Spatial Constraints-Oriented Modeling as an Alternative Approach to Movement, Microeconomic Theory, and Urban Policy

Burnett, Pat

doi:10.2747/0272-3638.1.1.53

Cited by 43 publications

(12 citation statements)

References 17 publications

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“…Richardson and Young (1982) showed that accessibility measures that ignore trip linking or trip chaining may considerably underestimate the accessibility to activities of noncentral urban locations. Further, integral measures also overlook the important impact of spatio-temporal constraints since many opportunities in the urban environment may be out of reach for an individual because of them (Burnett 1980;Kwan and Hong 1998). The ignorance of individual activity sequence and spacetime constraints points to the need for alternative measures of individual accessibility which avoid the inherent difficulties of integral measures.…”

Section: Integral and Space-time Accessibility Measuresmentioning

confidence: 99%

“…First, they ascribe the same level of accessibility to different individuals in the same zone even though what these individuals experienced may suggest otherwise (Pirie 1979). Second, as these conventional indices are integral measures which evaluate accessibility based on a single reference location such as the home, they ignore the fact that many trips that contribute to individual accessibility are made in the context of the sequential unfolding of a person's daily activity program (Hanson 1980a,b;Richardson and Young 1982).2 Third, these measures do not take into account the effect of spatiotemporal constraints that may render many opportunities in the urban environment unreachable by an individual (Burnett 1980;Landau, Prashker, and Alpern 1982).…”

mentioning

confidence: 99%

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Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework

Kwan

1998

Geographical Analysis

915

509

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Section: Integral and Space-time Accessibility Measuresmentioning

confidence: 99%

mentioning

confidence: 99%

Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework

Kwan

1998

Geographical Analysis

915

509

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“…Constraints have drawn some attention in past research (BrSg and Erl, 1981;Burnett, 1980) but seem to have been understudied in discrete choice models--that is, models are often built as though everyone has the same choice set. We attribute significant explanatory power to the understanding of the role constraints play in the choice of telecommuting.…”

Section: Constraint Variables 41 the Role Of Constraintsmentioning

confidence: 99%

Modeling the Choice of Telecommuting: 2. A Case of the Preferred Impossible Alternative

Mokhtarian

Salomon

1996

Environ Plan A

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A conceptual model of the choice to telecommute was advanced in an earlier paper. In this paper we present empirical data from a nonrepresentative sample of 628 City of San Diego employees on key variables and relationships in that model. The relationships among possibility, preference, and choice are examined. A key finding is the existence of a large group of people (57% of the sample) for whom telecommuting is a preferred impossible alternative. Dichotomous and continuous constraints are distinguished, and three dichotomous constraints are defined. ‘Lack of awareness’ is active for 4%, ‘job unsuitability’ for 44%, and ‘manager disapproval’ for 51% of the sample. For 68% of the sample, at least one of these constraints is active. Even among those for whom none of the dichotomous constraints is in force, most people do not choose telecommuting because of the presence of active continuous constraints. For only 11% of the entire sample, telecommuting is possible, preferred, and chosen. The potential impacts of self-selection bias are estimated, and sampling bias is qualitatively assessed. This analysis provides a crude but useful estimate of the potential of telecommuting in the population, and more specifically, the relative share of potential telecommuters who are prevented by key dichotomous constraints from choosing that option.

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“…(1979); Burnett (1980); these integrate the choice set formation step into the estimation procedure by jointly estimating choice set selection and selection of a particular alternative of this choice set (Manski, 1977;Ben-Akiva and Boccara, 1995). Probabilistic choice set formation is conceptually appealing; choice sets are, in principle, not restrained a priori by exogenous criteria, as in standard choice set speci cation.…”

Section: Destination Choice Set Speci Cationmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

499

123

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PrefaceDeveloping complex so ware for over a decade with a heterogeneous group of engineers and scientists, each with widely di erent skill levels and expertise across multiple locations around the world, requires dedication and mechanisms unusual for a university environment. This book is one of these mechanisms. It allows us, collectively, to take stock and present a coherent state-of-the-system: for us and anyone interested in this approach. It highlights basics for the student who wants to undertake a small rst research project as part of his or her degree, provides a description of the main functionalities, in detail, for the engineer setting up MATSim (MultiAgent Transport Simulation) to conduct a policy analysis and, nally, ts the approach into the theoretical background of complex systems in computer science and physics.The choice of the additional e-book format is an advantage, as it allows us to keep the book upto-date with future chapters, revisions and, if necessary, errata. Equally importantly it allows you, the readers, to select those sections relevant to your needs.The book comes at an important time for the system; for most of the rst decade, its use was limited to the original developers and users in Berlin and Zürich. It is now much more widely consulted around the world, as we document in the chapter summarizing contributions on scenarios so far.Scenario: This term will occur again and again. In MATSim context, it is de ned as the combination of speci c agent populations, their initial plans and activity locations (home, work, education), the network and facilities where, and on which, they compete in time-space for their slots and modules, i.e., behavioral dimensions, which they can adjust during their search for equilibrium. Within these scenarios, the user can experiment and explore with behavioral utility function parameters, with the sampling rate of the population between 1 % and 100 %, with algorithm parameters, e.g., the share of the sample engaged in replanning in any iteration, or behavioral dimensions or exact settings necessary to avoid gridlock due to the tra c ow dynamics. The creation of a scenario is a substantial e ort, and the framework makes a number of tools available to accelerate it: population synthesizers, network editors, network converters between popular formats and the MATSim representation, e.g., OSM (OpenStreetMap) or GTFS (General Transit Feed Speci cation), semi-automatic network matching to join information, among others.A large group of colleagues has been involved and many of them are contributors to this book; this is a list of those involved, other than ourselves, in Berlin, Singapore and Zürich. Additional contributors are mentioned as authors of their respective chapters in this book. We hope to acknowledge the contributions of more colleagues from other groups in future versions of this book and in the so ware.Special thanks go to a number of people who greatly helped improving this book beyond their own chapters. Benjamin Kickhöfer's deep knowledge of ...

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Spatial Constraints-Oriented Modeling as an Alternative Approach to Movement, Microeconomic Theory, and Urban Policy

Cited by 43 publications

References 17 publications

Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework

Space‐Time and Integral Measures of Individual Accessibility: A Comparative Analysis Using a Point‐based Framework

Modeling the Choice of Telecommuting: 2. A Case of the Preferred Impossible Alternative

The Multi-Agent Transport Simulation MATSim

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