The Effect of Selected Sociodemographic Characteristics on Daily Travel-Activity Behavior

Pas, Eric I.

doi:10.1068/a160571

Cited by 145 publications

(96 citation statements)

References 10 publications

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“…However, it is the total effects, given by equation system (3) and (4) Table 8. The total effects (Table 8) reveal some intriguing interrelationships in time use and trip chaining that expand previous results (Pant and Bullen, 1980, Supernak, et al, 1983, Kitamura, 1984, Pas, 1984, Goulias and Kitamura, 1990, Pendyala and Kitamura, 1991, Levinson and Kumar, 1995. But this type of joint model also reveals some new results.…”

Section: Including Working At Home In An Expanded Modelsupporting

confidence: 68%

“…Transportation researchers began to discover general studies of time use by individuals and households (e.g., Szalai, 1973;Robinson, 1977), and work began in earnest on modeling travel as a demand derived from the demand for activity participation (Damm, 1980;Kostyniuk and Kitamura, 1982;Kitamura, 1984;Pas, 1984). About the same time, Zahavi (1979) exposed patterns of time use in travel that are consistent with a joint activity and travel time setting (Zahavi and McLynn, 1983;Zahavi and Talvitie, 1980;Golob, et al, 1981).…”

Section: Background and Objectivesmentioning

confidence: 99%

“…We selected thirteen exogenous variables, based on extensive prior research (e.g., Kostyniuk and Kitamura, 1982, Supernak, 1983, Pas, 1984, Townsend, 1987, Strathman and Dueker, 1994, and Lu and Pas, 1997 and exploratory analyses. The exogenous variables can be divided into three groups: household demographics (six variables), socioeconomic variables (five variables), and variables to account for differences in time use by day of week.…”

Section: The Exogenous Variablesmentioning

confidence: 99%

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A simultaneous model of household activity participation and trip chain generation

Golob

2000

Transportation Research Part B: Methodological

186

103

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A trip generation model has been developed using a time-use perspective, in which trips are generated in conjunction with out-of-home activities, and time spent traveling is another component of overall time use. The model jointly forecasts three sets of endogenous variables -(1) activity participation and (2) travel time (together making up total out-of-home time use), and (3) trip generation --as a function of household characteristics and accessibility indices. It is estimated with data from the Portland, Oregon 1994 Activity and Travel Survey. Results show that the basic model, which has ten endogenous time use and trip generation variables and thirteen exogenous variables, fits well, and all postulated relationships are upheld. Test show that the basic model, which divides activities into work and nonwork, can be extended to a three-way breakdown of subsistence, discretionary and obligatory activities. The model can also capture the effects of in-home work on trip chaining and activity participation. We use the model to explore the effects on time use and trip chaining of GIS-based and zone-based accessibility indices.A simultaneous model of household activity participation and trip chain generation Tom Golob 2 BACKGROUND AND OBJECTIVES

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Section: Including Working At Home In An Expanded Modelsupporting

confidence: 68%

Section: Background and Objectivesmentioning

confidence: 99%

Section: The Exogenous Variablesmentioning

confidence: 99%

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A simultaneous model of household activity participation and trip chain generation

Golob

2000

Transportation Research Part B: Methodological

186

103

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“…This hypothesis is in line with geographical economics literature, where the interregional mobility of workers is mainly analysed using a high and low skills dichotomy (see for instance Pas (1984); Pissarides and Wadsworth (1989); Korsu and Wenglenski (2010)). Further, following Beaverstock's (2004) analysis of intercity trajectories of managerial elites and Taylor, Catalano, and Walker's (2002) observation of intercity flows between service firms, we see that white collar business trips are a major driving force of transnational flows between cities.…”

Section: Model Componentssupporting

confidence: 58%

Assessing geographical effects in spatial diffusion processes: The case of euro coins

Texier

Caruso

2017

Computers, Environment and Urban Systems

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International audienceWe examine how geographical structures impact diffusion processes within a regional system. From the example of euro coin diffusion across countries, we show how the relative position and population endowment of regions impact our understanding of interregional mobility, beyond simple spatial interaction effects. The mix of coins of different origins is a complex but unique trace of the movement of individuals within a common currency area, potentially revealing a new facet of European integration. We simulate an individual-based dynamic model where agents move and exchange coins across regions. We analyse the convergence towards a homogeneous mix of coins through time for a series of different theoretical spatial systems. This sensitivity analysis demonstrates the impact of the regularity and aggregation levels, or centrality/periphery effects, on spatial diffusion dynamics. We then calibrate the model against empirical data for the regions of 5 European countries and provide estimates of mobility rates, distance decay and population attractiveness factors, affecting the diffusion of coins, hence international movements and European integration

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“…As mobility is a derived demand, different individuals with different socio-demographic characteristics and different accessibilities and spatial opportunities generate different activity-travel patterns (Hanson 1980;Herz 1983;Pas 1984;Kitamura 1988). Schwanen et al (2003) report that the characteristics of individuals and their positions in the household have a stronger influence on commuting times by car than the urban structure in which people reside and work.…”

Section: The Influence Of the Metropolitan Structure On Commuting Patmentioning

confidence: 99%

The influence of built environment to the trends in commuting journeys in the Netherlands

2007

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In this paper we describe commuting trends in the Netherlands in the past decade and examine the influence of urban form and travel accessibility on commuting journeys over time on the basis of data from the Dutch National Travel Survey. Exploratory analysis is performed to identify changes in commuting participation, departure time, commuting time, commuting distance and the modal split. Regression analysis and choice models are used to examine the influence of the built environment on commuting parameters over time. The results indicate that urban form has consistently influenced the parameters of commuting journey in the Netherlands in the last 10 years. However, the trend of the influence is unique for each commuting model. Some influences have become less significant in the last decade and some have become stronger.

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The Effect of Selected Sociodemographic Characteristics on Daily Travel-Activity Behavior

Cited by 145 publications

References 10 publications

A simultaneous model of household activity participation and trip chain generation

A simultaneous model of household activity participation and trip chain generation

Assessing geographical effects in spatial diffusion processes: The case of euro coins

The influence of built environment to the trends in commuting journeys in the Netherlands

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