scholarly journals Understanding the Impact of Human Mobility Patterns on Taxi Drivers’ Profitability Using Clustering Techniques: A Case Study in Wuhan, China

Information ◽  
2017 ◽  
Vol 8 (2) ◽  
pp. 67 ◽  
Author(s):  
Hasan Naji ◽  
Chaozhong Wu ◽  
Hui Zhang
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Taxi Drivers ◽  
Clustering Techniques ◽  
The Impact

The Impact of Stationarity, Regularity, and Context on the Predictability of Individual Human Mobility

2021 ◽  
Vol 7 (4) ◽  
pp. 1-24
Author(s):  
Douglas Do Couto Teixeira ◽  
Aline Carneiro Viana ◽  
Jussara M. Almeida ◽  
Mrio S. Alvim
Keyword(s):  
State Of The Art ◽  
Human Mobility ◽  
Contextual Information ◽  
Prediction Method ◽  
Mobility Prediction ◽  
Mobility Patterns ◽  
Distinct Cell ◽  
Inherent Nature ◽  
The One ◽  
The Impact

Predicting mobility-related behavior is an important yet challenging task. On the one hand, factors such as one’s routine or preferences for a few favorite locations may help in predicting their mobility. On the other hand, several contextual factors, such as variations in individual preferences, weather, traffic, or even a person’s social contacts, can affect mobility patterns and make its modeling significantly more challenging. A fundamental approach to study mobility-related behavior is to assess how predictable such behavior is, deriving theoretical limits on the accuracy that a prediction model can achieve given a specific dataset. This approach focuses on the inherent nature and fundamental patterns of human behavior captured in that dataset, filtering out factors that depend on the specificities of the prediction method adopted. However, the current state-of-the-art method to estimate predictability in human mobility suffers from two major limitations: low interpretability and hardness to incorporate external factors that are known to help mobility prediction (i.e., contextual information). In this article, we revisit this state-of-the-art method, aiming at tackling these limitations. Specifically, we conduct a thorough analysis of how this widely used method works by looking into two different metrics that are easier to understand and, at the same time, capture reasonably well the effects of the original technique. We evaluate these metrics in the context of two different mobility prediction tasks, notably, next cell and next distinct cell prediction, which have different degrees of difficulty. Additionally, we propose alternative strategies to incorporate different types of contextual information into the existing technique. Our evaluation of these strategies offer quantitative measures of the impact of adding context to the predictability estimate, revealing the challenges associated with doing so in practical scenarios.

scholarly journals Lockdown related travel behavior undermines the containment of SARS-CoV-2

2020 ◽  
Author(s):  
Nishant Kishore ◽  
Rebecca Kahn ◽  
Pamela P. Martinez ◽  
Pablo M. De Salazar ◽  
Ayesha S. Mahmud ◽  
...  
Keyword(s):  
Rural Areas ◽  
Travel Behavior ◽  
Human Mobility ◽  
Unintended Consequences ◽  
Disease Spread ◽  
Mobility Patterns ◽  
Long Distance ◽  
Mobility Data ◽  
Travel Restrictions ◽  
The Impact

ABSTRACTIn response to the SARS-CoV-2 pandemic, unprecedented policies of travel restrictions and stay-at-home orders were enacted around the world. Ultimately, the public’s response to announcements of lockdowns - defined here as restrictions on both local movement or long distance travel - will determine how effective these kinds of interventions are. Here, we measure the impact of the announcement and implementation of lockdowns on human mobility patterns by analyzing aggregated mobility data from mobile phones. We find that following the announcement of lockdowns, both local and long distance movement increased. To examine how these behavioral responses to lockdown policies may contribute to epidemic spread, we developed a simple agent-based spatial model. We find that travel surges following announcements of lockdowns can increase seeding of the epidemic in rural areas, undermining the goal of the lockdown of preventing disease spread. Appropriate messaging surrounding the announcement of lockdowns and measures to decrease unnecessary travel are important for preventing these unintended consequences of lockdowns.

