Advertisement

Application of Information on Human Activity-Travel Behavior in Urban Space and Time in the Information Age

  • Nobuaki Ohmori
Part of the cSUR-UT Series: Library for Sustainable Urban Regeneration book series (LSUR, volume 5)

Abstract

Understanding mechanisms underlying individual travel behavior in urban space and time is essential for investigating effective policy measures for sustainable urban regeneration. Conventionally, travel is considered a demand, derived from the desire to engage in activities at certain locations. Hence, an understanding of the relationships between travel behavior and daily activity engagement is necessary to estimate individual and household responses to policy measures and to changes in environmental constraints. In this context, rather than a “trip-based approach,” an “activity-based approach” that originated from a series of studies in the 1970s at the Transport Studies Unit (TSU) of Oxford University (Jones et al. 1983) is useful, and has promise. This approach emphasizes that travel behavior is a result of decision making in activity participation under the spatial and temporal constraints of the urban activity and transport system which individuals face in their daily life and household interactions. It also recognizes the importance of integrating transport and land use planning.

Keywords

Global Position System Travel Behavior Spatial Data Infrastructure Stated Preference Survey Diary Survey 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aono S, Ohmori N, Harata N (2004) Development of an Internet-based travel survey system. Proceedings of the International Symposium on City Planning 2004, pp 41–50Google Scholar
  2. Aono S, Ohmori N, Harata N (2006) Development of a Web-GIS simulator for holiday non-work activities. Proceedings of the 5th International Conference on Traffic and Transportation Studies, pp 959–967Google Scholar
  3. Aono S, Takahashi O, Seto Y, Ohmori N, Harata N (2007) Development and application of a Web based activity-travel simulator for travel feedback program. Proceedings of the 10th International Conference on Computers in Urban Planning and Urban Management, CD-ROMGoogle Scholar
  4. Arentze TA, Timmermans HJP (2000) Albatross: A learning-based transportation oriented simulation system. European Institute of Retailing and Services Studies, Eindhoven, The NetherlandsGoogle Scholar
  5. Asakura Y, Hato E (2004) Tracking survey for individual travel behaviour using mobile communication instruments. Transportation Research C 12: pp 273–291CrossRefGoogle Scholar
  6. Bowman JL, Ben-Akiva ME (1997) Activity-based forecasting. In: Texas Transportation Institute (eds) Activity-based travel forecasting conference, June 2–5, 1996. Summary, recommendations, and compendium of papers. Travel Model Improvement Program. U.S. Department of Transportation and U.S. Environmental Protection Agency, Washington D.C.Google Scholar
  7. Brög W (1998) Individualized Marketing: implications for TDM. Proceedings of the 77th Annual Meeting of the Transportation Research Board, CD-ROMGoogle Scholar
  8. Burns LD (1979) Transportation, temporal, and spatial components of accessibility. Lexington Books, LexingtonGoogle Scholar
  9. Department of Transport, Western Australia (2000) Travel Smart: a cost-effective contribution to transport infrastructure. PerthGoogle Scholar
  10. Dijst MJ (2004) ICT and accessibility: an action space perspective on the impact of new information and communication technologies. In: Beuthe E, Himanen V, Reggiani A, Zamparini L (eds) Travel demand and organization in an evolving world. Springer, Berlin, pp 27–46Google Scholar
  11. Doherty ST, Miller EJ (2000) A computerized household activity scheduling survey. Transportation 27: pp 75–97CrossRefGoogle Scholar
  12. Ettema D, Borgers A, Timmermans HJP (1996) SMASH (Simulation Model of Activity Scheduling Heuristics): some simulations. Transportation Research Record 1551: pp 88–94Google Scholar
  13. Fujii S, Taniguchi A (2005) Reducing family car-use by providing travel advice or requesting behavioral plans: an experimental analysis of travel feedback programs. Transportation Research D 10: pp 385–393CrossRefGoogle Scholar
  14. Fujii S, Taniguchi A (2006) Determinants of the effectiveness of travel feedback programs—a review of communicative mobility management measures for changing travel behaviour in Japan. Transport Policy 13: pp 339–348CrossRefGoogle Scholar
  15. Gärling T, Brannas K, Garvill J, Golledge RG, Opal S, Holm E, Lindberg E (1989) Household activity scheduling. Selected proceedings of the 5th World Conference on Transport Research 4, pp 235–248Google Scholar
  16. Golob TF (2001) Travel Behavior Com: activity approaches to modeling the effects on information technology on personal travel behavior. In: Hensher DA (ed) Travel Behaviour Research: The Leading Edge. Pergamon, Oxford, pp 145–183CrossRefGoogle Scholar
