scholarly journals Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

2016 ◽  
Vol 09 (11) ◽  
pp. 519-534
Author(s):  
Masanao Ochi ◽  
Yuko Nakashio ◽  
Matthew Ruttley ◽  
Junichiro Mori ◽  
Ichiro Sakata
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Mobility Data

scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Disparity and Dynamics of Social Distancing Behaviors in Japan: An Investigation of mobile phone mobility data (Preprint)

2021 ◽  
Author(s):  
Zeyu Lyu ◽  
Hiroki Takikawa
Keyword(s):  
Large Scale ◽  
Age Groups ◽  
Mitigation Strategies ◽  
Social Distancing ◽  
Demographic Groups ◽  
Fine Grained ◽  
Mobility Data ◽  
The Social ◽  
The Government ◽  
Over Time

BACKGROUND The availability of large-scale and fine-grained aggregated mobility data has allowed researchers to observe the dynamic of social distancing behaviors at high spatial and temporal resolutions. Despite the increasing attentions paid to this research agenda, limited studies have focused on the demographic factors related to mobility and the dynamics of social distancing behaviors has not been fully investigated. OBJECTIVE This study aims to assist in the design and implementation of public health policies by exploring the social distancing behaviors among various demographic groups over time. METHODS We combined several data sources, including mobile tracking data and geographical statistics, to estimate visiting population of entertainment venues across demographic groups, which can be considered as the proxy of social distancing behaviors. Then, we employed time series analyze methods to investigate how voluntary and policy-induced social distancing behaviors shift over time across demographic groups. RESULTS Our findings demonstrate distinct patterns of social distancing behaviors and their dynamics across age groups. The population in the entertainment venues comprised mainly of individuals aged 20–40 years, while according to the dynamics of the mobility index and the policy-induced behavior, among the age groups, the extent of reduction of the frequency of visiting entertainment venues during the pandemic was generally the highest among younger individuals. Also, our results indicate the importance of implementing the social distancing policy promptly to limit the spread of the COVID-19 infection. However, it should be noticed that although the policy intervention during the second wave in Japan appeared to increase the awareness of the severity of the pandemic and concerns regarding COVID-19, its direct impact has been largely decreased could only last for a short time. CONCLUSIONS At the time we wrote this paper, in Japan, the number of daily confirmed cases was continuously increasing. Thus, this study provides a timely reference for decision makers about the current situation of policy-induced compliance behaviors. On the one hand, age-dependent disparity requires target mitigation strategies to increase the intention of elderly individuals to adopt mobility restriction behaviors. On the other hand, considering the decreasing impact of self-restriction recommendations, the government should employ policy interventions that limit the resurgence of cases, especially by imposing stronger, stricter social distancing interventions, as they are necessary to promote social distancing behaviors and mitigate the transmission of COVID-19. CLINICALTRIAL None

Application of smart card data in validating a large-scale multi-modal transit assignment model

2017 ◽  
Vol 10 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ahmad Tavassoli ◽  
Mahmoud Mesbah ◽  
Mark Hickman
Keyword(s):  
Smart Card ◽  
Large Scale ◽  
Transit Assignment ◽  
Assignment Model ◽  
Smart Card Data

scholarly journals Modelling of Bi-Directional Spatio-Temporal Dependence and Users’ Dynamic Preferences for Missing POI Check-In Identification

2019 ◽  
Vol 33 ◽  
pp. 5458-5465 ◽  
Author(s):  
Dongbo Xi ◽  
Fuzhen Zhuang ◽  
Yanchi Liu ◽  
Jingjing Gu ◽  
Hui Xiong ◽  
...  
Keyword(s):  
Large Scale ◽  
Real Life ◽  
Location Prediction ◽  
Temporal Dependence ◽  
Great Opportunity ◽  
Mobility Data ◽  
Proposed Model ◽  
Poi Recommendation ◽  
Spatio Temporal ◽  
Dynamic Preferences

Human mobility data accumulated from Point-of-Interest (POI) check-ins provides great opportunity for user behavior understanding. However, data quality issues (e.g., geolocation information missing, unreal check-ins, data sparsity) in real-life mobility data limit the effectiveness of existing POIoriented studies, e.g., POI recommendation and location prediction, when applied to real applications. To this end, in this paper, we develop a model, named Bi-STDDP, which can integrate bi-directional spatio-temporal dependence and users’ dynamic preferences, to identify the missing POI check-in where a user has visited at a specific time. Specifically, we first utilize bi-directional global spatial and local temporal information of POIs to capture the complex dependence relationships. Then, target temporal pattern in combination with user and POI information are fed into a multi-layer network to capture users’ dynamic preferences. Moreover, the dynamic preferences are transformed into the same space as the dependence relationships to form the final model. Finally, the proposed model is evaluated on three large-scale real-world datasets and the results demonstrate significant improvements of our model compared with state-of-the-art methods. Also, it is worth noting that the proposed model can be naturally extended to address POI recommendation and location prediction tasks with competitive performances.

Crowding cost estimation with large scale smart card and vehicle location data

2017 ◽  
Vol 95 ◽  
pp. 105-125 ◽  
Author(s):  
Daniel Hörcher ◽  
Daniel J. Graham ◽  
Richard J. Anderson
Keyword(s):  
Smart Card ◽  
Cost Estimation ◽  
Large Scale ◽  
Location Data ◽  
Vehicle Location

scholarly journals Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Raj Bridgelall ◽  
Pan Lu ◽  
Denver D. Tolliver ◽  
Tai Xu
Keyword(s):  
Large Scale ◽  
Statistical Tests ◽  
Poor Quality ◽  
Connected Vehicle ◽  
Cleaning Method ◽  
Mobility Data ◽  
Automated Method ◽  
Vehicle Data ◽  
Chi Squared ◽  
Trip Production

On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

scholarly journals Non-compulsory measures sufficiently reduced human mobility in Tokyo during the COVID-19 epidemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takahiro Yabe ◽  
Kota Tsubouchi ◽  
Naoya Fujiwara ◽  
Takayuki Wada ◽  
Yoshihide Sekimoto ◽  
...  
Keyword(s):  
Large Scale ◽  
Human Mobility ◽  
Social Contact ◽  
Mobility Patterns ◽  
State Of Emergency ◽  
Social Contacts ◽  
Policy Makers ◽  
Mobility Data ◽  
Contact Rates ◽  
Mobility Behavior

Abstract While large scale mobility data has become a popular tool to monitor the mobility patterns during the COVID-19 pandemic, the impacts of non-compulsory measures in Tokyo, Japan on human mobility patterns has been under-studied. Here, we analyze the temporal changes in human mobility behavior, social contact rates, and their correlations with the transmissibility of COVID-19, using mobility data collected from more than 200K anonymized mobile phone users in Tokyo. The analysis concludes that by April 15th (1 week into state of emergency), human mobility behavior decreased by around 50%, resulting in a 70% reduction of social contacts in Tokyo, showing the strong relationships with non-compulsory measures. Furthermore, the reduction in data-driven human mobility metrics showed correlation with the decrease in estimated effective reproduction number of COVID-19 in Tokyo. Such empirical insights could inform policy makers on deciding sufficient levels of mobility reduction to contain the disease.

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