Multi-Sensor Data Fusion for Travel Time Estimation

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases