Intrapersonal variability and model goodness-of-fit

Pas, Eric I.

doi:10.1016/0191-2607(87)90032-x

Cited by 79 publications

(64 citation statements)

References 3 publications

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“…The difference is composed of two parts: one is the within-group sum of squares (SSWG), representing the individual travel frequency difference, and the other is the between-group sum of squares (SSBG), representing the travel frequency difference between the elderly group and the middle-aged/young group. According to the definition of Pas [28] …”

Section: Difference In Travel Frequencymentioning

confidence: 99%

Elderly’s Travel Patterns and Trends: The Empirical Analysis of Beijing

Liu

Sun

et al. 2017

Sustainability

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Abstract:Travel by the elderly is generally characterized by relatively short distances and low frequencies. However, with social development, the travel patterns of the elderly are lacking in sustainability. In some European countries, the elderly are increasingly dependent on cars while traveling. In addition, the influencing factors of the elderly's travel behavior are also changing. At present, most foreign and domestic studies focus on the impact of individual and household socioeconomic attributes, travel attributes, and policy factors on the travel frequency, travel mode choice, and travel demand of the elderly, but they lack an analysis of the regional differences of the elderly's travel behavior characteristics and related countermeasures. The studies excessively focus on the influencing factors but overlook the difference between the elderly's travel characteristics and young people's travel characteristics, as well as the interactions at the household level. Based on data from the Fifth Travel Survey of Residents in Beijing 2014, this paper uses variance analysis, Spearman's correlation analysis, and descriptive and comparative analysis to study the difference in travel frequency over 24 hours between the elderly and middle-aged/young people in Beijing, the impact of household, individual, and travel attributes on the travel frequency difference, and the regional difference in the elderly's travel behavior characteristics. The results show that there is a significant difference in travel frequency between the elderly group and the middle-aged/young group in Beijing; the main reason is the individual difference between travelers. Travelers' attributes all exert an influence on the travel frequency of both groups, but the degree and direction of the influence are different. At the household level, middle-aged/young people with a higher household income travel less frequently, whereas the case is completely the opposite for the elderly. In terms of personal attributes, gender has a significant negative effect on the elderly's travel frequency; that is, women travel less than men, whereas there is no difference between men and women in middle-aged/young people. Regarding travel attributes, travel distance and travel duration have a significant negative effect on the two groups' travel frequencies. The elderly in some European countries are more dependent on cars, whereas a large proportion of elderly people in Beijing walk on foot, but the degree of dependence on cars of the "new generation" of the elderly in Beijing will increase rapidly, which will bring the lack of sustainability of travel patterns, further bringing new challenges to policymakers and transport planning departments.

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Section: Difference In Travel Frequencymentioning

confidence: 99%

Elderly’s Travel Patterns and Trends: The Empirical Analysis of Beijing

Liu

Sun

et al. 2017

Sustainability

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“…Focusing on components of total variation, Pas (10) highlights that a model based on cross-sectional data explains only inter-individual variation, and model performance could be apparently good because the total variation does not include intra-individual variation. His analysis shows that intra-individual variation comprises a substantial proportion (around 50%) of the total variation in terms of trip generation per day.…”

Section: Studies Of Intra-individual Variationmentioning

confidence: 99%

“…One of the remarkable characteristics is its day-to-day variability (1)(2)(3)(4)(5)(6)(7)(8)(9)(10). In this study, it is noteworthy to first clarify four technical terms: heterogeneity, variability, variation and variance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring Variation Properties of Departure Time Choice Behavior by Using Multilevel Analysis Approach

Chikaraishi

Fujiwara

Zhang

et al. 2009

Transportation Research Record: Journal of the Transportation R

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This paper examines the variation properties of departure time choice behavior by activity type, using a continuous six-week travel survey collected in the cities of Karlsruhe and Halle in Germany in 1999. Total variation of departure time choice is decomposed into five variation components: spatial variation, temporal variation at aggregate level, inter-household variation, inter-individual variation, and intra-individual variation. These variations are first quantitatively analyzed using multilevel modeling approach without considering the influences of explanatory variables. Then, based on the clarified variations patterns, the paper further examines how much of the variations can be captured by explanatory variables. It is confirmed that different activities show quite different variations and among the variations, the intra-individual variation accounts for the largest percentage of the total variation. Model estimation results further underscore the needs of simultaneously dealing with unobserved macro-level variations, especially inter-individual, inter-household and spatial variations, as well as the intra-individual variation, even when the explanatory variables are included in the model. Such quantitative assessment of various types of variations based on the multi-level modeling approach could provide a useful guide for the specification of behavior models, and deepen the knowledge of behavioral variability by going beyond the traditional paradigm which only focuses on a limited set of variations.

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“…(day-to-day variability) 1 Pas 2) Pendyala 3) Susilo and Kitamura 4) Kitamura et al 5) Pas 2) 50% Pendyala 3) Susilo and Kitamura 4) Kitamura et al 5) Pas and Sundar 6) 1/3 …”

mentioning

confidence: 99%