Longitudinal Model of Longer-Term Mobility Decisions: Framework and First Empirical Tests

Oakil, Abu Toasin; Ettema, Dick; Arentze, TA Theo; Timmermans, Hjp Harry

doi:10.1061/(asce)up.1943-5444.0000066

Cited by 27 publications

(17 citation statements)

References 20 publications

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“…Other forms of longitudinal data, such as retrospective data were used for similar purposes (e.g. Oakil et al, 2011). However, to our knowledge our study is the first to use panel data to look into family visits.…”

Section: Panel Datamentioning

confidence: 98%

The determinants of mode choice for family visits – evidence from Dutch panel data

Rubin

Mulder

Bertolini

2014

Journal of Transport Geography

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a b s t r a c tWe use panel data to estimate multinomial logistic regressions for the effect of household composition, car ownership, distance between family members, and degree of urbanization on mode choice for family visits in the Netherlands. Based on Mundlak's formulation our model accounts for unobserved heterogeneity and differentiates within-individual from between-individual variation. With respect to household composition, we find that living with a partner and having a child under six years old is negatively associated with the likelihood of using public transport for family visits. Number of children is not associated linearly with mode choice. Walking and cycling is mainly associated with distance between family members: the shorter the distance the higher the likelihood of using slow-modes instead of a car. Those travelling between areas of high degree of urbanization have a higher likelihood of using public transport relative to using cars. Car ownership is negatively associated with all other modes. Using a car for commuting is also found to be negatively associated with other modes for family visits.

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Section: Panel Datamentioning

confidence: 98%

The determinants of mode choice for family visits – evidence from Dutch panel data

Rubin

Mulder

Bertolini

2014

Journal of Transport Geography

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“…The retrospective survey approach is relatively easy and convenient, although such surveys covering long periods do raise questions regarding the accuracy of memory recall. In the literature, it is more common to use the retrospective approach to obtain information on the life courses (also, sometimes referred to as biographies) of events (see, for example, (4,27,31,32)). These life course models may also be incorporated in comprehensive models of urban land-use, though this is perhaps not as straightforward as for cross-sectional micro-models because of the many intricate linkages and sequentialities that need to be appropriately considered and implemented.…”

Section: Methods Used For Residential Relocation Modelingmentioning

confidence: 99%

Joint Model of Residential Relocation Choice and Underlying Causal Factors

Kortum

Paleti

Bhat

et al. 2012

Transportation Research Record

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Residential location choice is a key determinant of activity-travel behavior and yet, little is known about the underlying reasons why people choose to move, or not move, residences. Such understanding is critical to being able to model residential location choices over time, and design built environments that people find appealing. This paper attempts to fill this gap by developing a joint model of the choice to move residence and the primary reason for moving (or not moving). The model is estimated on the Florida subsample of the 2009 National Household Travel Survey. Model results shed considerable light on the socio-economic and demographic variables that impact household decision whether to move residence and the primary reason underlying that decision. Kortum, Paleti, Bhat and Pendyala 1 INTRODUCTIONResidential location choice is a topic of much interest because decisions about where to work, shop, go to school, or pursue recreational activities are all inextricably tied to people's residential location (1). Although there is considerable literature devoted to modeling and describing residential location choice behavior, an understanding of the underlying factors that contribute to a household decision to relocate residence (or not) continues to be challenging and in need of further enquiry. To set the context, we first briefly identify the factors that have been identified as determinants of residential relocation in the next section, followed by an overview of the methods used for residential relocation modeling in the subsequent section. Within each of these two sections, we position and highlight the salient aspects of this study.

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“…Such events bring changes in life, which may create a different context for individuals, leading them to rethink their present behaviors (Dargay & Hanly, 2007;Lanzendorf, 2003;Oakil, Ettema, Arentze, & Timmermans, 2011c;van der Waerden, Borgers, & Timmermans, 2003). A first mechanism is that a change in life circumstances directly impacts on the characteristics of the commute trip, such as travel time and cost.…”

Section: Introductionmentioning

confidence: 99%

Bicycle commuting in the Netherlands: An analysis of modal shift and its dependence on life cycle and mobility events

Oakil

Ettema

Arentze

et al. 2014

International Journal of Sustainable Transportation

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This article investigates the influence of both mobility events and household events on modal shift decision for bicycle commuting using data collected from a retrospective survey in the Netherlands. The results from a mixed-logit analysis illustrate the influence of several life events on commute modal shifts in addition to changes in commute time and sociodemographic variables. Job characteristics such as changes in work status and employer, mobility resource, long-term mobility events, and household events are seen to influence commuting decision by bicycle.

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Longitudinal Model of Longer-Term Mobility Decisions: Framework and First Empirical Tests

Cited by 27 publications

References 20 publications

The determinants of mode choice for family visits – evidence from Dutch panel data

The determinants of mode choice for family visits – evidence from Dutch panel data

Joint Model of Residential Relocation Choice and Underlying Causal Factors

Bicycle commuting in the Netherlands: An analysis of modal shift and its dependence on life cycle and mobility events

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