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Public transport users are increasingly expecting better service and up to date information, in pursuit of a seamless journey experience. In order to meet these expectations, many transport operators are already offering free mobile apps to help customers better plan their journeys and access real-time travel information. Leveraging the spatio-temporal data that such apps can produce at scale (i.e. timestamped GPS traces), opens an opportunity to bridge the gap between passenger expectations and capabilities of the operators by providing a real-time 360-degree view of the transport network based on the ‘Apps as infrastructure’ paradigm. The first step towards fulfilling this vision is to understand which routes and services the passengers are travelling on at any given time. Mapping a GPS trace onto a particular transport network is known as ‘network matching’. In this paper, the problem is formulated as a supervised sequence classification task, where sequences are made of geographic coordinates, time, and line and direction of travel as a label. We present and compare two data-driven approaches to this problem: (i) a heuristic algorithm, which looks for nearby stops and makes an estimation based on their timetables -- used as a baseline -- and (ii) a deep learning approach using a recurrent neural network (RNN). Since RNNs require considerable amounts of data to train a good model, and collecting and labelling this data from real users is a challenging task (e.g. asking too often can be overwhelming; privacy concerns on providing GPS location; not reliable labels due to mistakes or misuse), one of our contributions is a synthetic journey data generator. The datasets that we generated have been made as realistic as possible by querying real timetables and adding position and temporal noise to simulate variable GPS accuracy and vehicle delays, sampled from empirical distributions estimated using thousands of real location reports. To validate our approach we have us more...

The increasing number of passengers using mobile devices like smartphones or tablets in last few years have motivated transport companies to develop mobile websites and apps for their customers. However, the transition from desktop to mobile versions is challenging and many websites are still not optimised for user experience on such devices. In this paper we present a usability study carried out with the timetables of Nottingham City Transport website. A number of design changes have improved the overall user experience as confirmed by the results. more...