FORECASTING TRAIN TRAVEL TIMES AT AT-GRADE CROSSINGS
Document Type
Journal Article
Publication Date
2003
Subject Area
operations - traffic, infrastructure - bus/tram priority, infrastructure - traffic signals, ridership - commuting, ridership - forecasting, ridership - forecasting, mode - rail
Keywords
Train arrival time at crossings, Traffic signal priority systems, Traffic signal preemption, Signalized intersections, Signalised intersections, Scenarios, Railroad grade crossings, Projections, Preemption (Traffic signals), Neural networks, Modular computer architecture, Level crossings, Highway railroad grade crossings, Highway rail intersections, Grade crossings, Forecasting, Detectors, Artificial neural networks, ANNs (Artificial neural networks)
Abstract
The ability to accurately forecast train arrival times is essential for the safe and efficient operation of highway-railroad grade crossings (HRGCs). Trains in the United States are required to give a minimum of 20 s of warning time before arriving at an HRGC. With the recent development of new detection-equipment technology, detectors potentially could be employed further upstream of the HRGC, which would result in earlier detection times. This information would be particularly useful for preemption strategies at signalized intersections located near the HRGC (IHRGCs). For example, earlier warning times could be used to reduce or eliminate the risk of unsafe pedestrian movements at IHRGCs. In this study, a modular artificial neural network (ANN) was used to forecast the train arrival time at an HRGC. An ANN was adopted because there is a nonlinear relationship between the independent variables such as train speed profile and the dependent variable arrival time at an HRGC. A modular approach was used because the trains often have different characteristics depending on their cargo and the operational rules in effect at the time they are detected. Because the train detection is continuous, different models were developed for each separate data input. In this case, the prediction interval update was assumed to be 10 s and 24 models were developed. Approximately 499 trains were used for training the ANN and 183 trains were used for testing. It was found that a modular architecture gave superior results to that of a simple ANN model, standard regression techniques, and current forecasting methods for the entire detection time period. It was found that, with an increase in detection time, the forecast accuracy increases for all methods and the prediction interval tends to decrease.
Recommended Citation
Cho, H, Rilett, L. (2003). FORECASTING TRAIN TRAVEL TIMES AT AT-GRADE CROSSINGS. Transportation Research Record, Vol. 1844, p. 94-102.
