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access icon free Geo-referencing naturalistic driving data using a novel method based on vehicle speed

© The Institution of Engineering and Technology
Received 30/09/2012, Accepted 26/12/2012, Revised 12/12/2012, Published

Naturalistic driving is an experimentation model that allows us to recognise the driving modes observing the driver's behaviour at the wheel of a set of people in natural conditions during long periods of observation. This research methodology aims at increasing the representativeness of the data collected in opposition to data stemming from highly controlled laboratory experiments. However, naturalistic driving research designs produce large volumes of data that are difficult to handle. Thus, it is very important to work with suitable methods for representing and interpreting data, allowing us to observe the variability of the results. The aim of this study is to implement a new methodology adapted to the particularities of the naturalistic method that allows us to retrieve the positioning information through a georeferencing process of the available data. This method is the first step (preprocessing) to achieve a more clear and intuitive representation (cartographic representation) using Geographic Information Systems (GIS).

Inspec keywords: geographic information systems; road traffic; road safety; data handling; traffic engineering computing; behavioural sciences computing

Other keywords: naturalistic method; positioning information retrieval; geo-referencing naturalistic driving data; data representation; vehicle speed; data georeferencing process; driver behaviour; geographic information system; driving mode; data interpretation

Subjects: Data handling techniques; Traffic engineering computing; Social and behavioural sciences computing; Geography and cartography computing

