Travel-Mode Classification for Optimizing Vehicular Travel Route Planning

Chapter
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

Navigating and travelling between destinations with the help of Geographic Information Systems route planning is a very common task carried out by millions of commuters daily. The route is mostly based on geocoding of the addresses given by the traveller based on static road network into digital-map positions, and thus the creation of path and directions needed to be taken. Today’s navigation data sets rarely contain information about parking lots, related to building entrances, and walking paths. This is especially relevant for large building complexes (hospitals, industrial buildings, city halls, universities). A fine-tuned route tailored for the driver requirement, e.g., park the car close-by to destination, is required in such cases to save time and frustration. The idea of this chapter is to extract this information from the navigational behaviour of users, which is accessible via an analysis of GPS traces; analysis of car commuters in relation to their point of departure and destination by analysing the walking path they took from—and to—their parked car in relation to a specific address. A classification scheme of GPS-traces is suggested, which enables to classify robustly different travel modes that compose a single GPS trace. By ascribing the classified vehicular car trace, which is accompanied by a walking path to/from the car, to a specific address, it is made feasible to extract the required ascribed data: parking places corresponding to that address. This additional data can later be added to the road network navigation maps used by the route planning scheme to enable the construction of a more fine-tuned optimal and reliable route that will prevent subsequent detours.

Keywords

Data mining Classification GPS Route planner Optimization 

References

  1. Axhausen KW, Schönfelder S, Wolf J, Oliveria M, Samaga U (2003) 80 Weeks of GPS-traces: approaches to enritching the trip information. Arbeitsbericht Verkehrs- und Raumplanung, 178, Instut für Verkehrsplanung und Transportsysteme, ETH Zürich, ZürichGoogle Scholar
  2. Biagioni J, Gerlich T, Merrifield T, Eriksson J (2011) Easytracker: automatic transit tracking, mapping, and arrival time prediction using smartphones. In SenSys, pp 68–81. ACMGoogle Scholar
  3. Bohte W, Maat K (2009) Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: a large-scale application in the Netherlands, transportation research part C. Emerg. technol. 17(3):285–297CrossRefGoogle Scholar
  4. Chung E, Shalaby A (2007) A trip reconstruction tool for GPS-based personal travel surveys. Transp Planning Technol 28(5):381–401CrossRefGoogle Scholar
  5. Delling D, Sanders P, Schultes D, Wagner D (2009) Engineering route planning algorithms. In: Algorithmics of large and complex networks. Lecture Notes in Computer Science, vol 5515. Springer, Berlin, pp 117–139. Google Scholar
  6. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numer Math 1:269–271CrossRefGoogle Scholar
  7. Hsu C, Chang C, Lin C (2003) A practical guide to support vector classification. Technical report, Department of computer science and information engineering, National Taiwan UniversityGoogle Scholar
  8. Jariyasunant J, Work DB, Kerkez B, Sengupta R, Bayen AM, Glaser S (2010) Mobile transit trip planning with real-time data. Transportation Research Board Annual Meeting, p 17Google Scholar
  9. Li YT, Huang B, Lee DH (2011) Multimodal, multicriteria dynamic route choice: a GIS-microscopic traffic simulation approach. Annals of GIS 17(3):173–187CrossRefGoogle Scholar
  10. Nadi S, Delavar MR (2011) Multi-criteria, personalized route planning using quantifier-guided ordered weighted averaging operators. Int J Appl Earth Obs Geoinf 13(3):322–335CrossRefGoogle Scholar
  11. Oliveira M, Troped P, Wolf J, Mattheww C, Cromley E (2006) Mode and activity identification using GPS and accelerometer data. In: Proceedings of transportation research board 85th annual meeting, p 12Google Scholar
  12. Reddy S, Burke J, Estrin D, Hansen M, Srivastava M (2008) Determining transportation mode on mobile phones. In: 12th IEEE international symposium on wearable computers. ISWC, pp 25–28Google Scholar
  13. Schüssler N, Axhausen KW (2008) Processing GPS raw data without additional information. Working paper, p 15Google Scholar
  14. Smola AJ, Schölkopf B (1998) On a kernel–based method for pattern recognition, regression, approximation and operator inversion. Algorithmica 22:211–231CrossRefGoogle Scholar
  15. Tsui SA, Shalaby AS (2006) Enhanced system for link and mode identification for personal travel surveys based on global positioning systems. J Transp Res Board 1972:38–45CrossRefGoogle Scholar
  16. Wolf J (2006) Applications of new technologies in travel surveys: travel survey methods, Quality and future directions. Elsevier, Oxford, pp 531–544 Google Scholar
  17. Zheng Y, Liu L, Wang L, Xie X (2008) Learning transportation mode from raw GPS data for geographic applications on the web. In: Proceedings of the 17th international conference on world wide web, WWW 08, pp 247–256Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institut für Kartographie und Geoinformatik (IKG)Leibniz Universität HannoverHannoverGermany

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