The availability of vast amounts of location-based data from social media platforms such as Twitter has enabled us to look deeply into the dynamics of human movement. The aim of this paper is to leverage a large collection of geo-tagged tweets and the street networks of two major metropolitan areas-London and Tokyoto explore the underlying mechanism that determines the heterogeneity of human mobility patterns. For the two target cities, hundreds of thousands of tweet locations and road segments were processed to generate city hotspots and natural streets. User movement trajectories and city hotspots were then used to build a hotspot network capable of quantitatively characterizing the heterogeneous movement patterns of people within the cities. To emulate observed movement patterns, the study conducts a twolevel agent-based simulation that includes random walks through the hotspot networks and movements in the street networks using each of three distance types-metric, angular and combined. Comparisons of the simulated and observed movement flows at the segment and street levels show that the heterogeneity of human urban movements at the collective level is mainly shaped by the scaling structure of the urban space.

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This article describes the development of a comprehensive simulation model that integrates property damages (building-collapse, fire-spreading, and street-blockage) and various activities (rescue activity, firefighting activity, and wide-area evacuation activity) by local residents in the event of a large earthquake. Using this model, we analyze the effect and risk of rescue and firefighting activities carried out by local residents under the assumption that a large earthquake directly hits the Tokyo area. Furthermore, we attempt to find new knowledge on the situations where there are many fatal casualties caused by people failing to evacuate, being trapped on streets, and so on.

The spatial distribution of people in a building could provide valuable information for an effective guidance in emergency. This paper aims to develop a method to estimate the number of walking people in staircases using infrared human detection sensors. First, we constructed a walking behavior model and a sensor response model using the survey data observed in an emergency drill. Next, using these two models, we generated a large volume training data on human flows and corresponding sensor responses for a neural network model.Finally, we demonstrated the validation of the model through the comparison with the survey data.

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