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Research on public charging station (PCS) selection has accumulated a large variety of variables that have been shown to affect charging behavior. We offer a human-centered framework to classify and integrate variables that have been described in the literature. Different from previous overviews, the framework focuses on the cognitive decision-making processes that are employed by human deciders. Every charging event includes a human decision that involves three dimensions: where to charge the vehicle (location), when to charge the vehicle (time), and for how long the vehicle is being charged (duration). The framework provides an overview of variables that have been studied in previous research and can be linked to these three dimensions. As a step to validate the framework, we asked 1,019 participants (including 667 owners of EVs or hybrid cars) how important each of 22 choice attributes would be for them when choosing a charging station. A factor analysis revealed the following six factors in descending order of perceived importance: costs, accessibility, time, past experience (self and other), amenities, and provider attributes. EV owners were also asked when and for how long they typically charge their vehicle. A factor analysis of the description of the time of charging confirmed a three-factor structure of range, finances, and habit. Results revealed systematic differences in the time and duration of charging between owners of hybrid cars and plug-in cars. Future research questions are discussed including the relevance of human-centered approaches for policies on charging station deployment and infrastructure planning. 

Abstract In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses ( what if the disaster did not occur? ), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes. 

Despite the rising importance of enhancing community resilience to disasters, our understandings on when, how and why communities are able to recover from such extreme events are limited. Here, we study the macroscopic population recovery patterns in disaster affected regions, by observing human mobility trajectories of over 1.9 million mobile phone users across three countries before, during and after five major disasters. We find that, despite the diversity in socio-economic characteristics among the affected regions and the types of hazards, population recovery trends after significant displacement resemble similar patterns after all five disasters. Moreover, the heterogeneity in initial and long-term displacement rates across communities in the three countries were explained by a set of key common factors, including the community’s median income level, population, housing damage rates and the connectedness to other cities. Such insights discovered from large-scale empirical data could assist policymaking in various disciplines for developing community resilience to disasters.