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

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.

scholarly journals Characterizing Urban Mobility Patterns: A Case Study of Mexico City

2021 ◽  
pp. 153-170
Author(s):  
Pierre Melikov ◽  
Jeremy A. Kho ◽  
Vincent Fighiera ◽  
Fahad Alhasoun ◽  
Jorge Audiffred ◽  
...  
Keyword(s):  
Mexico City ◽  
Human Mobility ◽  
Data Sets ◽  
Mobility Patterns ◽  
Distribution Models ◽  
The World ◽  
The Right ◽  
Different Parts

AbstractSeamless access to destinations of value such as workplaces, schools, parks or hospitals, influences the quality of life of people all over the world. The first step to planning and improving proximity to services is to estimate the number of trips being made from different parts of a city. A challenge has been representative data available for that purpose. Relying on expensive and infrequently collected travel surveys for modeling trip distributions to facilities has slowed down the decision-making process. The growing abundance of data already collected, if analyzed with the right methods, can help us with planning and understanding cities. In this chapter, we examine human mobility patterns extracted from data passively collected. We present results on the use of points of interest (POIs) registered on Google Places to approximate trip attraction in a city. We compare the result of trip distribution models that utilize only POIs with those utilizing conventional data sets, based on surveys. We show that an extended radiation model provides very good estimates when compared with the official origin–destination matrices from the latest census in Mexico City.

scholarly journals Exploring Urban Dynamics from Bluetooth Tracking Data: A Case Study of Austin, Texas

2019 ◽  
Vol 2 ◽  
pp. 1-6
Author(s):  
Yihong Yuan ◽  
David Mills
Keyword(s):  
Real Time ◽  
Social Life ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Land Use Patterns ◽  
Urban Dynamics ◽  
Individual Level ◽  
Human Aspects ◽  
The Individual

<p><strong>Abstract.</strong> In recent decades, the growing availability of location-aware devices, such as Global Positioning System (GPS) receivers and smart phones, has provided new challenges and opportunities for policy makers to analyze, model, and predict human mobility patterns. However, previous studies on Bluetooth technologies have mainly focused on applying Bluetooth data to analyzing traffic and optimizing transportation networks or deploying new Bluetooth devices in civil engineering. The use of such datasets in understanding urban dynamics and real-time land use patterns is rather limited. This study develops an extendable workflow to explore urban dynamics from Bluetooth data based on a case study in Austin, Texas. We identified similar mobility patterns in different areas of Austin during various study periods, including the Memorial Day long weekend in 2016 and a national musical festival (South by Southwest). Our main goal is to prove the efficacy of this specific workflow and methodology to understand urban dynamics based on real-time Bluetooth data. The hypothesis is that Bluetooth data is sensitive to the daily patterns of human interactions and movements on the individual level, therefore it can capture detailed dynamic patterns. The proposed research also validates new concepts such as “human sensing” and “social sensing” in the field of geography and spatial sciences, which introduces new opportunities to monitor the human aspects of social life.</p>

scholarly journals Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand

2021 ◽  
Vol 13 (4) ◽  
pp. 2178
Author(s):  
Songkorn Siangsuebchart ◽  
Sarawut Ninsawat ◽  
Apichon Witayangkurn ◽  
Surachet Pravinvongvuth
Keyword(s):  
Performance Assessment ◽  
Public Transport ◽  
Human Mobility ◽  
Open Data ◽  
Capital City ◽  
Metropolitan Region ◽  
Traffic Information ◽  
Mobility Patterns ◽  
Transport Planning ◽  
Java Program

