This article quantifies the relationship between market size and innovation in the pharmaceutical

Our results suggest that (i) LYS are larger with VAT reduction than F&V stamps policies, (ii) information campaigns are the most cost-effective and (iii) market forces can limit the impacts of public health policies designed to favour F&V consumption increase.

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cannot associate a 100% probability of certainty to the events the data represent. The technique captures and combines whatever certainty or knowledge exists in the event classification capability of the information sources. The knowledge contributed by the information sources is combined, by using Dempster's rule, to find the conjunction of the events and the associated probability or belief that the decision is correct (1-4).The U.S. Department of Defense (DoD) data fusion model and Dempster-Shafer inference, in particular, as a tool for combining multisource data are discussed in the following sections. Field test data from inductive loop detector (ILD) pairs and toll collection stations are then used to demonstrate application of the technique to travel time analysis and estimation. This process involves computing a confusion matrix that describes the likelihood the sensors or toll collection devices are reporting correct travel times. Information derived from electronic toll collection (ETC) with tags, real-time credit card payments from the credit-card-only lane, and cash payments are used to find the "true" travel time values (used as the reference value) against which travel time estimates gathered from ILD data, toll tag data only, and toll tag plus real-time credit card payment data are compared. Travel times computed from toll tag data alone are an important travel time information source as those data can be supplied in real time to traffic management centers and used to supplement travel times derived from loops. DATA FUSION MODELMany data-processing techniques originally developed by the DoD to support identification and tracking of military objects, including Dempster-Shafer inference, can be used today to aid traffic management on surface streets and highways (5-7). The DoD data fusion model consists of a hierarchy of five processing levels. Level 0 deals with preprocessing of data from the contributing source. It may normalize, format, order, batch, and compress input data (7,8). It may even identify subobjects or features in the data that are used later in Level 1 processing.For highway and arterial traffic management, Level 1 processing concerns data gathering from all appropriate sources, including realtime point and wide-area traffic flow sensors, transit system operators, toll data, cellular telephone calls, emergency call box reports, probe vehicle and roving tow truck messages, commercial vehicle transmissions, and roadway-based weather sensors (3).Level 2 processing identifies the probable situation causing the observed data and events by combining the results of Level 1 processing with information from other sources and databases. These Availability, accuracy, and reliability of systemwide travel time information contribute to effective decision making in support of safe and efficient operation of traffic management systems. Such information is made available in modern traffic management systems through sensor and data-processing technologies that gather real-time traffic and weather da...

This article proposes data fusion from different sources to improve estimation and prediction accuracy of traffic states on motorways. This is demonstrated in two case studies on an intraurban and an interurban motorway section in Austria. Data fusion in this case combines local detector data and speed data from the Electronic Toll Collection (ETC) system for heavy goods vehicles (HGV). A macroscopic model for open motorway sections has been used to estimate passenger car and HGV density, applying a standard state‐space model and a linear Kalman filter. The resulting historical database of 4 months of speed‐density patterns has been used as a basis for pattern recognition. A nonparametric kernel predictor with memory length of 9 and 18 hours has been used to predict HGV speed for a prediction horizon of 15 minutes to 2 hours. Results show good overall prediction accuracy. Correlation analysis showed little bias of predicted speed for free flow and congested time intervals, whereas transition states between free flow and congestion were frequently biased. Prediction accuracy can be improved by applying a combination of different prediction methods. On the other hand, computational performance of the prediction has to be further improved prior to implementation in a traffic management center.