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.

In Search of the “Society Genes”: What Could Be a Link Between Individual Genetics and Social Structure

2021 ◽  
Vol 32 (6) ◽  
pp. 42
Author(s):  
Denis S. Andreyuk
Keyword(s):  
Social Relations ◽  
Risk Taking ◽  
Individual Characteristics ◽  
Evolutionary Development ◽  
Social Changes ◽  
Genetic Determinants ◽  
Genetic Changes ◽  
Individual Level ◽  
The Individual ◽  
Genetically Determined

Genome editing technologies make it important to look for genetic determinants that can influence the structure of society and basic social relations. This paper proposes to look for such determinants in the evolutionarily ancient mechanisms of group interaction, namely in the genes that determine the balance of cooperation and competition. The opposition of these two forces is thought to be the basis of the evolutionary development of intelligence in higher primates and humans. The article provides examples showing that individual characteristics such as extraversion/introversion as measured by the "Big Five" methodology, aggressiveness, which strongly associates with the risk taking, and the level of intelligence, all of these traits a) greatly influence the organization of social processes and b) are largely genetically determined. As a development of this approach of searching for socially significant genetic determinants, it is proposed to model genetic changes in sociality, aggressiveness and intelligence at the individual level, followed by an analysis of the resulting social changes.

scholarly journals Characteristics Contributing to Low- and Minimum-Wage Labour in Germany

2020 ◽  
Vol 240 (2-3) ◽  
pp. 161-200
Author(s):  
Matthias Dütsch ◽  
Ralf Himmelreicher
Keyword(s):  
Minimum Wage ◽  
Descriptive Analysis ◽  
Explanatory Power ◽  
Working Hours ◽  
Individual Characteristics ◽  
Systematic Analysis ◽  
Individual Level ◽  
Wage Labour ◽  
Random Intercept ◽  
The Individual

AbstractIn this article we examine the correlation between characteristics of individuals, companies, and industries involved in low-wage labour in Germany and the risks workers face of earning hourly wages that are below the minimum-wage or low-wage thresholds. To identify these characteristics, we use the Structure of Earnings Survey (SES) 2014. The SES is a mandatory survey of companies which provides information on wages and working hours from about 1 million jobs and nearly 70,000 companies from all industries. This data allows us to present the first systematic analysis of the interaction of individual-, company-, and industry-level factors on minimum- and low-wage working in Germany. Using a descriptive analysis, we first give an overview of typical low-paying jobs, companies, and industries. Second, we use random intercept-only models to estimate the explanatory power of the individual, company, and industry levels. One main finding is that the influence of individual characteristics on wage levels is often overstated: Less than 25 % of the differences in the employment situation regarding being employed in minimum-wage or low-wage jobs can be attributed to the individual level. Third, we performed logistic and linear regression estimations to assess the risks of having a minimum- or low-wage job and the distance between a worker’s actual earnings and the minimum- or low-wage thresholds. Our findings allow us to conclude that several determinants related to individuals appear to suggest a high low-wage incidence, but in fact lose their explanatory power once controls are added for factors relating to the companies or industries that employ these individuals.

Assessing the Potential Extent of Carsharing

2005 ◽  
Vol 1927 (1) ◽  
pp. 174-181 ◽  
Author(s):  
Thomas D. Schuster ◽  
John Byrne ◽  
James Corbett ◽  
Yda Schreuder
Keyword(s):  
Travel Behavior ◽  
Geographic Area ◽  
Social Consequences ◽  
Sensitivity Analyses ◽  
Policy Makers ◽  
Individual Level ◽  
Policy Tool ◽  
Interest Policy ◽  
Sound Policy ◽  
The Individual

Members of carsharing organizations reduce both the number of vehicles owned and vehicle miles traveled (VMT). Given these benefits at the individual level, carsharing may interest policy makers as another tool to address the negative environmental, economic, and social consequences of automobile dependence. However, the aggregate effects of carsharing must be estimated before sound policy decisions can be made. This paper describes a Monte Carlo simulation of the economic decision to own or share a vehicle on the basis of major cost components and past vehicle use. The simulation estimates the percentage of vehicles that would be cheaper to share than own. In Baltimore, Maryland, this result ranged from 4.2% under a traditional neighborhood carsharing model to 14.8% in a commuter-based carsharing model. Sensitivity analyses identified travel time and VMT as the most important economic factors, which likely incorporate other factors such as transit access and environmental attitudes. Because travel behavior, not ownership cost, drives the economic carsharing decision, the model hypothesizes that there will be increasing marginal societal benefits from policies that promote carsharing. The model can be applied to any geographic area and can be used to assess carsharing impacts of various policies that change the economics of owning or driving an auto. These results indicate that carsharing can become prevalent enough to be considered an important policy tool.

