, 2013.
Y. Zhang and H. Ge, Freeway travel time prediction using Takagi-Sugeno-Kang fuzzy neural network, Computer-Aided Civil and Infrastructure Engineering, 28:8, 2013.
Southwest Research Institute, Automatic Vehicle Identification Model Deployment Initiative-System Design Document, Texas Department of Transportation, 1998.
F. Dion and H. Rakha, Estimating dynamic roadway travel times using automatic vehicle identification data, Transportation Research Part B, Elsevier, 2006.
X. Ma and H. Koutsopoulos, Estimation of the automatic vehicle identification based spatial travel time information collected in Stockholm, IET Intelligent Transport Systems, vol. 4, iss. 4, 2010.
ITS Korea, Hitecom System, and Aju University, Development of Practical Technology for DSRC Traffic Information System, Korea Expressway Corporation, 2008. (In Korean)
D. V. Boxel, W. H. Schneider IV, and C. Bakula, An innovative real-time methodology for detecting travel time outliers on interstate highways and urban arterials, TRB 2011 Annual Meeting CD-ROM, Washington D.C., 2011.