  17. Harvey AS (2003) Time-space diaries: merging traditions. In: Stopher P, Jones P (eds) Transport Survey Quality and Innovation. Pergamon, Oxford, pp 151–180Google Scholar
  18. Harvey AS, Macnab PA (2000) Who’s up? global interpersonal temporal accessibility. In: Janelle DG, Hodge DC (eds) Information, Place, and Cyberspace: Issues in Accessibility. Springer, Berlin, pp 147–170Google Scholar
  19. Hägerstrand T (1970) What about people in regional science? Papers of the Regional Science Association 24: pp 7–21Google Scholar
  20. Izumiyama H, Ohmori N, Harata N (2007) Space-time accessibility measures for evaluating mobility-related social exclusion of the elderly. Proceedings of 11th International Conference on Mobility and Transport for Elderly and Disabled Persons. CD-ROMGoogle Scholar
  21. Jones PM (1982) ‘HATS’ educational manual: studying travel in the context of household activity patterns. Transport Studies Unit, Oxford University, Ref.193/PRGoogle Scholar
  22. Jones PM, Dix MC, Clarke MI, Heggie IG (1983) Understanding travel behavior. Gower, AldershotGoogle Scholar
  23. Jones PM (2003) Encouraging behavioral change through marketing and management: what can be achieved? Paper presented at the 10th International Conference on Travel Behavior Research Lucerne, August 2003Google Scholar
  24. Kenyon S, Lyons G, Rafferty J (2002) Transport and social exclusion: investigating the possibility of promoting inclusion through virtual mobility. Journal of Transport Geography 10: pp 207–219CrossRefGoogle Scholar
  25. Kenyon S (2006) The ‘accessibility diary’: discussing a new methodological approach to understand the impact of Internet use upon personal travel and activity participation. Transport Geography 14: pp 123–134CrossRefGoogle Scholar
  26. Kenyon S, Lyons G (2007) Introducing multitasking to the study of travel and ICT: examining its extent and assessing its potential importance. Transportation Research A 41: pp 161–175Google Scholar
  27. Kitamura R (1997) Applications of models of activity behavior for activity-based demand forecasting. In: Texas Transportation Institute (eds), Activity-based travel forecasting conference, June 2–5, 1996. Summary, recommendations, and compendium of papers. Travel Model Improvement Program. U.S. Department of Transportation and U.S. Environmental Protection Agency, Washington D.C.Google Scholar
  28. Kwan M-P (1998) Space-time and integral measures of individual accessibility: a comparative analysis using a point-based framework. Geographical Analysis 30: pp 191–216Google Scholar
  29. Kwan M-P (1999) Gender and individual access to urban opportunities: a study using space-time measures. Professional Geographer 51: pp 210–227CrossRefGoogle Scholar
  30. Lee M, McNally MG (2001) Experiments with a computerized self-administered activity survey. Transportation Research Record 1752: pp 91–99CrossRefGoogle Scholar
  31. Lenntorp B (1978) A time-geographic simulation model of individual activity programmes. In: Carlstein T, Parks D, Thrift N (eds) Timing Space and Spacing Time 2, Human Activity and Time Geography. Arnold, London, pp 162–180Google Scholar
  32. Lenz B, Nobis C (2007) The changing allocation of activities in space and time by the use of ICT — “fragmentation” as a new concept and empirical results. Transportation Research A 41: pp 190–204Google Scholar
  33. Lyons G, Urry J (2005) Travel time use in the information age. Transportation Research A 39: pp 257–276Google Scholar
  34. Lyons G, Jain J, Holley D (2007) The use of travel time by rail passengers in Great Britain. Transportation Research A 41: pp 107–120Google Scholar
  35. Meyer MD, Miller EJ (2001) Urban transportation planning: a decision-oriented approach, 2nd ed. McGraw-Hill, New YorkGoogle Scholar
  36. Miller HJ (1999) Measuring space-time accessibility benefits within transportation networks: basic theory and computational procedures. Geographical Analysis, 31: pp 187–212Google Scholar
  37. Mokhtarian PL, Salomon I (2001) How derived is the demand for travel? some conceptual and measurement considerations. Transportation Research A 35: pp 695–719CrossRefGoogle Scholar
  38. Mokhtarian PL, Salomon, I, Handy SL (2006) The impact of ICT on leisure activities and travel: a conceptual exploration. Transportation 33: pp 263–289CrossRefGoogle Scholar
  39. Murakami E, Wagner DP (1999) Can using global positioning system (GPS) improve trip reporting? Transportation Research C 7: pp 149–165CrossRefGoogle Scholar
  40. Nakazato M, Ohmori N, Aono S, Maruyama T, Harata N (2006) Internet GIS-based activity-travel simulator for investigating alternative activity-travel patterns. Paper presented at the 11th International Conference on Travel Behaviour Research, Kyoto, August 2006Google Scholar
  41. Nishii K, Sasaki K, Kitamura R, Kondo K (2005) Recent Developments in activity diary-based surveys and analysis: some Japanese case studies, In: Timmermans H (ed) Progress in Activity-Based Analysis. Elsevier, Oxford, pp 335–354CrossRefGoogle Scholar
