Faster Evacuation after Disaster: Finding Alternative Routes using Probable Human Behavior

This poster presents an app that can help disaster affected communities find efficient and safe evacuation routes to reduce the loss of human and resources, both during and after a disaster has hit. This proposed app will navigate people seeking evacuation through suitable routes based on geographical condition, structural vulnerability, disaster severity, traffic density, human mobility, etc. The choice of most effective and safe evacuation paths primarily relies on stochastic probability of human movement and requires frequently updated data. In order to achieve this, the app uses real time GPS data by simulating the movement pattern of its users connected to network as well as their previous movement patterns when they are found offline. This simulation process will find out the less congested and safer routes for faster traversal. Users can use these path suggestions to safely drive themselves out of the disaster stricken area. In case of a user being offline, this app will use data stored on the device to suggest evacuation routes based on human mobility pattern. The implementation of this idea will help the app users evacuate safely and quickly, thus minimizing human casualty due to disaster fatality

Investigasi Perilaku Spontan Individu Saat Bencana Alam: Dalam dan Luar Bangunan

Indonesia is prone to natural disasters that can occur unexpectedly and gradually. Natural disasters implement surprising impacts due to a lack of awareness and preparedness in facing the threats. One of the reasons caused by such a disaster that can be seen is through human behaviour. Human behaviour is challenging to predict in an emergency, stressful and chaotic. Spontaneous behaviour is distinguished by location factors, indoor or outdoor, with indoor, divided by Home and Public Building. This research aims to reveal spontaneous human behaviour during natural disasters while inside and outside the building. This research was conducted with a qualitative exploratory method. Data were collected using an online questionnaire with open-ended questions and distributed freely. The findings show tendencies to withstand when in the public building while going to distance themselves from building while at home, as for those outdoor opt to surrender as not to do anything  Indonesia merupakan negara yang rawan terhadap bencana alam, baik bencana yang dapat terjadi secara tiba-tiba maupun perlahan. Bencana alam yang terjadi dapat memberikan dampak kejutan akibat kurangnya kewaspadaan dan persiapan dalam menghadapi ancaman. Salah satu respons yang terlihat dari dampak yang ditimbulkan oleh bencana tersebut adalah pada pola perilaku manusia. Perilaku manusia sangat sulit untuk diprediksi saat berada di keadaan darurat yang menegangkan dan kacau balau tersebut. Perilaku spontan yang terjadi dipengaruhi faktor lokasi keberadaan, yaitu saat berada di dalam bangunan seperti dalam bangunan tempat tinggal dan bangunan umum lainnya, atau di luar bangunan. Tujuan penelitian ini adalah untuk mengungkap perilaku spontan manusia yang dilakukan saat terjadi bencana alam saat berada di dalam dan luar bangunan. Penelitian ini merupakan penelitian kualitatif yang bersifat eksploratif. Data dikumpulkan menggunakan kuesioner daring dengan pertanyaan yang bersifat terbuka (open-ended) dan dibagikan secara bebas. Hasil analisis menunjukkan bahwa dalam merespons kejadian bencana alam, terdapat kecenderungan manusia untuk tetap bertahan saat berada di bangunan umum dan memilih untuk menjauhi bangunan saat berada di tempat tinggal. Sedangkan individu yang sedang berada di luar bangunan, mereka akan cenderung memilih untuk tidak bertindak sama sekali

UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

Big Data Analytics: Towards a Model to Understand Development Equity for Villages in Indonesia

The aim of this paper is to design a prototype model that can be used to better understand development equity for villages in terms of public monitoring and evaluation. In designing the model, the research has reviewed several techniques of big data analytics as well as alignment of business strategic objectives and technology. The prototype model also tested using several types of data. Although some obstacles have found, as it also found in the reviewed literature, a prototype model which can guide researchers and practitioners to understand ways to capture public monitoring is presented in this paper. Furthermore, Information systems researchers could use this prototype model for further research to get a deeper understanding of big data analytics roles for development, particularly in developing countries. © The Authors, published by EDP Sciences, 2018

Predicting Evacuation Decisions using Representations of Individuals' Pre-Disaster Web Search Behavior

Predicting the evacuation decisions of individuals before the disaster strikes is crucial for planning first response strategies. In addition to the studies on post-disaster analysis of evacuation behavior, there are various works that attempt to predict the evacuation decisions beforehand. Most of these predictive methods, however, require real time location data for calibration, which are becoming much harder to obtain due to the rising privacy concerns. Meanwhile, web search queries of anonymous users have been collected by web companies. Although such data raise less privacy concerns, they have been under-utilized for various applications. In this study, we investigate whether web search data observed prior to the disaster can be used to predict the evacuation decisions. More specifically, we utilize a "session-based query encoder" that learns the representations of each user's web search behavior prior to evacuation. Our proposed approach is empirically tested using web search data collected from users affected by a major flood in Japan. Results are validated using location data collected from mobile phones of the same set of users as ground truth. We show that evacuation decisions can be accurately predicted (84%) using only the users' pre-disaster web search data as input. This study proposes an alternative method for evacuation prediction that does not require highly sensitive location data, which can assist local governments to prepare effective first response strategies.Comment: Accepted in ACM KDD 201