The Role of GIS in Land Use and Transport Planning

Slavin, Howard

doi:10.1108/9781615832538-019

Cited by 5 publications

(3 citation statements)

References 2 publications

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“…• CUBE, EMME /2, TransCAD, VISUM (Florian 1999, 2008, Slavin, 2004 • SCOOT (1980s), SCATS (1982) • Microscopic traffic simulation models (Category B, CORSIM, PARAMICS, VISSIM, AIMSUN)…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling

Jin

Boyce

Qiu

et al. 2012

Journal of Intelligent Transportation Systems

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In this paper, we attempt to summarize the impact of technologies, especially intelligent transportation system (ITS) technologies, on transportation research during the last several decades and provide perspectives on how future transportation research may be affected by the availability and development of new ITS technologies. The intended audience of the paper includes young transportation researchers and professionals. Current transportation models are divided into "generations" based on their technological and practical background. Based on the trends in the past and the potential technologies to be implemented in the future, general characteristics of the next generations of transportation models are proposed and discussed to provide a vision regarding expected future achievements in transportation research. This paper is intended to be a working document, in the sense that it will be updated periodically.

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Section: Challenges and Opportunitiesmentioning

confidence: 99%

Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling

Jin

Boyce

Qiu

et al. 2012

Journal of Intelligent Transportation Systems

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“…), this research is concerned with the instantiation of those procedures in GIS. More specifically, it is common to distinguish between general‐purpose GIS and GIS‐T software designed specifically for transportation applications (Waters ; Slavin ); this research considers the former. Early efforts at comparing routing heuristics examined “microcomputer‐based vehicle routing software” prior to the widespread availability of GIS (Golden et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Comparative Analysis of Traveling Salesman Solutions from Geographic Information Systems

Curtin

Voicu

Rice

et al. 2013

Transactions in GIS

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The Traveling Salesman Problem is one of the most prominent problems in combinatorial optimization, and is regularly employed in a wide variety of applications. The objective of this article is to demonstrate the extent of sub‐optimality produced by Traveling Salesman solution procedures implemented in the context of Geographic Information Systems and to discuss the consequences that such solutions have for practice. Toward that end, an analysis is made of Traveling Salesman solutions from implementations in four Geographic Information System packages. These implementations are tested against the optimal solution for a range of problem sizes. Computational results are presented in the context of a school bus routing application. This analysis concludes that no Traveling Salesman implementation in GIS is likely to find the optimal solution when problems exceed 10 stops. In contrast, optimal solutions can be generated with desktop linear programming software for up to 25 cities. Moreover, one GIS implementation consistently found solutions that were closer to optimal than its competitors. This research strongly suggests that for applications with fewer than 25 stops, the use of an optimal solution procedure is advised, and that GIS implementations can benefit from the integration of more robust optimization techniques.

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“…Therefore, it is necessary to have information on attributes of the zones and data on individual and household characteristics. With respect to the former, geographical information systems (GIS) techniques can be used in order to provide information on land use, density characteristics and accessibility of each zone (Slavin, 2004).…”

Section: Introductionmentioning

confidence: 99%

How to Incorporate the Spatial Dimension in Destination Choice Models: The Case of Antwerp

Hammadou

Thomas

Verhetsel

et al. 2008

Transportation Planning and Technology

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This paper presents and estimates destination choice models based on a large sample of intra-urban trips. Particular attention is paid to incorporating the effects of the spatial dimension. The data used relate to non-work trips in the agglomeration of Antwerp (Belgium). A geographical analysis is performed in order to represent the city and its suburbs by a limited set of zones of destinations and to characterize these zones in terms of land use. Different types of discrete choice model are compared in terms of utility function, global formulation and performance. The mixed nested logit formulation with random coefficients appears to be the most attractive. The results confirm the difficulty of grasping spatial realities by simple quantitative measurements but also illustrate the importance of 'space' when choosing a destination. The empirical results also show that land use and urban development policies clearly have their effect on urban mobility.

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The Role of GIS in Land Use and Transport Planning

Cited by 5 publications

References 2 publications

Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling

Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling

A Comparative Analysis of Traveling Salesman Solutions from Geographic Information Systems

How to Incorporate the Spatial Dimension in Destination Choice Models: The Case of Antwerp

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