Bangkok, the capital city of Thailand, is one of the most developed and expansive cities. Due to the ongoing development and expansion of Bangkok, urbanization has continued to expand into adjacent provinces, creating the Bangkok Metropolitan Region (BMR). Continuous monitoring of human mobility in BMR aids in public transport planning and design, and efficient performance assessment. The purpose of this study is to design and develop a process to derive human mobility patterns from the real movement of people who use both fixed-route and non-fixed-route public transport modes, including taxis, vans, and electric rail. Taxi GPS open data were collected by the Intelligent Traffic Information Center Foundation (iTIC) from all GPS-equipped taxis of one operator in BMR. GPS probe data of all operating GPS-equipped vans were collected by the Ministry of Transport’s Department of Land Transport for daily speed and driving behavior monitoring. Finally, the ridership data of all electric rail lines were collected from smartcards by the Automated Fare Collection (AFC). None of the previous works on human mobility extraction from multi-sourced big data have used van data; therefore, it is a challenge to use this data with other sources in the study of human mobility. Each public transport mode has traveling characteristics unique to its passengers and, therefore, specific analytical tools. Firstly, the taxi trip extraction process was developed using Hadoop Hive to process a large quantity of data spanning a one-month period to derive the origin and destination (OD) of each trip. Secondly, for van data, a Java program was used to construct the ODs of van trips. Thirdly, another Java program was used to create the ODs of the electric rail lines. All OD locations of these three modes were aggregated into transportation analysis zones (TAZ). The major taxi trip destinations were found to be international airports and provincial bus terminals. The significant trip destinations of vans were provincial bus terminals in Bangkok, electric rail stations, and the industrial estates in other provinces of BMR. In contrast, electric rail destinations were electric rail line interchange stations, the central business district (CBD), and commercial office areas. Therefore, these significant destinations of taxis and vans should be considered in electric rail planning to reduce the air pollution from gasoline vehicles (taxis and vans). Using the designed procedures, the up-to-date dataset of public transport can be processed to derive a time series of human mobility as an input into continuous and sustainable public transport planning and performance assessment. Based on the results of the study, the procedures can benefit other cities in Thailand and other countries.

scholarly journals Vehicle Energy Consumption in Python (VencoPy): Presenting and Demonstrating an Open-Source Tool to Calculate Electric Vehicle Charging Flexibility

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4349
Author(s):  
Niklas Wulff ◽  
Fabia Miorelli ◽  
Hans Christian Gils ◽  
Patrick Jochem
Keyword(s):  
Open Source ◽  
Electric Vehicle ◽  
Full Range ◽  
Mobility Patterns ◽  
Electric Vehicle Charging ◽  
Survey Results ◽  
Vehicle Demand ◽  
Vehicle Fleet ◽  
Electric Loads

As electric vehicle fleets grow, rising electric loads necessitate energy systems models to incorporate their respective demand and potential flexibility. Recently, a small number of tools for electric vehicle demand and flexibility modeling have been released under open source licenses. These usually sample discrete trips based on aggregate mobility statistics. However, the full range of variables of travel surveys cannot be accessed in this way and sub-national mobility patterns cannot be modeled. Therefore, a tool is proposed to estimate future electric vehicle fleet charging flexibility while being able to directly access detailed survey results. The framework is applied in a case study involving two recent German national travel surveys (from the years 2008 and 2017) to exemplify the implications of different mobility patterns of motorized individual vehicles on load shifting potential of electric vehicle fleets. The results show that different mobility patterns, have a significant impact on the resulting load flexibilites. Most obviously, an increased daily mileage results in higher electricty demand. A reduced number of trips per day, on the other hand, leads to correspondingly higher grid connectivity of the vehicle fleet. VencoPy is an open source, well-documented and maintained tool, capable of assessing electric vehicle fleet scenarios based on national travel surveys. To scrutinize the tool, a validation of the simulated charging by empirically observed electric vehicle fleet charging is advised.

scholarly journals Understanding senior's daily mobility patterns in California using human mobility motifs

2021 ◽  
Vol 94 ◽  
pp. 103117
Author(s):  
Rongxiang Su ◽  
Jingyi Xiao ◽  
Elizabeth C. McBride ◽  
Konstadinos G. Goulias
Keyword(s):  
Human Mobility ◽  
Mobility Patterns ◽  
Daily Mobility
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