Advertisement

Facility Use-Choice Model with Travel Costs Incorporating Means of Transportation and Travel Direction

  • Toshihiro OsaragiEmail author
  • Sayaka Tsuda
Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Estimating the number of users and their spatial distribution is necessary in the planning process of new public facilities. In the present study, we construct a model based on the nested logit model that is composed of facilities’ utility and users’ travel costs to describe facility use-choice behavior with respect to the facilities. The travel costs are described in terms of network distance, means of transportation, direction of travel, and number of transfers. As a numerical analysis, validation of the proposed model is achieved by estimating the number of users and their spatial distribution for newly constructed public libraries.

Keywords

Catchment Area Travel Cost Residential Location Minor District User Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to acknowledge the valuable comments and useful suggestions from anonymous reviewers to improve the content and clarity of the chapter. A part of this paper was presented at Journal of Architectural Planning and Engineering, AIJ, “Travel costs considering means and direction of movement within a city incorporated with a model of facility choice behavior”, Vol. 77, no. 676, pp. 1293–1300, 2012 (in Japanese).

References

  1. Apparicio P, Abdelmajid M, Rival M, Shearmur R (2008) Comparing alternative approaches to measuring the geographical accessibility of urban health services: Distance types and aggregation-error issues. Int J Health Geograph 7(7):1–14Google Scholar
  2. Arentze T, Borgers A, Timmermans H (1994) Geographical information systems and the measurement of accessibility in the context of multipurpose travel: a new approach. Geograph Syst 1:87–102Google Scholar
  3. Burns LD (1979) Transportation, temporal, and spatial components of accessibility. Lexington Books, LexingtonGoogle Scholar
  4. Cliquet G (2002) Geomarketing—methods and strategies in spatial marketing, ISTEGoogle Scholar
  5. Comber A, Brunsdon C, Green E (2008) Using a GIS-based network analysis to determine urban greenspace accessibility for different ethnic and religious group. Landscape Urban Plann 86(1):103–114CrossRefGoogle Scholar
  6. Ettema D, Timmermans H (2007) Space–time accessibility under conditions of uncertain travel times: theory and numerical simulations. Geograph Anal 39:217–240CrossRefGoogle Scholar
  7. Geospatial Information Authority of Japan (GSI) (1998) Instruction book of detailed digital information, Japan Map CenterGoogle Scholar
  8. Green C, Breetzke K, Mans G (2009) GIS based evaluation of public facility provision to achieve improved governance and equitable service delivery. Geo multimedia 2009: 14th international conference on urban planning and regional development in the information society, pp 1–94Google Scholar
  9. Hsu C, Hsieh Y (2004) Travel and activity choices based on an individual accessibility model. Papers in Regional Science 83:387–406CrossRefGoogle Scholar
  10. Inoi H, Nakaoka R (2007) Research on the palliation of physical burden from slope by community bus. Infrastructure planning review, Japan society of civil engineers, vol 36. CD-ROMGoogle Scholar
  11. Nakamura K, Kurihara K (1998) The way of planning the library system to regional area: Fundamental investigation for the planning of library system to community area (12). J Arch Plann Eng 512:123–130 in JapaneseGoogle Scholar
  12. Neutens T, Delafontaine M, Schwanen T, Van de Weghe N (2012) The relationship between opening hours and accessibility of public service delivery. J Transp Geogr 25:128–140CrossRefGoogle Scholar
  13. Osaragi T (2002) Accessibility evaluation: effects of free return system on users’ behaviour of public libraries. Env PlannB: Plann Des 29(5):637–654CrossRefGoogle Scholar
  14. Osaragi T (2009) Estimating spatio-temporal distribution of railroad users and its application to disaster prevention planning (Lecture notes in geoinformation and cartography). In: Sester M et al. (eds) Advances in GI Science, Springer, Berlin, pp 233–250Google Scholar
  15. Peterson K (2004) The power of place—Advanced customer and location analytics for market planning, IntegrasGoogle Scholar
  16. Satoh E, Yoshikawa T, Yamada A (2008) Examination of continuity of local living based on the converted walking distance: Model for location planning of regional facilities considering topographical condition and aging society Part 2. J Arch Plann Eng 625:611–618CrossRefGoogle Scholar
  17. Schüssler F (2006) Gemoarketing—Anwendung Geographischer Informations-systeme im Einzelhandel, TectumGoogle Scholar
  18. Teixeira JC, Antunes AP (2008) A hierarchical location model for public facility planning. Eur J Oper Res 185(1):92–104CrossRefGoogle Scholar
  19. Wang F, Luo W (2005) Assessing spatial and nonspatial factors for healthcare access: Towards an integrated approach to defining health professional shortage areas. Health Place 2:131–146CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Mechanical and Environmental Informatics, Graduate School of Information Science and EngineeringTokyo Institute of TechnologyTokyoJapan

Personalised recommendations