Estimating Train Arrival Times at Highway - Railroad Grade Crossing Using Multiple Sensors
January 17, 2010
/system/datas/34/original/railroad_crossing_2.png Safety should always be a concern when highway-railroad grade crossings (HRGCs) are near a signalized intersection due to the potential risk of accidents involving highway traffic and trains. Thus, traffic engineers are always seeking for better solutions to handle traffic in these locations. Current traffic signal preemption strategies at signalized intersections near HRGCs are based on the minimum required warning time for train arrival at an HRGC of 20 seconds for highway users provided by first-generation train detection technologies. If train arrival times at HRGCs can be accurately estimated within approximately a traffic signal cycle length ahead of train arrivals, signal preemption strategies may be able to be improved and highway traffic can be managed in a safer manner. This research conducted by Diego Franca, under the supervision of Dr. Elizabeth Jones during his master’s program at the University of Nebraska-Lincoln, developed a method to improve the estimation of train arrival times at HRGCs by fusing speed data from two sensors located upstream of the studied HRGC: Doppler radar and video image detection (second generation technologies). A Kalman filter model was used to fuse the speed data to overcome the limitations of both sensors when detecting trains on a multiple track environment. Results showed that the fused data provided better train arrival time estimations than either radar or video detection estimations alone in a multi-train, multi-track environment.
The following presentation provides an overview of Diego’s research and results. Contact information is also listed below in case you have additional quesitons on this topic.
Diego Franca, M.Sc.
Transportation Analyst, Kittelson & Associates, Inc.
Phone: (954) 828-1730
Elizabeth “Libby” Jones, PhD
Assoc. Professor, Civil Engineering, University of Nebraska - Lincoln
Phone: (402) 554-3869