Service-oriented computing and model-driven development as enablers of port information systems: an integrated view

Port information management is considered a critical instrument towards enabling international transport and trade; thus, various forms of Port Information Systems (PORTIS) have been developed today, namely Port Community Systems, Terminal Operating Systems and Single Window systems. In Europe, the nautical information system SafeSeaNet is viewed as an important e-infrastructure. PORTIS systems are expected to evolve into next-generation technological platforms in order to offer a fully integrated digital environment to a multitude of maritime business actors and public authorities towards more efficient, safe and environment-aware transport and trade infrastructures and services. We propose service-oriented computing and model-driven development techniques as a robust PORTIS modelling and development approach. We present a reference model of PORTIS and a particular enabling methodological and technological framework. The proposed approach has been tested in a maritime single-window case.     

Knowledge based decision support in airport terminal design

The state of the art in airport terminal design has essentially remained unchanged since the 1970s, relying on the use of detailed simulation together with empirical and statistical formulae for sizing the terminal. Air transport has altered considerably since then, with increased use of air travel for both business and leisure, together with the increased use of ‘hub’ terminals in which the terminal acts as an interchange stop on a journey involving several flights. The use of simulation involves a detailed analysis of the terminal design under one set of conditions. To change the design or operating conditions involves a complete resimulation using the new design parameters which is time consuming and computationally expensive.

By using a knowledge based approach, a system can be provided which has the flexibility and speed required to explore the consequences of implementing design decisions in a variety of conditions, together with the ability to use and alter facility sizing methodologies to reflect current and future design guidelines.     

Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review

Accurate measurement of travel behaviour is vital for transport planning, modelling, public health epidemiology, and assessing the impact of travel interventions. Self-reported diaries and questionnaires are traditionally used as measurement tools; advances in Global Positioning Systems (GPS) technology allow for comparison. This review aimed to identify and report about studies comparing self-reported and GPS-measured journey durations. We systematically searched, appraised, and analysed published and unpublished articles from electronic databases, reference lists, bibliographies, and websites up to December 2012. Included studies used GPS and self-report to investigate trip duration. The average trip duration from each measure was compared and an aggregated, pooled estimate of the difference, weighted by number of trips, was calculated. We found 12 results from eight eligible studies. All studies showed self-reported journey times were greater than GPS-measured times. The difference between self-report and GPS times ranged from over-reporting of +2.2 to +13.5 minutes per journey. The aggregated, pooled estimate of the difference, weighted by number of trips, was over-report of +4.4 minutes (+28.6%). Studies comparing self-reported and GPS-measured journey duration have shown self-reported to be consistently over-reported across the study sample. Our findings suggest that when using self-reported journey behaviour, the journey durations should be treated as an over-estimation.     

Computational methodology to predict injury risk for motor vehicle crash victims: A framework for improving Advanced Automatic Crash Notification systems

Advanced Automatic Crash Notification (AACN) systems, capable of predicting post-crash injury severity and subsequent automatic transfer of injury assessment data to emergency medical services, may significantly improve the timeliness, appropriateness, and efficacy of care provided. The estimation of injury severity based on statistical field data, as incorporated in current AACN systems, lack specificity and accuracy to identify the risk of life-threatening conditions. To enhance the existing AACN framework, the goal of the current study was to develop a computational methodology to predict risk of injury in specific body regions based on specific characteristics of the crash, occupant and vehicle. The computational technique involved multibody models of the vehicle and the occupant to simulate the case-specific occupant dynamics and subsequently predict the injury risk using established physical metrics. To demonstrate the computational-based injury prediction methodology, three frontal crash cases involving adult drivers in passenger cars were extracted from the US National Automotive Sampling System Crashworthiness Data System. The representative vehicle model, anthropometrically scaled model of the occupant and kinematic information related to the crash cases, selected at different severities, were used for the blinded verification of injury risk estimations in five different body regions. When compared to existing statistical algorithms, the current computational methodology is a significant improvement toward post-crash injury prediction specifically tailored to individual attributes of the crash. Variations in the initial posture of the driver, analyzed as a pre-crash variable, were shown to have a significant effect on the injury risk.     

A technique for the investigation of scheme benefits in large-scale transport networks

Existing methods of evaluating large-scale transport networks involve the use of mathematical models of traffic flow which are generally both large and complex. However, the time and cost involved in the use of these models normally restricts their use for the detailed forecasting of traffic flows and costs to the assessment of a relatively small number of alternative patterns of overall investment. In order to evaluate the individual projects and groups of projects which go to make up an overall investment plan, it is, therefore, usually necessary to make simplifying assumptions about the influence of any one project on the overall traffic pattern, so as to isolate it from the influence of neighbouring projects. These assumptions generally result in the loss of a certain amount of the detail normally available from a standard model, and the task of assessing the relative value of different projects and the amount of interaction between them is made more difficult.This paper describes a new technique, designed to permit the evaluation of individual projects whilst still retaining the level of detail available from a full-scale mathematical model. The aim has been to produce a cheap and easy-to-use technique, capable of producing substantially the same results as a standard model. The technique uses newly developed computer algorithms which short-cut the full-scale model by forecasting the changes in an existing travel pattern resulting from the influence of a particular project. Initial tests suggest that approaching the problem in this way can save up to 70% of the computing time and costs involved in the use of a standard model for the evaluation of individual projects.The technique as described here is envisaged as a tool for aiding strategic investment decisions. It can, however, provide data for more detailed investigations, and could, with modifications, carry out these investigations on smaller problems than those for which it was originally designed.Crown copyright reserved, 1973     

Accident cost saving and highway attributes

Two semi-logarithmic regression models are developed to estimate accident rates and accident costs, respectively, for rural non-interstate highways in the state of Iowa. Data on 21,224 accidents occurring between 1989 and 1991 on 17,767 road segments are used in the analysis. Seven road attributes of these road segments are included as predictor variables. Applying the resulting regression models to a rather typical highway upgrade situation, the present value of the accident cost saving is computed. The sensitivity of the estimated cost saving to values for fatal, personal injury, and property damage only accidents is tested.Because factors other than road characteristics greatly influence accident costs, the models developed in this research explain a limited amount of the variance in these costs among road segments. Results of the analysis indicate that the most important attribute associated with accident costs is average daily traffic per lane, followed by conditions requiring passing restrictions and the sharpness of curves. Varying the values for the three categories of accidents shows that results are far more sensitive to the value of personal injuries than fatalities. The feasibility of using predictive models of accident costs in benefit-cost analyses of highway investments is demonstrated.     

The economics of bus deregulation in Britain

This paper reviews some of the arguments which have been put forward on the economics of bus deregulation, particularly the paper by Gwilliam and others. It argues that the experience of road haulage licensing is relevant. The evidence there suggests that liberalization has not had the adverse effects that many predicted and that in most respects one could expect bus deregulation to have no less favourable consequences. The main difference between buses and road haulage is over how far bus drivers might revert to the old practices of the 1920s of chasing, tailing, etc. which were said at the time to be a major cause of consumer dissatisfaction. The paper reviews the economics of some of these practices and suggests which might be discounted and which need further attention.