Border enforcement and the sorting and commuting patterns of hispanics

2022 ◽  
Author(s):  
Heepyung Cho
Keyword(s):  
Border Enforcement ◽  
Commuting Patterns

scholarly journals Evaluating the Effect of the Financial Status to the Mobility Customs

2021 ◽  
Vol 10 (5) ◽  
pp. 328
Author(s):  
Gergo Pintér ◽  
Imre Felde
Keyword(s):  
Socioeconomic Status ◽  
Economic Status ◽  
Housing Prices ◽  
Principal Component ◽  
Cellular Phone ◽  
Radius Of Gyration ◽  
Financial Status ◽  
Property Price ◽  
Statistical Relationships ◽  
Commuting Patterns

In this article, we explore the relationship between cellular phone data and housing prices in Budapest, Hungary. We determine mobility indicators from one months of Call Detail Records (CDR) data, while the property price data are used to characterize the socioeconomic status at the Capital of Hungary. First, we validated the proposed methodology by comparing the Home and Work locations estimation and the commuting patterns derived from the cellular network dataset with reports of the national mini census. We investigated the statistical relationships between mobile phone indicators, such as Radius of Gyration, the distance between Home and Work locations or the Entropy of visited cells, and measures of economic status based on housing prices. Our findings show that the mobility correlates significantly with the socioeconomic status. We performed Principal Component Analysis (PCA) on combined vectors of mobility indicators in order to characterize the dependence of mobility habits on socioeconomic status. The results of the PCA investigation showed remarkable correlation of housing prices and mobility customs.

scholarly journals Data-Driven Analysis on Inter-City Commuting Decisions in Germany

2021 ◽  
Vol 13 (11) ◽  
pp. 6320
Author(s):  
Hui Chen ◽  
Sven Voigt ◽  
Xiaoming Fu
Keyword(s):  
Good Accuracy ◽  
Housing Price ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Steady Increase ◽  
Time Duration ◽  
Major Bottleneck ◽  
Potential Factors ◽  
Commuting Patterns ◽  
Industry Sectors

Understanding commuters’ behavior and influencing factors becomes more and more important every day. With the steady increase of the number of commuters, commuter traffic becomes a major bottleneck for many cities. Commuter behavior consequently plays an increasingly important role in city and transport planning and policy making. Although prior studies investigated a variety of potential factors influencing commuting decisions, most of them are constrained by the data scale in terms of limited time duration, space and number of commuters under investigation, largely owing to their dependence on questionnaires or survey panel data; as such only small sets of features can be explored and no predictions of commuter numbers have been made, to the best of our knowledge. To fill this gap, we collected inter-city commuting data in Germany between 1994 and 2018, and, along with other data sources, analyzed the influence of GDP, housing and the labor market on the decision to commute. Our analysis suggests that the access to employment opportunities, housing price, income and the distribution of the location’s industry sectors are important factors in commuting decisions. In addition, different age, gender and income groups have different commuting patterns. We employed several machine learning algorithms to predict the commuter number using the identified related features with reasonably good accuracy.

scholarly journals Investigating Sustainable Commuting Patterns by Socio-Economic Factors

2021 ◽  
Vol 13 (4) ◽  
pp. 2180 ◽  
Author(s):  
Woo Jang ◽  
Fei Yuan ◽  
Jose Javier Lopez
Keyword(s):  
Low Income ◽  
Older Workers ◽  
Central City ◽  
Minority Group ◽  
Primary Data ◽  
Economic Factors ◽  
Group Status ◽  
Multiple Variables ◽  
Commuting Patterns ◽  
Socio Economic Factors

This research aims to analyze how modes of transportation differ according to socio-economic factors in an urban space. The study area is Ramsey County, the most densely populated county in Minnesota. The primary data used were from the recent 2012–2016 Census Transportation Planning Products (CTPP). We performed regression models to identify the relationship between mode of transport and socio-economic variables, and further analyzed disaggregate trip data to provide a more realistic evaluation of commuting patterns by use of multiple variables in combination. The research found that sustainable commuting patterns correlated significantly with both poverty and minority group status, but bore no significant relationship to older workers. Additionally, there was a significant correlation between commuting alone by car with both minority group status and older workers, but not with poverty. This research also confirmed that the sustainable commuting patterns of the working poor were mostly located in the downtown area, while causes of low-income workers driving alone typically involved much longer commutes to and from points throughout the study area, suggesting that more efficient commutes are a significant quality of life factor for the urban poor when evaluating residential and employment opportunities in the central city.

scholarly journals Teleworking and Online Shopping: Socio-Economic Factors Affecting Their Impact on Transport Demand

2021 ◽  
Vol 13 (13) ◽  
pp. 7211
Author(s):  
Juan Ramón López Soler ◽  
Panayotis Christidis ◽  
José Manuel Vassallo
Keyword(s):  
Urban Areas ◽  
Online Shopping ◽  
New Technologies ◽  
Age Groups ◽  
Mobility Patterns ◽  
Factors Affecting ◽  
Ex Post ◽  
Potential Impact ◽  
Commuting Patterns ◽  
Socio Economic Factors

Teleworking and online shopping became commonplace during the COVID-19 pandemic and can be expected to maintain a strong presence in the foreseeable future. They can lead to significant changes in mobility patterns and transport demand. It is still unclear, however, how extensive their adoption can be, since each individual has different preferences or constraints. The overall impact on transport depends on which segments of the population will modify their behaviour and on what the substitutes to the current patterns will be. The purpose of this work is to identify the user profiles and spatial aspects that affect the adoption of teleworking and online shopping, and to explore the potential impact on transport demand. To that end, data from an EU-wide survey on mobility were analysed using a Machine Learning methodology. The results suggest that while the take up of the new work and consumption patterns is high on average, there are significant differences among countries and across different socio-economic profiles. Teleworking appears to have a high potential mainly in certain services sectors, affecting commuting patterns predominantly in large urban areas. Online shopping activity is more uniform across the population, although differences among countries and age groups may still be relevant. The findings of this work can be useful for the analysis of policies to encourage the uptake of new technologies in transport and mobility. They can be also a good reference point for future studies on the ex-post analysis of the impacts of the pandemic on mobility.

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