Enhancing vehicle destination prediction using latent trajectory information

Abstract

Intelligent transportation systems have the potential to provide road users with a range of useful applications, including vehicle preconditioning, traffic flow management and intelligent parking recommendations. The majority of these applications can benefit from knowledge of vehicle activities (common situations that a vehicle encounters e.g. traffic), along with the upcoming destinations that a vehicle will visit. We focus on the trajectories that vehicles provide, and the data contained within them, in order to ascertain information about the patterns in individuals' mobility data. Machine learning has been used in many different vehicle applications, and we focus on using these techniques to predict the activity of a vehicle and its future destinations. Clustering methods can be applied at the level of trajectories or the individual instances within them, and we explore both of these alternatives in this thesis. Additionally, we explore several classification approaches to predict activities and destinations. In developing our methods, we make use of a combination of both geospatial and temporal data along with on-board vehicle sensor data. This thesis presents novel methods for filtering stay points to identify points of interest and applying destination prediction to vehicle trajectories. Existing methods for stay point detection are not specific to vehicles, and therefore any region of low mobility is potentially considered to be of interest. We propose a novel method for filtering the extracted stay points to identify points of interest, using vehicle data to predict vehicle activities. The predicted activities are further used to represent trajectories as sequences of annotated locations, to inform the detection of similarities between journeys. Finally, this thesis presents a novel method for using additional properties of a trajectory to cluster trajectories into groupings of similar trajectories with the aim of improving the accuracy of destination prediction. We evaluate our proposed methods on a set of vehicle datasets, varying in purpose and the data available