scholarly journals The impact of dengue illness on social distancing and caregiving behavior

2021 ◽  
Vol 15 (7) ◽  
pp. e0009614
Author(s):  
Kathryn L. Schaber ◽  
Amy C. Morrison ◽  
William H. Elson ◽  
Helvio Astete-Vega ◽  
Jhonny J. Córdova-López ◽  
...  
Keyword(s):  
Human Mobility ◽  
Social Contact ◽  
Febrile Illness ◽  
Well Being ◽  
Denv Infection ◽  
Transmission Dynamics ◽  
Mobility Patterns ◽  
Social Contacts ◽  
Infectious Individual ◽  
The Impact

Background Human mobility among residential locations can drive dengue virus (DENV) transmission dynamics. Recently, it was shown that individuals with symptomatic DENV infection exhibit significant changes in their mobility patterns, spending more time at home during illness. This change in mobility is predicted to increase the risk of acquiring infection for those living with or visiting the ill individual. It has yet to be considered, however, whether social contacts are also changing their mobility, either by socially distancing themselves from the infectious individual or increasing contact to help care for them. Social, or physical, distancing and caregiving could have diverse yet important impacts on DENV transmission dynamics; therefore, it is necessary to better understand the nature and frequency of these behaviors including their effect on mobility. Methodology and principal findings Through community-based febrile illness surveillance and RT-PCR infection confirmation, 67 DENV positive (DENV+) residents were identified in the city of Iquitos, Peru. Using retrospective interviews, data were collected on visitors and home-based care received during the illness. While 15% of participants lost visitors during their illness, 22% gained visitors; overall, 32% of all individuals (particularly females) received visitors while symptomatic. Caregiving was common (90%), particularly caring by housemates (91%) and caring for children (98%). Twenty-eight percent of caregivers changed their behavior enough to have their work (and, likely, mobility patterns) affected. This was significantly more likely when caring for individuals with low “health-related quality of well-being” during illness (Fisher’s Exact, p = 0.01). Conclusions/Significance Our study demonstrates that social contacts of individuals with dengue modify their patterns of visitation and caregiving. The observed mobility changes could impact a susceptible individual’s exposure to virus or a presymptomatic/clinically inapparent individual’s contribution to onward transmission. Accounting for changes in social contact mobility is imperative in order to get a more accurate understanding of DENV transmission.

scholarly journals Predicting Dynamic Patterns of Short-Term Movement

2019 ◽  
Vol 34 (Supplement_1) ◽  
pp. S26-S34
Author(s):  
Sveta Milusheva
Keyword(s):  
Predictive Modeling ◽  
Human Mobility ◽  
Real Data ◽  
Mobile Phone Data ◽  
Population Movement ◽  
Short Term ◽  
Policy Makers ◽  
Important Health ◽  
The Impact

Abstract Short-term human mobility has important health consequences, but measuring short-term movement using survey data is difficult and costly, and use of mobile phone data to study short-term movement is only possible in locations that can access the data. Combining several accessible data sources, Senegal is used as a case study to predict short-term movement within the country. The focus is on two main drivers of movement—economic and social—which explain almost 70 percent of the variation in short-term movement. Comparing real and predicted short-term movement to measure the impact of population movement on the spread of malaria in Senegal, the predictions generated by the model provide estimates for the effect that are not significantly different from the estimates using the real data. Given that the data used in this paper are often accessible in other country settings, this paper demonstrates how predictive modeling can be used by policy makers to estimate short-term mobility.

