Sustainable freight: impacts of the London congestion charge and low emissions zone

This paper assesses whether two sustainability policies currently in effect in London, a congestion charge zone and a low emission zone, have affected freight operations and reduced vehicle kilometers travelled. It investigates responses by freight operators, including re-timing, re-routing, or reducing the number of trips, or replacing vehicles. Freight traffic trends from 1994 to 2012 were identified using road traffic estimates, cordon counts, and vehicle speed data and supplemented by interviews with freight industry experts and operators. Findings indicate that freight traffic increased throughout London during this timeframe, but declined in the central boroughs partly within the congestion charge zone. The congestion charge may have time-shifted some light goods trips, but most freight trips face a variety of constraints on operators’ delivery window. No evidence was found of re-routing of freight traffic or avoidance traffic around the charged zone. The low emission zone spurred higher levels of operational change than the congestion charge zone, and it was effective at spurring freight vehicle replacement. The paper also discusses freight operators’ perceptions of these policies and how they could be improved.

Risk Modelling Framework for Emergency Hospital Readmission, Using Hospital Episode Statistics Inpatient Data

The objective of this study was to develop, test and benchmark a framework and a predictive risk model for hospital emergency readmission within 12 months. We performed the development using routinely collected Hospital Episode Statistics data covering inpatient hospital admissions in England. Three different timeframes were used for training, testing and benchmarking: 1999 to 2004, 2000 to 2005 and 2004 to 2009 financial years. Each timeframe includes 20% of all inpatients admitted within the trigger year. The comparisons were made using positive predictive value, sensitivity and specificity for different risk cut-offs, risk bands and top risk segments, together with the receiver operating characteristic curve. The constructed Bayes Point Machine using this feature selection framework produces a risk probability for each admitted patient, and it was validated for different timeframes, sub-populations and cut-off points. At risk cut-off of 50%, the positive predictive value was 69.3% to 73.7%, the specificity was 88.0% to 88.9% and sensitivity was 44.5% to 46.3% across different timeframes. Also, the area under the receiver operating characteristic curve was 73.0% to 74.3%. The developed framework and model performed considerably better than existing modelling approaches with high precision and moderate sensitivity.