Analyzing Transit User Behavior with 51 Weeks of Smart Card Data

2019 ◽  
Vol 2673 (6) ◽  
pp. 33-45 ◽  
Author(s):  
Elodie Deschaintres ◽  
Catherine Morency ◽  
Martin Trépanier
Keyword(s):  
Smart Card ◽  
Public Transportation ◽  
Travel Behavior ◽  
Travel Demand ◽  
Public Transit ◽  
User Behavior ◽  
Individual Level ◽  
Smart Card Data ◽  
Demand Models ◽  
The Individual

A better understanding of mobility behaviors is relevant to many applications in public transportation, from more accurate travel demand models to improved supply adjustment, customized services and integrated pricing. In line with this context, this study mined 51 weeks of smart card (SC) data from Montréal, Canada to analyze interpersonal and intrapersonal variability in the weekly use of public transit. Passengers who used only one type of product (AP − annual pass, MP − monthly pass, or TB − ticket book) over 12 months were selected, amounting to some 200,000 cards. Data was first preprocessed and summarized into card-week vectors to generate a typology of weeks. The most popular weekly patterns were identified for each type of product and further studied at the individual level. Sequences of week clusters were constructed to represent the weekly travel behavior of each user over 51 weeks. They were then segmented by type of product according to an original distance, therefore highlighting the heterogeneity between passengers. Two indicators were also proposed to quantify intrapersonal regularity as the repetition of weekly clusters throughout the weeks. The results revealed MP owners have a more regular and diversified use of public transit. AP users are mainly commuters whereas TB users tend to be more occasional transit users. However, some atypical groups were found for each type of product, for instance users with 4-day work weeks and loyal TB users.

Gender and party cohesion in the Italian parliament: a spatial analysis

2017 ◽  
Vol 48 (2) ◽  
pp. 243-264 ◽  
Author(s):  
Licia C. Papavero ◽  
Francesco Zucchini
Keyword(s):  
Principal Component ◽  
Individual Characteristics ◽  
Multidimensional Space ◽  
Legislative Behavior ◽  
Individual Level ◽  
Men 2 ◽  
Lower Chamber ◽  
Party Cohesion ◽  
The Individual ◽  
The Impact

Studies on female legislative behavior suggest that women parliamentarians may challenge party cohesion by allying across party lines. In this paper we analyze a specific parliamentary activity – bill co-sponsorship – in the Italian lower Chamber, between 1979 and 2016, as a source of information about MPs’ original preferences to study how gender affects party cohesion. Do women form a separated group in the Italian parliament? On average, are they more or less distant from the center of their parties than men? Does gender affect systematically party cohesion? A principal component analysis of co-sponsorship data allows us to identify the ideal points of all MPs in a multidimensional space for each legislature. Based on these data we estimate the impact of gender on party cohesion at the individual level while controlling for the impact of several other variables of different kind (individual, partisan, and institutional). We find that: (1) on average, women show lower cohesion as a group inside different parties and higher party cohesion than men; (2) the influence of gender on party cohesion is not conditional upon individual characteristics, upon the size and organization of parliamentary parties, and upon the share of women in their parliamentary groups; (3) the different behavior of women MPs may depend on the different patterns of recruitment in the parties.

Individual difference factors related to anthropomorphic tendency

2016 ◽  
Vol 50 (5/6) ◽  
pp. 973-1002 ◽  
Author(s):  
Kate Letheren ◽  
Kerri-Ann L. Kuhn ◽  
Ian Lings ◽  
Nigel K. Ll. Pope
Keyword(s):  
Individual Difference ◽  
Online Survey ◽  
Individual Characteristics ◽  
Future Research ◽  
Content Type ◽  
Individual Level ◽  
Personal Connection ◽  
Experiential Thinking ◽  
Psychology Literature ◽  
The Individual

Purpose This paper aims to addresses an important gap in anthropomorphism research by examining the individual-level factors that correlate with anthropomorphic tendency. Design/methodology/approach The extant psychology, marketing and consumer psychology literature is reviewed, and eight hypotheses devised. Data from 509 online survey respondents are analysed to identify individual characteristics associated with anthropomorphic tendency. Findings The results reveal that anthropomorphic tendency varies by individual and is significantly related to personality, age, relationship status, personal connection to animals and experiential thinking. Research limitations/implications This paper extends on recent research into the individual nature of anthropomorphic tendency, once thought to be a universal trait. Given that this paper is the first of its kind, testing of further traits is merited. It is suggested that future research further examine personality, as well as other elements of individual difference, and test the role of anthropomorphic tendency in the development of processing abilities with age. Practical implications Findings show that anthropomorphic tendency may prove to be a key variable in the segmentation of markets and the design of marketing communications, and that younger, single, more creative, conscientious consumers are an appropriate target for anthropomorphic messages. The importance of personal connection to animals, as well as experiential thinking, is also highlighted. Originality/value Given the importance of anthropomorphic tendency for the processing of messages involving non-human endorsers, as well as the formation of relevant attitudes and behaviours, this paper fulfils an identified need to further understand the characteristics of those high on this tendency.

