Advertisement

Personalizing Walkability: A Concept for Pedestrian Needs Profiling Based on Movement Trajectories

  • David Jonietz
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Recently, location-based navigation systems have evolved from purely car-centered services to incorporate route planning for other means of transportation, such as walking or cycling. In this context, a particular challenge lies in the computation of optimal routes for pedestrians, who expect a high walkability of their urban environment. In particular, route computation should explicitly incorporate such specific infrastructural needs but also acknowledge the heterogeneity of pedestrians. Thus, this research proposes a concept to create individual pedestrian user profiles based on their pre-recorded movement trajectories. This information is then used for the evaluation of the expected personalized walkability of urban areas. By exemplarily applying the method to a real-world scenario, its usefulness is demonstrated.

Keywords

Personalization Walkability Movement trajectory 

References

  1. Adkins A, Dill J, Luhr G, Neal M (2012) Unpacking walkability: testing the influence of urban design features on perceptions of walking environment attractiveness. J Urban Des 17(4):499–510CrossRefGoogle Scholar
  2. Agrawal AW, Schlossberg M, Irvin K (2008) How far, by which route and why? A spatial analysis of pedestrian preference. J Urban Des 13(1):81–98CrossRefGoogle Scholar
  3. Alfonzo MA (2005) To walk or not to walk? The hierarchy of walking needs. Environ Behav 37(6):808–836CrossRefGoogle Scholar
  4. Badland H, Schofield G (2005) Transport, urban design, and physical activity: an evidence-based update. Transp Res Part D 10:177–196Google Scholar
  5. Basbas S, Konstantinidou C, Ribas DJM (2010) Factors and mechanisms which determine the outcome of strategic decisions with regard to walking. In: COST 358 Pedestrian Quality Needs Final Report, Part B, pp 127–156Google Scholar
  6. Borst HC, Miedema HM, de Vries SI, Graham JM, van Dongen JE (2008) Relationships between street characteristics and perceived attractiveness for walking reported by elderly people. J Environ Psychol 28(4):353–361CrossRefGoogle Scholar
  7. Brown BB, Szalay C (2007) Walkable enroute perceptions and physical features converging evidence for en route walking experiences. Environ Behav 39:34–61CrossRefGoogle Scholar
  8. Buchmueller S, Weidmann U (2006) Parameters of pedestrians, pedestrian traffic and walking facilities. IVT-Report Nr. 132, Institute for Transport Planning and Systems, ETH ZurichGoogle Scholar
  9. Cervero R, Kockelmann K (1997) Travel demand and the 3Ds: density, diversity, and design. Transp Res D 2(3):199–219Google Scholar
  10. Chen M, Bärwolff G, Schwandt H (2009) Automation model with variable cell size for the simulation of pedestrian flow. In: Proceedings of the 7th international conference on information and management sciences 2008, Urumtschi, pp 727–736Google Scholar
  11. Clifton KJ, Livi AD (2004) Gender differences in walking behavior, attitudes about walking, and perceptions of the environment in three Maryland communities. In: Transportation research board conference proceedings 35, conference on research on women’s issues in transportation, Chicago, pp 79–88Google Scholar
  12. ESRI (2012) ArcGIS Desktop: Release 10.2. Environmental Systems Research Institute, Redlands, CAGoogle Scholar
  13. Gartner G, Huang H, Millonig A, Schmidt M, Ortag F (2011) Human-centered mobile pedestrian navigation systems. Mitteilungen der Österreichischen Geographischen Gesellschaft 153:237–250CrossRefGoogle Scholar
  14. Gibson J (1979) The ecological approach to visual perception. Houghton Mifflin Company, BostonGoogle Scholar
  15. Handy SL (2004) Critical assessment of the literature on the relationship among transportation, land use, and physical activity. Report, Transportation Research Board, Washington, DCGoogle Scholar
  16. Handy S (2005) Does the built environment influence physical activity? TRB special report 282. Transportation Research Board, Washington DCGoogle Scholar
  17. Hidalgo CM, Berto R, Galindo MP, Getrevi A (2006) Identifying attractive and unattractive urban places: categories, restorativeness and aesthetic attributes. Medio Ambiente y Comportamiento Humano 7(2):115–133Google Scholar
  18. Holone H, Misund SE, Ramachandran B (2007) Users are doing it for themselves: pedestrian navigation with user generated content. In: NGMAST 2007, New York, USAGoogle Scholar
  19. Huang H, Klettner S, Schmidt M, Gartner G, Leitinger S, Wagner A, Steinmann R (2014) AffectRoute—considering people’s affective responses to environments for enhancing route-planning services. IJGIS 28(12):2456–2473Google Scholar