  42. Niwa Y, Ohmori N (2006) Communications behavior of young couples: through four-week activity-telecommunications diary and depth interview surveys (in Japanese). Papers on City Planning 41: CD-ROMGoogle Scholar
  43. Ohmori N, Muromachi Y, Harata N, Ohta K (1998) Possibility of GPS to travel behavior survey (in Japanese). Proceedings of the 18th Traffic Engineering Conference, pp 5–8Google Scholar
  44. Ohmori N, Muromachi Y, Harata N, Ohta K (1999) A study on accessibility and going-out behavior of aged people considering daily activity pattern. Journal of the Eastern Asia Society for Transportation Studies 3: pp 139–153Google Scholar
  45. Ohmori N, Muromachi Y, Harata N, Ohta K (2000) Travel behavior data collected using GPS and PHS. Proceedings of the 2nd International Conference on Traffic and Transportation Studies, pp 851–858Google Scholar
  46. Ohmori N, Harata N, Ohta K (2001) The effects of using telecommunications on individual activity schedule (in Japanese). Infrastracture Planning Review 18: pp 587–594Google Scholar
  47. Ohmori N, Muromachi Y, Harata N, Ohta K (2003) Simulation Model for Activity Planning (SMAP): GIS-based gaming simulation. In: Park C-H, Cho JR, Oh J, Hayashi Y, Viegas J (eds) Selected Proceedings from the 9th World Conference on Transport Research. Elsevier, Oxford, CD-ROMGoogle Scholar
  48. Ohmori N, Harata N (2004) Evaluation of space-time accessibility considering daily activity engagement of the elderly. Proceedings of the 10th International Conference on Mobility and Transport for Elderly and Disabled Persons, CD-ROMGoogle Scholar
  49. Ohmori N, Hirano T, Harata N (2004) Passengers’ waiting behavior at bus stop. Proceedings of the 4th International Conference on Traffic and Transportation Studies, pp 157–164Google Scholar
  50. Ohmori N, Harata N, Ohta K (2005) Two applications of GIS-based activity-travel simulators. In: Timmermans H (ed), Progress in Activity-Based Analysis. Elsevier, Oxford, pp 415–435CrossRefGoogle Scholar
  51. Ohmori N (2006) Connected anytime: telecommunications and activity-travel behavior from Asian perspectives. Paper presented at the 11th International Conference on Travel Behaviour Research, Kyoto, August 2006Google Scholar
  52. Ohmori N, Harata N (2006) Activities while commuting by train: a case in Tokyo. Paper presented at the 2nd International Specialist Meeting on ICT, Everyday Life and Urban Change, Bergen, November 2006Google Scholar
  53. Ohmori N, Hirano T, Harata N (2006a) Meeting appointment and waiting behavior with mobile communications. Transportation Research Record 1977: pp 250–257Google Scholar
  54. Ohmori N, Nakazato M, Harata N, Sasaki K, Nishii K (2006b) Activity diary surveys using GPS mobile phones and PDA. Compendium of Papers of the 85th Annual Meeting of the Transportation Research Board, CD-ROMGoogle Scholar
  55. Recker WW, McNally MG, Root GS (1986) A model of complex travel behavior: Part I: theoretical development. Transportation Research B 20: pp 307–318CrossRefGoogle Scholar
  56. RDC, Inc. (1995) Activity-based modeling system for travel demand forecasting. Report DOT-T-96-2. Washington, D.C: U.S. Department of TransportationGoogle Scholar
  57. Redmond LS, Mokhtarian PL (2001) The positive utility of the commute: modeling ideal commute time and relative desired commute amount. Transportation 28: pp 179–205CrossRefGoogle Scholar
  58. Rose G, Ampt E (2001) Travel Blending: an Australian travel awareness initiative. Transportation Research D 6: pp 95–110CrossRefGoogle Scholar
  59. Salomon I (1985) Telecommunications and travel: substitution or modified mobility? Journal of Transport Economics and Policy 19: pp 219–235Google Scholar
  60. Segawa S, Sadahiro Y (1995) A policy making support system for child care facilities using GIS (in Japanese). Papers and Proceedings of the Geographic Information Systems Association 4: pp 59–64Google Scholar
  61. Wermuth M, Sommer C, Kreitz M (2003) Impact of new technologies in travel surveys. In: Stopher P, Jones PM (eds), Transport Survey Quality and Innovation. Pergamon, Oxford, pp 151–180Google Scholar
  62. Zhang M, Marchau V, van Wee B, van der Hoorn T (2006) Wireless internet on trains: the impacts on the performance of business travelers. Compendium of Papers of the 85th Annual Meeting of the Transportation Research Board, CD-ROMGoogle Scholar
  63. Zhou J, Golledge R (2004) Real-time tracking of activity scheduling/schedule execution within unified data collection framework. Compendium of Papers of the 83rd Annual Meeting of the Transportation Research Board, CD-ROMGoogle Scholar

Copyright information

© Springer 2008

Authors and Affiliations

  • Nobuaki Ohmori
    • 1
  1. 1.Department of Urban EngineeringThe University of TokyoTokyoJapan

Personalised recommendations