Break prediction models can help water utility decision-makers to build pipe rehabilitation programs. For many years, using them has been a specialist matter. After more than 15 years of research into the ageing of water pipes, Irstea (formerly Cemagref) has developed the Linear Extension of the Yule Process (LEYP) model based on counting process theory, which relies not only on a pipe's characteristics and environment but also on its age and previous breaks. It was then decided to develop a break prediction tool usable by water utilities: the ‘Casses’ freeware. To make this possible, it was necessary to deal with several constraints. To cope with the diversity of available data for various water utilities, flexible input data formats were designed as well as an importation module which checks the conformity and coherence of data. Tools for data management and an advice module dedicated to model calibration were conceived for non-statistician users. The break prediction results can be used directly to compare break evolution with different rehabilitation strategies and they can also feed multicriteria decision tools. In this case, the ‘Casses’ freeware can work as a ‘slave’ of the integrated application.
A core ingredient of Smart Cities is the use of emergency services both as a lens through which to monitor their everchanging state and as a rapid response mechanism to the needs of their population. Emergency response units in particular employ diverse ubiquitous computing technologies for sensing, resilient communication, and dispatch and depend on extensive command and control infrastructure that links into the healthcare and transportation systems. In the case of ambulance services in particular, command and control centres collate medical incident, vehicle position and status data to build a realtime picture of the City. Taking the London Ambulance Service (LAS) as our case study we develop a simulation framework and introduce an enhanced routing and dispatch method that combines concurrent assignment and redeployment of resources in a single algorithm. We provide evidence that our unified proactive relocation and dispatch model produces significant improvements in measured performance in terms of meeting citizen needs.
Actions taken immediately following a life-threatening personal health incident are critical for the survival of the sufferer. The timely arrival of specialist ambulance crew in particular often makes the difference between life and death. As a consequence, it is critical that emergency ambulance services achieve short response times. This objective sets a considerable challenge to ambulance services worldwide, especially in metropolitan areas, where the density of incident occurrence and traffic congestion are high. Using London as a case study, in this paper, we consider the advantages and limitations of data-driven methods for ambulance routing and navigation. Our long-term aim is to enable considerable improvements to their operational efficiency through the automated generation of more effective response strategies and tactics. A key ingredient of our approach is to use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on the London road network graph modified to reflect the differences between emergency and civilian vehicle traffic, we develop a methodology for the precise estimation of expected ambulance speed at the individual road segment level. We demonstrate how a model that exploits this information achieves best predictive performance by implicitly capturing route-specific persistent patterns in changing traffic conditions. We then present a predictive method that achieves a high route similarity score while minimising journey duration error. This is achieved through the combination of a technique that correctly predicts routes selected by the current LAS navigation system and our best performing speed estimation model. This hybrid approach outperforms alternative mobility models. To the best of our knowledge, this paper represents the first attempt to apply a data-driven methodology for route selection and the estimation of arrival times of ambulances travelling with blue lights and sirens on. INDEX TERMS Ambulance mobility, emergency services, routing and navigation, smart cities. MARCUS POULTON received the master's degree in research in computer science from the Birkbeck, University of London, in 2013, where he is currently pursuing the Ph.D. degree with the Department of Computer Science and Information Systems. He is a specialist in emergency services computer systems, and has worked with many U.K. emergency services, including the Metropolitan Police and the London Ambulance Service. His work currently focuses on ambulance mobility and simulation. His research interests also include intelligent systems for the emergency services and smart city technology.
Multi-Robot Task Allocation (MRTA) is the problem of distributing a set of tasks to a team of robots with the objective of optimising some criteria, such as minimising the amount of time or energy spent to complete all the tasks or maximising the efficiency of the team's joint activity. The exploration of MRTA methods is typically restricted to laboratory and field experimentation. There are few existing real-world models in which teams of autonomous mobile robots are deployed "in the wild", e.g., in industrial settings. In the work presented here, a marketbased MRTA approach is applied to the problem of ambulance dispatch, where ambulances are allocated in respond to patients' calls for help. Ambulances and robots are limited (and perhaps scarce), specialised mobile resources; incidents and tasks represent time-sensitive, specific, potentially unlimited, precisely-located demands for the services which the resources provide. Historical data from the London Ambulance Service describing a set of more than 1 million (anonymised) incidents are used as the basis for evaluating the predicted performance of the market-based approach versus the current, largely manual, method of allocating ambulances to incidents. Experimental results show statistically significant improvement in response times when using the market-based approach.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.