  20. Jonietz D, Timpf S (2013) An affordance-based simulation framework for assessing spatial suitability. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, Nierstrasz O, Pandu Rangan C, Steffen B, Sudan M, Terzopoulos D, Tygar D, Vardi MY, Weikum G, Tenbrink T, Stell J, Galton A, Wood Z (eds) Lecture notes in computer science. Springer International Publishing, Cham, pp 169–184Google Scholar
  21. Jonietz D, Schuster W, Timpf S (2013) Modelling the suitability of urban networks for pedestrians: an affordance-based framework. In: Vandenbroucke D, Bucher B, Crompvoets J (eds) Geographic information science at the heart of Europe. Lecture notes in geoinformation and cartography. Springer, Heidelberg, pp 369–382CrossRefGoogle Scholar
  22. Jonietz D (2016) From space to place—A computational model of functional place. Doctoral thesis, University of Augsburg, GermanyGoogle Scholar
  23. Kaufmann C, Papaioannou P, Blaszczyk M, Marques Almeida D de (2010) Preconditions and how they are perceived. In: COST 358 Pedestrian quality needs final report, Part B, pp 15–48Google Scholar
  24. Kemke C (2001) About the ontology of actions. Technical Report MCCS-01-328, Computing Research Laboratory, New Mexico State UniversityGoogle Scholar
  25. Krisp J, Keler A (2015) Car navigation: computing routest hat avoid complicated crossings. IJGIS 29(11):1988–2000Google Scholar
  26. Leontiev AN (1978) Activity, consciousness, and personality. Prentice-Hall, New JerseyGoogle Scholar
  27. Lindal P, Hartig T (2013) Architectural variation, building height, and the restorative quality of urban residential streetscapes. J Environ Psychol 33:26–36CrossRefGoogle Scholar
  28. Lynch K, Rivkin M (1959) A walk around the block. Landscape 8:24–34Google Scholar
  29. Mautz R (2009) Overview of current indoor positioning systems. Geodezija ir Kartografija 35(1):18–22CrossRefGoogle Scholar
  30. McCormack GR, Shiell A (2011) In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. Int J Behav Nutr Phys Act 8(125):1–11Google Scholar
  31. Millonig A (2006) Routennetze für movbile Fußgänger-Navigationsanwendungen:ein neuer Ansatz für die Optimierung auf Basis von quantitativen Bewegungsdaten. In: CORP 2006, Vienna, AustriaGoogle Scholar
  32. Owen N, Humpel N, Leslie E, Baumann A, Sallis JF (2004) Understanding environmental influences on walking. review and research agenda. Am J Prev Med 27(1):67–76CrossRefGoogle Scholar
  33. Park S (2008) Defining, measuring, and evaluating path walkability, and testing its impacts on transit users’ mode choice and walking distance to the station. Dissertation, University of California, BerkeleyGoogle Scholar
  34. Randell C, Djiallis C, Muller H (2003) Personal position measurement using dead reckoning. In: Proceedings of the seventh international symposium on wearable computers. IEEE Computer Society, pp 166–173Google Scholar
  35. Saelens BE, Handy S (2008) Built environment correlates of walking: a review. Med Sci Sports Exerc 40(7 suppl):550–566CrossRefGoogle Scholar
  36. Saelens BE, Sallis JF, Frank LD (2003) Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Ann Behav Med 25(2):80–91CrossRefGoogle Scholar
  37. Sallis JF, Floyd MF, Rodriguez DA, Saelens BE (2012) Role of built environments in physical activity, obesity, and cardiovascular disease. Circulation 125(5):729–737CrossRefGoogle Scholar
  38. Samarasekara GN, Fukahori K, Kubota Y (2011) Environmental correlates that provide walkability cues for tourists: an analysis based on walking decision narrations. Environ Behav 43(4):501–524CrossRefGoogle Scholar
  39. Sanches SP, Ferreira MAG (2007) How the elderly perceive the quality of sidewalks. In: ITE 2000 annual meeting and exhibit, NashvilleGoogle Scholar
  40. Sugiyama T, Neuhaus M, Cole R, Giles-Corti B, Owen N (2012) Destination and route attributes associated with adults’ walking: a review. Med Sci Sports Exerc 44(7):1275–1286CrossRefGoogle Scholar
  41. van Holle V, Deforche B, van Cauwenberg J, Goubert L, Maes L, van de Weghe N, Bourdeaudhuij I de (2012) Relationship between the physical environment and different domains of physical activity in European adults: a systematic review. BMC Public Health 12Google Scholar
  42. Völkel T, Weber G (2008) RouteCheckr: personalized multicriteria routing for mobility-impaired pedestrians. In: ASSETS’08, Halifax, CanadaGoogle Scholar
  43. Vukmirovic M (2010) Functional abilities of humans and identification of specific groups of pedestrians. In: Walk 21 XI international conference on walking and liveable communities, The Hague, Netherlands, 16–19 November 2010Google Scholar
  44. Walter V, Kada M, Chen H (2006) Shortest path analyses in raster maps for pedestrian navigation in location based systems. In: Commision IV, WG IV/6Google Scholar
  45. Warren WH (1995) Constructing an econiche. In: Flach J, Hancock P, Caird J, Vicente K (eds) The ecology of human-machine systems. Erlbaum, Hilldsale, pp 210–237Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of GeographyUniversity of HeidelbergHeidelbergGermany

Personalised recommendations