Efficient management of patient flow (demand) in emergency departments (EDs) has become an urgent issue for many hospital administrations. Today, more and more attention is being paid to hospital management systems to optimally manage patient flow and to improve management strategies, efficiency and safety in such establishments. To this end, EDs require significant human and material resources, but unfortunately these are limited. Within such a framework, the ability to accurately forecast demand in emergency departments has considerable implications for hospitals to improve resource allocation and strategic planning. The aim of this study was to develop models for forecasting daily attendances at the hospital emergency department in Lille, France. The study demonstrates how time-series analysis can be used to forecast, at least in the short term, demand for emergency services in a hospital emergency department. The forecasts were based on daily patient attendances at the paediatric emergency department in Lille regional hospital centre, France, from January 2012 to December 2012. An autoregressive integrated moving average (ARIMA) method was applied separately to each of the two GEMSA categories and total patient attendances. Time-series analysis was shown to provide a useful, readily available tool for forecasting emergency department demand.

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Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.

Monitoring of production systems, such as those in hospitals, is primordial for ensuring the best management and maintenance desired product quality. Detection of emergent abnormalities allows preemptive actions that can prevent more serious consequences. Principal component analysis (PCA)-based anomaly-detection approach has been used successfully for monitoring systems with highly correlated variables. However, conventional PCA-based detection indices, such as the Hotelling's T 2 and the Q statistics, are ill suited to detect small abnormalities because they use only information from the most recent observations. Other multivariate statistical metrics, such as the multivariate cumulative sum (MCUSUM) control scheme, are more suitable for detection small anomalies. In this paper, a generic anomaly detection scheme based on PCA is proposed to monitor demands to an emergency department. In such a framework, the MCUSUM control chart is applied to the uncorrelated residuals obtained from the PCA model. The proposed PCA-based MCUSUM anomaly detection strategy is successfully applied to the practical data collected from the database of the pediatric emergency department in the Lille Regional Hospital Centre, France. The detection results evidence that the proposed method is more effective than the conventional PCA-based anomaly-detection methods.

As they involve many interacting agents behaving in numerous ways that are extremely difficult to predict, urban transportation systems are complex in nature. The development of intermodal passenger transportation solutions to address the mobility issues constitutes a major thrust area of urban transport policies. But, to offer citizens comprehensive seamless mobility, intermodal transportation system management (ITSM) requires the integration of two major components. The traffic regulation support system, to help the operator responsible for the regulation tasks: coordination of timetables, synchronising arrival and departure times between the different transportation modes, and the traveller information system, giving customers access to information and using a comprehensive set of information tools. In this paper, a generic model of a transport management system, integrating these two components is proposed. This generic model is then used to elaborate a traffic regulation system in the case of a bimodal transportation system (tram-bus). The traffic regulation support system, based on the decision model of an operator, and the traveler information system are described.

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International audienceThanks to the important and increasing growth of the carpooling phenomenon throughout the world, many researchers have particularly focused their efforts on this concept. Researches led to many systems affording carpooling service not usually effective. In fact, most of them present multiple drawbacks regarding automation, functionalities, accessibility, etc. Besides, only few researchers focused on real time carpooling concept without producing promising results. To address these gaps, we introduce a novel approach called DARTiC: a Distributed dijkstra for the implementation of a Real Time Carpooling system based on the multi-agent concept, we particularly focus on the distributed and dynamic aspect within dijkstra's implementation. A new modeling of the served network highlights the distributed architecture, helping to perform decentralized parallel process. This helped to take into consideration different aspects we should be involved in, especially optimization issue. Users' requests must be performed in a reasonable time and responses should be as efficient as possible with regards to the fixed optimization criteria

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