scholarly journals Identifying Urban Functional Zones Using Public Bicycle Rental Records and Point-of-Interest Data

2018 ◽  
Vol 7 (12) ◽  
pp. 459 ◽  
Author(s):  
Xiaoyi Zhang ◽  
Wenwen Li ◽  
Feng Zhang ◽  
Renyi Liu ◽  
Zhenhong Du
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Visual Analytics ◽  
Human Mobility ◽  
Residential Areas ◽  
Trip Purpose ◽  
Mobility Data ◽  
Point Of Interest ◽  
Functional Zones ◽  
Urban Functions

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.

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 Linking activity participation, socioeconomic characteristics, land use and travel patterns: a comparison of industry and commerce sector workers

2013 ◽  
Vol 7 (3) ◽  
pp. 59-86 ◽  
Author(s):  
Cira Souza Pitombo ◽  
Eiji Kawamoto ◽  
Antonio Jorge Gonçalves de Sousa
Keyword(s):  
Land Use ◽  
Travel Behavior ◽  
Household Income ◽  
Statistical Significance ◽  
Activity Participation ◽  
Car Ownership ◽  
Economic Activities ◽  
Socioeconomic Characteristics ◽  
Trip Purpose ◽  
Industry And Commerce

The objective of this work is to analyze the travel behavior of industry and commerce sector workers in terms of three variables groups: activity participation, socioeconomic characteristics and land use. This work is based on the Origin-Destination survey carried out in the São Paulo Metropolitan Area (SPMA) in 1997. Relationships were found between the concerned variables (Decision Tree), and the statistical significance of independent variables was assessed (Multiple Linear Regression). We analyzed the influence of the three variables groups on travel pattern choices: (A) socioeconomic variables (Household Income, Transit Pass Ownership and Car-ownership) affect the travel mode sequence; (B) activity participation (Study, Work) has an effect on the trip purpose sequence; and (C) land use variables (accumulated proportion of jobs by distance buffers starting from the home traffic zone centroid) influence the sequence of destinations chosen, especially in the case of industry sector workers. The different spatial distributions of economic activities (commercial and industrial) in the urban environment influence the travel of workers. This paper contributes essentially proposing the land use variable, through the intervening opportunities model as well as the presentation of a methodology, formed by application of exploratory and confirmatory techniques of multivariate data analysis.

scholarly journals Exploring the utility of Google(R) Mobility data during COVID-19 pandemic: A digital epidemiological analysis from India (Preprint)

2021 ◽  
Author(s):  
Kamal Kishore ◽  
Vidushi Jaswal ◽  
Madhur Verma ◽  
Vipin Kaushal
Keyword(s):  
Disease Severity ◽  
Disease Transmission ◽  
Human Mobility ◽  
National Level ◽  
Residential Areas ◽  
Mobility Data ◽  
Application Programming ◽  
Programming Software ◽  
Relative Change

BACKGROUND Association between human mobility and disease transmission for COVID-19 is established, but quantifying the levels of mobility over large geographical areas is difficult. Google released Community Mobility Report (CMR) data collated from mobile devices and gives an idea about the movement of people. OBJECTIVE Therefore, we attempt to explore the use of CMR to assess the role of mobility in spreading COVID-19 infection in India. METHODS An Ecological study analyzed CMR for human mobility. The data were compared for before, during, and after lockdown phases with the reference periods. Another dataset depicting the burden of COVID-19 after deriving various disease severity indicators was derived from a crowd-sourced Application Programming software. The relationship between the two datasets was investigated using Kendall’s tau correlation to depict the correlation between mobility and disease severity. RESULTS At the national level, mobility decreased everywhere except residential areas during the lockdown period, compared to the reference period. Mizoram (minimum cases) depicted a higher relative change in mobility than Maharashtra (maximum cases). Residential mobility negatively correlated with all other measures of mobility. The magnitude of correlations for intra-mobility indicators was comparatively low for the lockdown phase compared to other phases. A high correlation coefficient between epidemiological and mobility indicators is observed for the lockdown and unlock phases compared to the pre-lockdown. CONCLUSIONS We can use mobile-based open-source mobility data to provide the temporal anatomy of social distancing. CMR data depicted an association between mobility and disease severity, and we suggest that this technique supplement future COVID-19 surveillance. CLINICALTRIAL NA

scholarly journals A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data

2020 ◽  
Vol 9 (2) ◽  
pp. 124 ◽  
Author(s):  
Xucai Zhang ◽  
Yeran Sun ◽  
Anyao Zheng ◽  
Yu Wang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Planning ◽  
Human Mobility ◽  
Activity Patterns ◽  
Land Use Classification ◽  
Social Sensing ◽  
Point Of Interest ◽  
Land Use Types ◽  
Activity Behavior