Modeling geographical spread of COVID-19 in India using network-based approach (Preprint)

2020 ◽  
Author(s):  
Ankush Kumar
Keyword(s):  
Human Mobility ◽  
Uttar Pradesh ◽  
Decisive Role ◽  
Northeast India ◽  
North India ◽  
Mobility Patterns ◽  
Large Country ◽  
Geographical Spread ◽  
Internal Mobility ◽  
The Impact

BACKGROUND COVID-19 pandemic is a global concern, due to its high spreading and alarming fatality rate. Mathematical models can play a decisive role in mitigating the spread and predicting the growth of the epidemic. India is a large country, with a highly variable inter-state mobility, and dynamically varying infection cases in different locations; thus, the existing models, based solely on the aspects of growth rates, or generalized network concepts, may not provide desired predictions. The internal mobility of a country must be considered, for accurate prediction. OBJECTIVE This study aims to propose a framework for predicting the geographical spread of COVID-19 based on human mobility, by incorporating migration and transport statistics. The motivation of the research is to identify the locations, which can be at higher level COVID -19 spread risk, during migrants transfer and transportation activities. METHODS We use reported COVID-19 cases, census migration data, and monthly airline data of passengers. RESULTS We discover that spreading depends on the spatial distribution of existing cases, human mobility patterns, and administrative decisions. In India, the mobility towards professional sites can surge incoming cases at Maharastra and Karnataka, while migration towards the native places can risk Uttar Pradesh and Bihar. We anticipate that the state Kerala, with one of the highest cases of COVID-19, may not receive significant incoming cases, while Karnataka and Haryana may receive the challenge of high incoming cases, with medium cases so far. Using airline passenger's data, we also estimate the number of potential incoming cases at various airports. The study predicts that the airports located in the region of north India are vulnerable, whereas in the northeast India and in some south India are relatively safe. CONCLUSIONS A model is developed for systematically understanding the effect of migration and transport on the spreading of COVID-19, and predetermining the hotspots on real time basis. Through the model, we identified the airports and states that are at higher level of COVID-19 risk. The study can guide policymakers in prior planning of transport and estimate the required medical and quarantine facilities to minimize the impact of COVID-19.

scholarly journals Exploring the Roles of Local Mobility Patterns, Socioeconomic Conditions, and Lockdown Policies in Shaping the Patterns of COVID-19 Spread

2021 ◽  
Vol 13 (5) ◽  
pp. 112
Author(s):  
Mauricio Herrera ◽  
Alex Godoy-Faúndez
Keyword(s):  
Latin American ◽  
Low Income ◽  
Large Scale ◽  
Human Mobility ◽  
Epidemiological Data ◽  
High Income ◽  
Metropolitan Region ◽  
Mobility Patterns ◽  
Large Cities

The COVID-19 crisis has shown that we can only prevent the risk of mass contagion through timely, large-scale, coordinated, and decisive actions. This pandemic has also highlighted the critical importance of generating rigorous evidence for decision-making, and actionable insights from data, considering further the intricate web of causes and drivers behind observed patterns of contagion diffusion. Using mobility, socioeconomic, and epidemiological data recorded throughout the pandemic development in the Santiago Metropolitan Region, we seek to understand the observed patterns of contagion. We characterize human mobility patterns during the pandemic through different mobility indices and correlate such patterns with the observed contagion diffusion, providing data-driven models for insights, analysis, and inferences. Through these models, we examine some effects of the late application of mobility restrictions in high-income urban regions that were affected by high contagion rates at the beginning of the pandemic. Using augmented synthesis control methods, we study the consequences of the early lifting of mobility restrictions in low-income sectors connected by public transport to high-risk and high-income communes. The Santiago Metropolitan Region is one of the largest Latin American metropolises with features that are common to large cities. Therefore, it can be used as a relevant case study to unravel complex patterns of the spread of COVID-19.

Uncovering stable and occasional human mobility patterns: A case study of the Beijing subway

2018 ◽  
Vol 492 ◽  
pp. 28-38 ◽  
Author(s):  
Nuo Yong ◽  
Shunjiang Ni ◽  
Shifei Shen ◽  
Peng Chen ◽  
Xuewei Ji
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Beijing Subway
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