The analysis of travel as an example of complex human behavior in spatially-constrained situations: Definition and measurement issues

Burnett, Pat; Hanson, Susan

doi:10.1016/0191-2607(82)90001-2

Cited by 74 publications

(46 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Given our limited knowledge about human activity decisions, both atomistic and holistic approaches to accounting for the complexity of activity patterns are equally important and complementary (Burnett and Hanson, 1982). The atomistic or decomposing approach serves as a core part of activity-based models to predict the patterns, while the holistic approach provides theoretical and empirical foundations by identifying travel determinants.…”

Section: Discrepancy Analysis Of Activity Sequencesmentioning

confidence: 99%

“…Given that it is very difficult to capture their full complexities with all the dimensions, two general approaches have been used to measure the complexity of activity-travel patterns (Burnett and Hanson, 1982). One is decomposing an individual's activity pattern into numerous dimensions and generating separate measures for each of the dimensions.…”

Section: Discrepancy Analysis Of Activity Sequencesmentioning

confidence: 99%

“…The theory implies that "travel can be best understood in the broader context of activity patterns" (Ettema, 1996). An individual's activity pattern is a complex phenomenon resulting from interactions of multiple dimensions, such as timing, duration, locations, activity types, travel mode, trip chaining, activity jointness, activity substitution, activity priority, activity planning horizon, and so on (Burnett and Hanson, 1982).…”

Section: Discrepancy Analysis Of Activity Sequencesmentioning

confidence: 99%

See 2 more Smart Citations

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Wang

Kim²

2014

View full text Add to dashboard Cite

There is an urgent need for improved models that address the interdependencies between land use and transportation, and considerable new work is underway to develop such models in Oregon and elsewhere. These models and planning practices to integrate land use into the process, however, require the integration of massive amounts of land use data that is messy and incomplete. There have been considerable advances in the treatment of such data problems in other domains, drawing on data mining and machine-learning techniques to address issues in various domains. To date, however, little systematic effort has applied these technological advances to the problem domain of land use and transportation data. Experience suggests that as much as 70% of the total effort in developing integrated land use and transportation models is directly or indirectly associated with data development, integration and cleaning, yet there is remarkably little systematic research focused on the development of reusable methods and tools to support this problem domain.In coordination with an ongoing project of similar theme funded by University of California Transportation Center (UCTC), this project will focus on bringing interdisciplinary methods to develop land use datasets for integrated land use and transportation planning and modeling, with special attention on preserving temporal dimension of the data and monitoring data quality through indicators. Utilizing statistics and machinelearning techniques, we will develop reusable tools that will create a harmonized and coherent land use database from various public and private sources. These tools will be made available to the public and can be used by cities, counties, metropolitan planning agencies, state agencies, universities or anyone else needing to develop a usable database for use in integrated planning and modeling. All of the resulting data, with the exception of proprietary or confidential input data where there is no viable alternative, would be public and reusable for planning and research.17.

show abstract

Section: Discrepancy Analysis Of Activity Sequencesmentioning

confidence: 99%

Section: Discrepancy Analysis Of Activity Sequencesmentioning

confidence: 99%

Section: Discrepancy Analysis Of Activity Sequencesmentioning

confidence: 99%

See 1 more Smart Citation

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Wang

Kim²

2014

View full text Add to dashboard Cite

show abstract

“…Recent advances. in measuring activity-travel patterns and in exploring constraints and their effects on the set of choice options within which travel decisions are made are discussed in Burnett and Hanson (1982). This conceptually appealing approach is useful to provide deeper insights into the derived nature of travel and the structure of multipurpose and multistop trips.…”

Section: Outlook: Alternative Approaches To Modelling the Dynamics Ofmentioning

confidence: 99%

“…Burnett and Hanson (1982), for example, argue that the assumption that intra-urban travel is the outcorae of a rational decision-making process, even with limited information, seems to be dubious since increasing evidenoe indicates that travel is a stable daily routine, also a constrained choice for most, but most likely a deep-seated avoidance behaviour for many, too. The human activity constraint time bud-' get approaoh to understanding spatial ohoioe behaviour emphasizes espeeially the need to view choice decisions within a broader context and to adopt a more realistic and more complex conceptualization.…”

Section: Outlook: Alternative Approaches To Modelling the Dynamics Ofmentioning

confidence: 99%

From static towards dynamic discrete choice modelling

Fischer

Nijkamp

1987

Regional Science and Urban Economics

View full text Add to dashboard Cite

The main aim of the present paper is to survey sorae major trends in current research in the field of discrete choice modelling, with particular emphasis on dynamic approaches. The paper is organized as follows. Section 2 provides a brief overview of static disaggregate choice modelling and random utility maximimation, based inter alia on multinomial logit and/or probit models, generalized extreme value models, and nested logit models. Particular attention is given here to model representation issues, sampling and estimation issues and model performance issues. Next, section 3 is devoted to some recent developments in the rapidly growing new field of dynamic discrete choice modelling. In contrast to stochastic panel data models of buying behaviour, dynamic discrete choice models incorporate explanatory variables and take adaptive behaviour explicitly into account (i.e. the effect of past experience on choice behaviour). Several dynamic discrete choice model approaches are summarized. Special attention is paid to the seminal work of Heekman. In the final section, complementary and alternative approaches to dynamic choice modelling are discussed, such as the human activity constraint approach, the computational process modelling approach and the master equation approach. It is concluded that contextual effects, multi-actor or synergetic interactions and shifting individual preferences based-on learning principles are of primary importance in dynamic discrete choice modelling.

show abstract

Attributes Impacting Out-Of-Stall Business Contacts

Ochia

2022

Marketplace Trade and West African Urban Development

View full text Add to dashboard Cite

The analysis of travel as an example of complex human behavior in spatially-constrained situations: Definition and measurement issues

Cited by 74 publications

References 23 publications

Continuous Data Integration for Land Use and Transportation Planning and Modeling

Continuous Data Integration for Land Use and Transportation Planning and Modeling

From static towards dynamic discrete choice modelling

Attributes Impacting Out-Of-Stall Business Contacts

Contact Info

Product

Resources

About