Real-time arrival prediction models for light rail train systems

Abstract: One of the main objectives of public transport operators is to adhere to the planned timetable and to provide accurate information to passengers in order to improve actual and perceived service reliability. The aim of this thesis is to address the flowing question: how can the accuracy of a prediction system for light rail systems be measured and improved? The real-time prediction is an output of a telecommunication system, named Automatic Vehicle Location System, which computerizes the predictions. In order to improve a system, it is first important to understand how it works. The mechanism of the prediction computation will be analyzed and each part of the process will be studied in order to seek potential improvements. The first part of the prediction scheme development consists in a statistical analysis of historical data to provide the reference travel times and dwell times and their variations along a day or along a week. Then, two models (the designed-speed model and the speed/position model) will be studied to estimate the remaining time to reach the downstream stop. This estimation is mainly based on the current data (vehicle position and speed). The proposed prediction schemes were implemented and applied for a case study light rail line. Bybanen, a light rail train in Bergen was selected as case study. Real-time information displays are available at all platforms and refer to the waiting to the next two light rail trains. This study focuses on improving the accuracy of these waiting times predictions. In order to establish and analyze the performance of the current prediction scheme, a model for reproducing these computations was developed. Then, the possible improvements have been implemented in the model and the accuracy of the new predictions has been compared to the base case. The assessment and the comparison of prediction systems are not trivial tasks. Which predictions should be taken into account? How does the model identify inconsistency in the data? How could the perception of passengers be taken into account? A set of measures has been used in order to evaluate alternative prediction schemes. The comparison of the different models shows that it is possible to improve the accuracy of the short-term predictions, but it is more difficult to improve the accuracy of long-term predictions because the incertitude of small changes has more impact in long-term predictions. This thesis shows that the reference travel times and dwell times should be assimilated to the most common value instead of the average which is too dependent on high values. Moreover, the dwell time variations are related to the flow passengers. Finally, the most accurate and efficient model is the designed-speed model. The speed/position model is a bit less accurate except in the case of disturbances along the line but its modularity made easier possible improvements. Finally, this paper highlights the time-depending variations of the dwell time in the case of a light rail train system. It could be interesting to analyze the behavior of variations of two consequent dwell times and to implement a forgetting factor. Moreover, the speed-position model shows really good results and a better understanding of the drivers’ behaviors is a key to improve the model. Finally, the differences between the different models will be probably larger for a middle-distance train system, which could be an interesting application of this thesis.