Modelling of evolution of urban travel demand is fundamental for urban planners and policy makers to assess the spatial demand for transportation capacity and decide on appropriate interventions. We follow the approach of (Ellam et al., 2018) and introduce a novel application of spatial interaction models and a mathematical evolution of their dynamics to urban travel demand. We exploit economic structure characteristics (e.g. employment) to inform travel demand between a set of origin and destination locations.
Highways England’s objective to improve the health and safety of its customers is tightly linked to better monitoring of on-road water discharge. The increasing climate volatility can impede accurate hydrological modelling and therefore a probabilistic approach is adopted to modelling on-road rainfall-runoff. This report illustrates the potential of using a hybrid of statistical and hydrological models to better understand on-road water conditions. The study area is a major A-road in north-west of England.
This thesis details an approach known as change-point detection (CPD) that aims to detect changes in the mean, variance and covariance of a time series. The scope of CPD is limited to an on-line (real-time) Bayesian spatio-temporal setting. In this setting, the goal of CPD is to provide step-ahead predictions and partition the time series into disjoint segments every time a new datum is received using Bayesian inference.