Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions are tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast with the assumed complexity of evacuation decision making mechanisms in crisis situations, the individuals' evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost, by using population density information and seismic intensity which can be observed instantaneously after the shock

Traffic Speed Prediction and Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data

Providing accurate traffic speed prediction is essential for the success of Intelligent Transportation Systems (ITS) deployments. Accurate traffic speed prediction allows traffic managers take proper countermeasures when emergent changes happen in the transportation network. In this thesis, we present a computationally less expensive machine learning approach XGBoost to predict the future travel speed of a selected sub-network in Beijing\u27s transportation network. We perform different experiments for predicting speed in the network from future 1 min to 20 min. We compare the XGBoost approach against other well-known machine learning and statistical models such as linear regression and decision tree, gradient boosting tree, and random forest regression models. Three metrics MAE, MAPE, and RMSE are used to evaluate the performance of the selected models. Our results show that XGBoost outperforms other models across different experiment conditions. Based on the prediction accuracy of different links, we find that the number of vehicles operating in a network also affect prediction performance. In addition, understanding individual mobility behavior is critical for modeling urban dynamics. It provides deeper insights on the generative mechanisms of human movements. Recently, different types of emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used to analyze individual mobility behavior. In this thesis, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel data, we develop an algorithm to identify users\u27 visited places and the functions of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited place and their rank, the spatial distribution of residences and workplaces, and the distribution of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. Our study shows a tremendous potential of developing high-resolution individual-level mobility model that can predict the demand of emerging mobility services with high accuracy