Future Location Prediction for Emergency Vehicles Using Big Data: A Case Study of Healthcare Engineering

Abstract

The number of devices equipped with GPS sensors has increased enormously, which generates a massive amount of data. To analyse this huge data for various applications is still challenging. One such application is to predict the future location of an ambulance in the healthcare system based on its previous locations. For example, many smart city applications rely on user movement and location prediction like SnapTrends and Geofeedia. There are many models and algorithms which help predict the future location with high probabilities. However, in terms of efficiency and accuracy, the existing algorithms are still improving. In this study, a novel algorithm, NextSTMove, is proposed according to the available dataset which results in lower latency and higher probability. Apache Spark, a big data platform, was used for reducing the processing time and efficiently managing computing resources. The algorithm achieved 75% to 85% accuracy and in some cases 100% accuracy, where the users do not change their daily routine frequently. After comparing the prediction results of our algorithm, it was experimentally found that it predicts processes up to 300% faster than traditional algorithms. NextSTMove is therefore compared with and without Apache Spark and can help in finding useful knowledge for healthcare medical information systems and other data analytics related solutions especially healthcare engineering.

Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations

“…Authors concluded that using small cell inside the ambulance could be particularly useful in high bandwidth congestion scenarios. Another way to help address mobility challenges can be to predict the future location of the ambulance based on its previous locations as reported in [ 66 ]. The authors proposed an algorithm, NextSTMove, which is 300% faster than traditional algorithms and achieved accuracies of 75% to 100%.…”

Fuel Consumption Monitoring through COPERT Model—A Case Study for Urban Sustainability

“…The knowledge obtained from movement patterns can help analysts in making informed decision. For example, vehicle future location prediction using geo-spatial big data context within sustainability and urban design are recently reported [22].…”

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

“…Because of fluctuating behavior of BD, the health-care organizations are also changing and updating themselves by deploying new technologies in their organizations (Harerimana et al, 2018;Zhang et al, 2015). Kamal et al (2020) presented a new algorithm, NextSTMove where the future movement of vehicles is predicted with or without Apache Spark. This study is useful in predicting the future location of emergency vehicles.…”