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 Consumers’ Journey between Liquid and Solid Consumption

2021 ◽  
Vol 13 (24) ◽  
pp. 13730
Author(s):  
Edoardo Beretta ◽  
Giulia Miniero ◽  
Francesco Ricotta
Keyword(s):  
Sharing Economy ◽  
Individual Characteristics ◽  
Current Debate ◽  
Individual Level ◽  
Behavioral Profile ◽  
Theoretical Approaches ◽  
Managerial Implications ◽  
Angle Of View ◽  
The Individual ◽  
Productive Systems

Sharing economy brought changes both at the macroeconomic and the individual level. New models of consumption, such as the liquid one, are becoming very frequent, shaping countries’ productive systems and consumers’ habits. This paper—combining both theoretical approaches—aims at measuring the individual characteristics that induce consumers to prefer liquid versus solid consumption. First, the article contextualizes the topic from a broader, macroeconomic perspective, and later on, it narrows its angle of view making it rather microeconomic and behavioral. In this specific regard, by means of a cluster analysis, four profiles of consumers are identified: (1) Rational and liquid; (2) Hybrid and question mark; (3) Solid in transition; (4) Hyper solid. Characteristics as well as theoretical and managerial implications are outlined for each cluster. This research focusing on emerging consumer behavior contributes to the current debate on solid and liquid consumption (i) exploring the continuum between these two extremes, (ii) defining a first behavioral profile of customer that are traveling between solid and liquid state and (iii) designing a possible way to target such a blurred and fast evolving customer that mostly qualifies a global and rapidly evolving economic environment.

scholarly journals Learning to Personalize Medicine from Aggregate Data

2020 ◽  
Author(s):  
Rich Colbaugh ◽  
Kristin Glass
Keyword(s):  
Clinical Trials ◽  
Personalized Medicine ◽  
Prediction Models ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Individual Characteristics ◽  
Training Data ◽  
Experimental Therapy ◽  
Individual Level ◽  
The Individual

AbstractThere is great interest in personalized medicine, in which treatment is tailored to the individual characteristics of patients. Achieving the objectives of precision healthcare will require clinically-grounded, evidence-based approaches, which in turn demands rigorous, scalable predictive analytics. Standard strategies for deriving prediction models for medicine involve acquiring ‘training’ data for large numbers of patients, labeling each patient according to the outcome of interest, and then using the labeled examples to learn to predict the outcome for new patients. Unfortunately, labeling individuals is time-consuming and expertise-intensive in medical applications and thus represents a major impediment to practical personalized medicine. We overcome this obstacle with a novel machine learning algorithm that enables individual-level prediction models to be induced from aggregate-level labeled data, which is readily-available in many health domains. The utility of the proposed learning methodology is demonstrated by: i.) leveraging US county-level mental health statistics to create a screening tool which detects individuals suffering from depression based upon their Twitter activity; ii.) designing a decision-support system that exploits aggregate clinical trials data on multiple sclerosis (MS) treatment to predict which therapy would work best for the presenting patient; iii.) employing group-level clinical trials data to induce a model able to find those MS patients likely to be helped by an experimental therapy.

scholarly journals PhenoBR: a model to phenotype body condition dynamics in meat sheep

2020 ◽  
Author(s):  
Tiphaine Macé ◽  
Eliel González-García ◽  
György Kövér ◽  
Dominique Hazard ◽  
Masoomeh Taghipoor
Keyword(s):  
Body Condition ◽  
Accretion Rate ◽  
Body Condition Score ◽  
Negative Energy ◽  
Individual Variability ◽  
Individual Characteristics ◽  
Individual Level ◽  
Condition Score ◽  
The Individual ◽  
Meat Sheep

AbstractIn situations of negative energy balance (NEB) due to feed scarcity or high physiological demands, body energy reserves (BR), mainly stored in adipose tissues, become the main sources of energy for ruminants. The capacity to mobilize and restore such BRs in response to different challenges is of major concern in the current context of breeding for resilience. Body condition score (BCS) is a common, practical indicator of BR variations throughout successive productive cycles, and quantitative tools for characterizing such dynamics at the individual level are still lacking. The main objective of this work was to characterize body condition dynamics in terms of BR mobilization and accretion capacities of meat sheep during their productive lifespan through a modelling approach.The animal model used in this work was the reproductive meat ewe (n = 1478) reared in extensive rangeland. Regular measurements of BCS for each productive cycle were used as the indicator of BR variations. A hybrid mathematical model and a web interface, called PhenoBR, was developed to characterize ewes’ BCS variations through four synthetic and biologically meaningful parameters for each productive cycle i: BR accretion rate , BR mobilization rate , plus the time of onset and the duration of the BR mobilization, and ΔTi, respectively.The model converged for all the ewes included in the analysis. Estimation of the parameters indicated the inter-individual variability for BR accretion and mobilization rates, and for the length of the mobilization period. Body reserve mobilization rates were closely correlated between productive cycles. Significant correlations between BR mobilization and accretion rates suggest that the two processes are biologically linked. Parameters kp and kb decreased as parity increased. BR mobilization rate and duration increased as litter size increased, while BR accretion rate decreased.Individual characterization of animals by these parameters makes it possible to rank them for their efficiency in the use of body reserves when facing NEB challenges. Such parameters could contribute to better management and decision-making by farmers and advisors, e.g. by adapting feeding systems to the individual characteristics of BR dynamics, or by geneticists as criteria to develop future animal breeding programs including BR dynamics for more robust and resilient animals.

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