The information of land use plays an important role in urban planning and optimizing the allocation of resources. However, traditional land use classification is imprecise. For instance, the type of commercial land is highly filled with the categories of shopping, eating, etc. The number of mixed-use lands is increasingly growing nowadays, and these lands sometimes are too mixed to be well investigated by conventional approaches such as remote sensing technology. To address this issue, we used a new social sensing approach to classify land use according to human mobility and activity patterns. Previous studies used other social sensing approaches to predict land use types at the parcel or the area level, whilst fine-grained point-of-interest (POI)-level land use data are likely to more useful in urban planning. To abridge this research gap, we proposed a new social sensing approach dedicated to classifying land use at a finer scale (i.e., POI-level or building level) according to human mobility and activity patterns reflected by location-based social network (LBSN) data. Specifically, we firstly investigated spatial and temporal patterns of human mobility and activity behavior using check-in data from a popular Chinese LBSN named Sina Weibo and subsequently applied those patterns to predicting the category of POI to refine urban land use classification in Guangzhou, China. In this study, we applied three classification methods (i.e., naive Bayes, support vector machines, and random forest) to recognize category of a certain POI by spatial and temporal features of human mobility and activity behavior as well as POIs’ locational characteristics. Random forest outperformed the other two methods and obtained an overall accuracy of 72.21%. Apart from that, we compared the results of the different rules in filtering check-in samples. The comparison results show that a reasonable rule to select samples is essential for predicting the category of POI. Moreover, the approach proposed in this study can be potentially applied to identifying functions of buildings according to visitors’ mobility and activity behavior and buildings’ locational characteristics.

scholarly journals Representing Urban Functions through Zone Embedding with Human Mobility Patterns

2018 ◽  
Author(s):  
Zijun Yao ◽  
Yanjie Fu ◽  
Bin Liu ◽  
Wangsu Hu ◽  
Hui Xiong
Keyword(s):  
New York ◽  
Large Scale ◽  
Arrival Time ◽  
Human Mobility ◽  
Location Based Service ◽  
Mobility Patterns ◽  
Mobility Data ◽  
Traffic Capacity ◽  
Spatio Temporal ◽  
Urban Functions

Urban functions refer to the purposes of land use in cities where each zone plays a distinct role and cooperates with each other to serve people’s various life needs. Understanding zone functions helps to solve a variety of urban related problems, such as increasing traffic capacity and enhancing location-based service. Therefore, it is beneficial to investigate how to learn the representations of city zones in terms of urban functions, for better supporting urban analytic applications. To this end, in this paper, we propose a framework to learn the vector representation (embedding) of city zones by exploiting large-scale taxi trajectories. Specifically, we extract human mobility patterns from taxi trajectories, and use the co-occurrence of origin-destination zones to learn zone embeddings. To utilize the spatio-temporal characteristics of human mobility patterns, we incorporate mobility direction, departure/arrival time, destination attraction, and travel distance into the modeling of zone embeddings. We conduct extensive experiments with real-world urban datasets of New York City. Experimental results demonstrate the effectiveness of the proposed embedding model to represent urban functions of zones with human mobility data.

Examining the Discrepancies between Self-Reported and Actual Commuting Behavior at the Individual Level

2021 ◽  
pp. 036119812110372
Author(s):  
Tianyu Su ◽  
M. Elena Renda ◽  
Jinhua Zhao
Keyword(s):  
Travel Behavior ◽  
Human Mobility ◽  
Individual Characteristics ◽  
Mobile Systems ◽  
Massachusetts Institute Of Technology ◽  
Individual Level ◽  
Mobility Data ◽  
Passive Data ◽  
Institute Of Technology ◽  
The Individual

For decades, transportation researchers have used survey data to understand the factors that affect travel-related choices. Nowadays, travel surveys lay the foundation of travel behavior analysis for transportation modeling, planning, and policy-making. The development of information technology for urban sensing has enabled substantial improvements to be made in survey-elicited and passive mobility data collection. Actively collected and passive data are very different, and being able to compare and integrate them could allow stakeholders to achieve a greater understanding of human mobility. The comparison between survey self-reported travel behavior and actual travel behavior revealed by urban and mobile systems provides us with the opportunity to find potential discrepancies. Previous work has examined these discrepancies mostly at the population level. An individual-level investigation of these discrepancies could provide many benefits, from increasing our understanding of survey and passive data accuracy and collection, to designing personalized transportation services. In this study, the discrepancies between self-reported and observed travel behavior are analyzed at both the individual and aggregated level by utilizing the available mobility data, namely, survey-based commuting diaries and passive mobility records. We propose a group of discrepancy metrics for commuting activities for which we have available and comparable data, and apply the framework to an empirical analysis at the Massachusetts Institute of Technology in Cambridge, U.S.A. Our results show that survey-elicited commuting diaries are quite reliable when examining overall commuting trends, whereas passive mobility data are more suitable for investigating individual-level commuting behavior. Furthermore, we identify the association between discrepancies in commuting behavior and certain individual characteristics, for example, employee type and age.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

scholarly journals Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaobin Wang ◽  
Yun Tong ◽  
Yupeng Fan ◽  
Haimeng Liu ◽  
Jun Wu ◽  
...  
Keyword(s):  
Impact Analysis ◽  
Human Mobility ◽  
Economic Activities ◽  
Underdeveloped Countries ◽  
Mobility Data ◽  
Long Run ◽  
Level Of Economic Development ◽  
Human World ◽  
Dynamic Relationships ◽  
The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

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