Review of survey techniques used in urban freight studies

Report produced as part of the Green Logistics project (EPSRC and Department for Transport funded). This report is based on a review of studies in which data has been collected to obtain an understanding of road-based urban freight transport activities and patterns of operation. Studies from the UK and other countries have been included in this review. While it may be thought that relatively few such studies have been conducted, approximately 60 such studies have been identified as taking place in the UK and approximately 100 elsewhere since the 1960s. In addition, other studies have been carried out in order to assess industry and policy maker opinions about urban freight transport , however this type of study and survey work is not the focus of this report. Gaining an understanding of road-based urban freight transport activities is an important element in determining the current sustainability of such activity (in economic, social and environmental terms) and how best to go about enhancing its sustainability. By reviewing the existing survey work in this subject it has been possible to draw together the methodologies developed and implemented. This should therefore be of help in understanding which techniques are most commonly used, the strengths and limitations of the various techniques, and in assessing the most suitable urban freight survey techniques for a given study.

Survey techniques in urban freight transport studies

This article reviews and assesses the various survey techniques that have been used in studies in which data have been collected to obtain an understanding of road-based urban freight transport activities. The review identified 162 such studies in 18 countries over the last 50 years, and 12 different types of urban survey technique. In the studies reviewed, establishment surveys have been the most commonly used technique, followed by freight operator surveys, vehicle observation surveys, driver surveys, roadside interview surveys, and vehicle trip diaries. Few examples of other survey techniques (commodity flow, parking, global positioning system, supplier and service provider surveys) were identified. Establishment and vehicle observation surveys have the potential to offer insight into a wider range of issues associated with urban freight deliveries and collections to and from establishments in a specific urban area than the other survey techniques reviewed. These two survey techniques can provide insight into the frequency of goods vehicles deliveries and collections by time of day and day of week, the activities involved in the loading/unloading process, and the freight requirements of individual establishments. However, all 12 survey techniques provide insight into varying aspects of urban freight operations and various combinations are therefore likely to be used in future studies.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.