An Online Decision-Theoretic Pipeline for Responder Dispatch

The problem of dispatching emergency responders to service traffic accidents, fire, distress calls and crimes plagues urban areas across the globe. While such problems have been extensively looked at, most approaches are offline. Such methodologies fail to capture the dynamically changing environments under which critical emergency response occurs, and therefore, fail to be implemented in practice. Any holistic approach towards creating a pipeline for effective emergency response must also look at other challenges that it subsumes - predicting when and where incidents happen and understanding the changing environmental dynamics. We describe a system that collectively deals with all these problems in an online manner, meaning that the models get updated with streaming data sources. We highlight why such an approach is crucial to the effectiveness of emergency response, and present an algorithmic framework that can compute promising actions for a given decision-theoretic model for responder dispatch. We argue that carefully crafted heuristic measures can balance the trade-off between computational time and the quality of solutions achieved and highlight why such an approach is more scalable and tractable than traditional approaches. We also present an online mechanism for incident prediction, as well as an approach based on recurrent neural networks for learning and predicting environmental features that affect responder dispatch. We compare our methodology with prior state-of-the-art and existing dispatch strategies in the field, which show that our approach results in a reduction in response time with a drastic reduction in computational time.Comment: Appeared in ICCPS 201

Artificial Intelligence for Emergency Response

Emergency response management (ERM) is a challenge faced by communities across the globe. First responders must respond to various incidents, such as fires, traffic accidents, and medical emergencies. They must respond quickly to incidents to minimize the risk to human life. Consequently, considerable attention has been devoted to studying emergency incidents and response in the last several decades. In particular, data-driven models help reduce human and financial loss and improve design codes, traffic regulations, and safety measures. This tutorial paper explores four sub-problems within emergency response: incident prediction, incident detection, resource allocation, and resource dispatch. We aim to present mathematical formulations for these problems and broad frameworks for each problem. We also share open-source (synthetic) data from a large metropolitan area in the USA for future work on data-driven emergency response.Comment: This is a pre-print for a book chapter to appear in Vorobeychik, Yevgeniy., and Mukhopadhyay, Ayan., (Eds.). (2023). \textit{Artificial Intelligence and Society}. ACM Pres

A Structured Literature Review

Publisher Copyright: © 2022 by the authors.A significant amount of research has been conducted on the resource allocation in fire departments (RAFD) and literature reviews about the fire protection service (FPS), but to the best of our knowledge, no literature reviews have been conducted about the RAFD. Therefore, the purpose of this research is to review literature about allocating resources to urban fire departments (FDs) to gain state-of-the-art knowledge of RAFD and identify the most frequent methodologies and measures in the studies. A five-stage structured literature review (SLR) is undertaken to analyze the RAFD-related studies; subsequently, statistical analysis is used to disclose additional information from the retrieved data and develop a general framework for RAFD. According to the structured literature review, which yielded 417 independent variables for RAFD, integer programming (IP) and data envelopment analysis (DEA) are the most common approaches for RAFD among the mathematical and statistical models in the evaluated articles. Based on the findings, a general conceptual framework for RAFD is suggested. The findings of this study can help public and private FDs and FPS managers, decision-makers, resource allocation (RA) researchers, and academicians gain state-of-the-art knowledge of RAFD. The proposed RAFD framework can provide the FPS decision-makers with the appropriate method and variables for allocating their limited resources in a more efficient way within their FDs.publishersversionpublishe

Modelling blue-light ambulance mobility in the London metropolitan area

Actions taken immediately following a life-threatening incident are critical for the survival of the patient. In particular, the timely arrival of ambulance crew often makes the difference between life and death. As a consequence, ambulance services are under persistent pressure to achieve rapid emergency response. Meeting stringent performance requirements poses special challenges in metropolitan areas where the higher population density results in high rates of life-threatening incident occurrence, compounded by lower response speeds due to traffic congestion. A key ingredient of data-driven approaches to address these challenges is the effective modelling of ambulance movement thus enabling the accurate prediction of the expected arrival time of a crew at the site of an incident. Ambulance mobility patterns however are distinct and in particular differ from civilian traffic: crews travelling with ashing blue lights and sirens are by law exempt from certain traffic regulations; and moreover, ambulance journeys are triggered by emergency incidents that are generated following distinct spatial and temporal patterns. We use a large historical dataset of incidents and ambulance location traces to model route selection and arrival times. Working on a road routing network modified to reflect the differences between emergency and regular vehicle traffic, we develop a methodology for matching ambulances Global Positioning System (GPS) coordinates to road segments, allowing the reconstruction of ambulance routes with precise speed data. We demonstrate how a road speed model that exploits this information achieves best predictive performance by implicitly capturing route-specific patterns in changing traffic conditions. We then present a hybrid model that achieves a high route similarity score while minimising journey duration error. This hybrid model outperforms alternative mobility models. To the best of our knowledge, this study represents the first attempt to apply data-driven methodologies to route selection and estimation of arrival times of ambulances travelling with blue lights and sirens

Disaster management in industrial areas: perspectives, challenges and future research

Purpose: In most countries, development, growth, and sustenance of industrial facilities are given utmost importance due to the influence in the socio-economic development of the country. Therefore, special economic zones, or industrial areas or industrial cities are developed in order to provide the required services for the sustained operation of such facilities. Such facilities not only provide a prolonged economic support to the country but it also helps in the societal aspects as well by providing livelihood to thousands of people. Therefore, any disaster in any of the facilities in the industrial area will have a significant impact on the population, facilities, the economy, and threatens the sustainability of the operations. This paper provides review of such literature that focus on theory and practice of disaster management in industrial cities. Design/methodology/approach: In the paper, content analysis method is used in order to elicit the insights of the literature available. The methodology uses search methods, literature segregation and developing the current knowledge on different phases of industrial disaster management. Findings: It is found that the research is done in all phases of disaster management, namely, preventive phase, reactive phase and corrective phase. The research in each of these areas are focused on four main aspects, which are facilities, resources, support systems and modeling. Nevertheless, the research in the industrial cities is insignificant. Moreover, the modeling part does not explicitly consider the nature of industrial cities, where many of the chemical and chemical processing can be highly flammable thus creating a very large disaster impact. Some research is focused at an individual plant and scaled up to the industrial cities. The modeling part is weak in terms of comprehensively analyzing and assisting disaster management in the industrial cities. Originality/value: The comprehensive review using content analysis on disaster management is presented here. The review helps the researchers to understand the gap in the literature in order to extend further research for disaster management in large scale industrial cities.Peer Reviewe

Uncertainty and transparency:augmenting modelling and prediction for crisis response

Emergencies are characterised by uncertainty. This motivates the design of information systems that model and predict complex natural, material or human processes to support understanding and reduce uncertainty through prediction. The correspondence between system models and reality, however, is also governed by uncertainties, and designers have developed methods to render ‘the world’ transparent in ways that can inform, fine-tune and validate models. Additionally, people experience uncertainties in their use of simulation and prediction systems. This is a major obstacle to effective utilisation. We discuss ethically and socially motivated demands for transparency