Estimating train passenger load from automated data systems : application to London Underground

Abstract

The purpose of this thesis is to assess the feasibility of identifying which trains individual passengers take to get from their origin to destination while travelling in a high frequency urban rail transportation system. If this proves possible, the resulting information will inform capital and operations planning decisions as well as improve the ability to measure the aspects of passenger experience related to travel time and crowding. This thesis will first explore this idea by presenting the design, implementation, and application of a model that attempts to identify the selected train level itineraries through a temporal and spatial matching process. As a result of this process, the model is designed to estimate passenger loads, walk times, and the number of left behind passengers. The thesis will then assess the accuracy of these results by comparing them with figures produced by existing models. The model will be developed and applied in the context of the London Underground, but should also be applicable to other urban public transportation systems. Assessment of the results of this model and consideration of the challenges in the creating the model does not conclusively indicate that identifying the exact train a passenger selects to get from his origin to destination is possible. However, the results do indicate that the model has significant potential, and can be improved in future research. These initial results can serve as indicators on how to improve the model.