Rating-Based Guidance System for Public Safety Using Classified Localities: Public Safety Application

Rating-Based Guidance System for Public Safety Using Classified Localities: Public Safety Application

Y. Venkataramana Lokeswari, Venkata Vara Prasad D., Shomona Gracia Jacob, Mohamed Musaraf P. M., Babu Aravind, P. B. Mohanram
Copyright: © 2024 |Pages: 21
DOI: 10.4018/979-8-3693-1702-0.ch016
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Abstract

To automate the manual SOS procedure given in Kavalan mobile application and to provide users a safe path by the given source and destination (from the users). This work uses regression algorithms such as linear regression, decision trees, or support vector machines (SVM) to predict the rating for a current zone which can be used as input for generating the graph with rating as weights and the graph is used as the input for the Dijkstra algorithm which produces the safest path based on the rating. Thus, this path can be used to navigate the public safely to their destination while avoiding unsafe zones. Furthermore, a feedback form is available using which the user can provide textual as well as numerical feedback regarding the places they travel. Decision tree regression provides an accuracy of 89.4% compared with the other regression models since the dataset is categorical and less in size. The safety path is also being produced using Dijkstra's algorithm and the feedback is analysed using the T5 model.
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Liu et. al 2019 develops a big data and network governance approach to support and guarantee for urban public safety management, mainly in technology, concept and form. It proposes a combination of application and development of big data with actual needs of urban public safety management at the current stage. As a city is the ecosystem and the subsystems are citizens, urban cities, urban transportation facilities and urban communications, the main goal is to build a social stability early warning platform through the use of big data and other advanced technologies. Haishuo Gu et. al 2020 proposed a typical work in big data analysis in policing. The usual mobility pattern of passengers was analyzed, and some pickpocketing suspects were detected by defining some specific rules. The results indicated that significant traveling patterns exist for routine passengers, and some exceptional individuals who have a unique pattern which is quite different from routine passengers are discovered. The data available for analysis was accessed from Beijing Metro Subway Co Ltd. The attributes in the dataset include - Transaction date, End time, Start time, End line number, Start line number, End station number, Start station number, Card type (the metro subway card has many classifications, including general card, student card, elderly card), Transaction code (transaction status types, including progress transactions and outbound Consumption) and Card ID. The classification of data is based on card number and checking whether they are abnormal or not (based on few conditions). If the cards are abnormal, trace the traveled path (trajectories).

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