A Bayesian model to improve transit assignment using smart card data

Abstract

Public transport systems are playing a significant role in providing society with cost-effective and efficient access to main services such as markets, employment, and education. Public transport planners can predict passenger loads and levels of service by applying their prior knowledge about the transit network and using transit assignment models. Static Transit Assignment (STA) is the preferred tool for strategic transport planning due to its simplicity and computational efficiency, even for large networks. Strategic transport models are often used to compare different scenarios or variants. In this context, it is important that differences between scenarios can be attributed to the scenarios themselves and not to unstable model results. Also, estimating the route choice factors, considering the fact that network cost attributes might be non-deterministic, is complicated.The individual travel history data available from automated fare collection (AFC) systems bring the opportunity of understanding the individual’s travel behaviour, which is necessary to develop a transit assignment model. By combining the prior knowledge about the transit network with the AFC data, a transit assignment model can be developed.This thesis is dedicated to the application of smart card data in developing an STA model on a large-scale multimodal network, allowing for the uncertainty and the random variations of route choice parameters. To estimate the route choice parameters, a Bayesian Hierarchical model is proposed. The Bayesian model combines prior knowledge, typically in the form of a statistical distribution, with current data (such as AFC data) to derive a meaningful posterior. The inference of the number of passengers on each route, estimated from the proposed model, allows better forecasting and prediction of travellers’ behaviour, and thus enables more efficient service planning for the transit network.Following a sequential approach, this thesis can be divided into six parts: (1) reviewing the literature on the existing transit assignment models and on AFC data applications in public transport networks, (2) AFC data acquisition and preparation, (3) analysing AFC data to detect passengers’ route choice behaviour and defining a variety of factors affecting transit route choice, (4) developing a transit assignment model and calibrating the model using AFC data, (5) undertaking different sensitivity analysis, and (6) evaluating the performance of the model.The six parts have interconnections and overlaps, contributing new knowledge and insights on utilizing AFC data to model and develop the public transport assignment in a large-scale multimodal network. This research has given contributions to a number of components of transit assignment modelling. The contributions of this research are sixfold. First, a new demand assignment framework, taking advantage of AFC data, is proposed to estimate the passenger flow on each segment of a multimodal transit network. Second, by taking travel time variation into consideration, the proposed model can better describe network travel time and its uncertainty on each segment. Third, in order to represent the mode share of different transit modes, the attributes of each mode are considered separately in the model. Fourth, an approach for computing the ESS in a Bayesian model of transit assignment is proposed. Fifth, this is the first study that compares passenger flow assignments for two different spatial resolutions of the OD matrix. Finally, the temporal transferability of the model is verified.