References

    1. 1)
      • 13. Yang, S., Peng, H.: ‘Development of an errorable car-following driver model’, Veh. Syst. Dyn., 2009, 48, (6), pp. 751–773 (doi: 10.1080/00423110903128524).
    2. 2)
      • 8. Regan, M.A., Williamson, A., Grzebieta, R., Tao, L.: ‘Naturalistic driving studies: literature review and planning for the Australian naturalistic driving study’. Proc. Int. Conf. ACRS Road Safety, Sydney, Australia, August 2012.
    3. 3)
      • 26. Mekik, C., Arslanoglu, M.: ‘Investigation on accuracies of real time kinematic GPS for GIS applications’, Remote Sens., 2009, 1, (1), pp. 22–35 (doi: 10.3390/rs1010022).
    4. 4)
      • 43. Yang, Z.Z., Jiang, Y.L., Chen, Y.: ‘Developing a road traffic control system with GIS’, Proc. ICE-Transp., 2009, 162, (4), pp. 189–194 (doi: 10.1680/tran.2009.162.4.189).
    5. 5)
      • 47. Hughes, C., Glavin, M., Jones, E., Denny, P.: ‘Wide-angle camera technology for automotive applications: a review’, IET Intell. Transp. Syst., 2009, 3, (1), pp. 19–31 (doi: 10.1049/iet-its:20080017).
    6. 6)
      • 20. Chisholm, S., Caird, J.K., Teteris, E., Lockhart, J., Smiley, A.: ‘Novice and experienced driver performance with cell phones’. Proc. Int. Conf. 50th Annual Human Factors and Ergonomics Meeting, California, US, October 2006, pp. 2354–2358.
    7. 7)
      • 51. van Nes, N., Walta, L., Morsink, P.: ‘Enhancing speed management by in-car speed assistance systems’, IET Intell. Transp. Syst., 2010, 4, (1), pp. 3–11 (doi: 10.1049/iet-its.2009.0038).
    8. 8)
      • 17. Lee, K., Peng, H.: ‘Evaluation of automotive forward collision warning and collision avoidance algorithms’, Veh. Syst. Dyn., 2005, 43, (10), pp. 735–751 (doi: 10.1080/00423110412331282850).
    9. 9)
      • 45. Ogunbodede, E.F.: ‘Assessment of traffic congestions in Akure (Nigeria) using GIS approach: lessons and challenges for urban sustenance’. Proc. Int. Conf. Whole Life Urban Sustainability and its Assessment, Glasglow, Scotland, June 2007.
    10. 10)
      • 36. Barbeau, S.J., Winters, P.L., Georggi, N.L., Labrador, M.A., Perez, R.: ‘Travel assistance device: utilising global positioning system-enabled mobile phones to aid transit riders with special needs’, IET Intell. Transp. Syst., 2010, 4, (1), pp. 12–23 (doi: 10.1049/iet-its.2009.0028).
    11. 11)
      • 35. Cevallos, F., Yuan, Q., Wang, X., Gan, A.: ‘Using personal global positioning system devices in paratransit’, IET Intell. Transp. Syst., 2009, 3, (3), pp. 282–288 (doi: 10.1049/iet-its.2008.0078).
    12. 12)
      • 33. Souza, E., Galera, J.F., Pagamisse, A.: ‘GPS satellite kinematic relative positioning: analyzing and improving the functional mathematical model using wavelets’, Math. Probl. Engng., 2009, 2009, pp. 1–19 (doi: 10.1155/2009/934524).
    13. 13)
      • 11. Young, K.L., Bayly, M., Lenné, M.G.: ‘Cross-regional in-vehicle information system design: the preferences and comprehension of Australian, US and Chinese drivers’, IET Intell. Transp. Syst., 2012, 6, (1), pp. 36–43 (doi: 10.1049/iet-its.2010.0133).
    14. 14)
      • 3. Pañeda González, N.: ‘Recognizing safety-critical events from naturalistic driving data’. PhD Master's Thesis, Chalmers University of Technology, Göteborg, Sweden, 2011.
    15. 15)
      • 15. Green, P.E., Wada, T., Oberholtzer, J., Green, P.A., Schweitzer, J., Eoh, H.: ‘How do distracted and normal driving differ: an analysis of the ACAS naturalistic driving data’. Transportation Research Institute (UMTRI), University of Michigan, MI, 2007.
    16. 16)
      • 27. Julg, Th.: ‘GNSS accuracies under multipath propagation conditions’. Proc. Int. IEEE Symp. Spread Spectrum Techniques and Applications, Sun City, South Africa, September 1998, pp. 709–713.
    17. 17)
      • 9. McLaughlin, S.B., Hankey, J.M., Dingus, T.A.: ‘A method for evaluating avoidance systems using naturalistic driving data’, Accident Anal. Prev., 2008, 40, pp. 8–16 (doi: 10.1016/j.aap.2007.03.016).
    18. 18)
      • 22. http://www.prologue-eu.eu, accessed February 2012.
    19. 19)
      • 7. Backer-Grøndahl, A., Phillips, R., Sagberg, F., Touliou, K., Gatscha, M.: ‘Topics and applications of previous and current naturalistic driving studies’. PROmoting real Life Observations for Gaining Understanding of road user behaviour in Europe, 2009.
    20. 20)
      • 32. van Diggelen, F.: ‘A-GPS: assisted GPS, GNSS, and SBAS’ (Artech House Publishers, 2009).
    21. 21)
      • 30. Groves, P.D.: ‘Principles of GNSS, inertial, and multi-sensor integrated navigation systems (GNSS technology and applications)’ (Artech House Publishers, 2008).
    22. 22)
      • 16. Thomas, T., van Berkum, E.C.: ‘Detection of incidents and events in urban networks’, IET Intell. Transp. Syst., 2009, 3, (2), pp. 198–205 (doi: 10.1049/iet-its:20080045).
    23. 23)
      • 5. Stutts, J., Feaganes, J., Reinfurt, D., et al: ‘Driver's exposure to distractions in their natural driving environment’, Accident Anal. Prev., 2005, 37, pp. 1093–1101 (doi: 10.1016/j.aap.2005.06.007).
    24. 24)
      • 2. Higgs, B.J.: ‘Application of naturalistic truck driving data to analyze and improve car following models’. PhD Master's Thesis, Virginia Polytechnic Institute and State University, 2011.
    25. 25)
      • 38. Goldsberry, K.: ‘GeoVisualization of automobile congestion. GeoVisualization of dynamics, movement and change’. Int. Proc. Conf. AGILE Workshop, Girona, Spain, May 2008.
    26. 26)
      • 24. Burrough, P.A., McDonnell, R.A.: ‘Principles of geographical information systems’ (Oxford University Press, 1998).
    27. 27)
      • 12. Jie, L., van Zuylen, H.J., Chen, Y.S., Lu, R.: ‘Comparison of driver behaviour and saturation flow in China and the Netherlands’, IET Intell. Transp. Syst., 2012, 6, (3), pp. 318–327 (doi: 10.1049/iet-its.2010.0203).
    28. 28)
      • 25. Balsa-Barreiro, J.: ‘Análise para a implementación dun sistema de información xeográfica (SIX; GIS) para a xestión de servizos en calquera nivel da Administración. Particularización e aplicación de mellora para o caso da Dirección Xeral de Turismo-Turgalicia (Consellería de Innovación e Industria)’ (Escola Galega de Administración Pública, Monografía n°9, 2009).
    29. 29)
      • 19. Strayer, D.L., Drews, F.A.: ‘Profiles in driver distraction: effects of cell phone conversation on younger and older drivers’, Hum. Factors, 2004, 46, (4), pp. 640–649 (doi: 10.1518/hfes.46.4.640.56806).
    30. 30)
      • 1. Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., Ramsey, D.J.: ‘The impact of driver inattention on near-crash/crash risk: an analysis using the 100-car naturalistic driving study data’. Virginia Tech Transportation Institute, Blacksburg, VA, 2006.
    31. 31)
      • 46. Gordon, T., Green, L., Kostyniuk, L., et al: ‘A multivariate analysis of crash and naturalistic event data in relation to highway factors using the GIS framework’. Fourth SHRP2 Safety Research Symposium, Washington DC, 2009.
    32. 32)
      • 48. Jiménez, F., Aparicio, F., Páez, J.: ‘Evaluation of in-vehicle dynamic speed assistance in Spain: algorithm and driver behaviour’, IET Intell. Transp. Syst., 2008, 2, (2), pp. 132–142 (doi: 10.1049/iet-its:20070019).
    33. 33)
      • 50. van Nes, N., Houtenbos, M., van Schagen, I.: ‘Improving speed behaviour: the potential of in-car speed assistance and speed limit credibility’, IET Intell. Transp. Syst., 2008, 2, (4), pp. 323–330 (doi: 10.1049/iet-its:20080036).
    34. 34)
      • 41. Slingsby, A., Wood, J., Dykes, J.: ‘Treemap cartography for showing spatial and temporal traffic patterns’, J. Maps, 2010, 2010, pp. 135–146 (doi: 10.4113/jom.2010.1071).
    35. 35)
      • 4. Dingus, T.A., Klauer, S.G., Neale, V.L., et al: ‘The 100-car naturalistic driving study. Phase II – results of the 100-car field experiment’. National Highway Traffic Safety Administration, Washington, DC, 2006.
    36. 36)
      • 14. Barr, L.C., Yang, D., Hanowski, R.J., Olson, R.: ‘An assessment of driver drowsiness, distraction, and performance in a naturalistic setting’. Federal Motor Carrier Safety Administration, US Department of Transportation, 2011.
    37. 37)
      • 34. Gao, Y., Wojciechowski, A.: ‘High precision kinematic positioning using single dual-frequency GPS receiver’. Proc. Int. Archives of the Photogrammetry, Remote Sensing and Spatial Information Systems, Istanbul, Turkey, July 2004, pp. 845–850.
    38. 38)
      • 37. González, P.A., Weinstein, J.S., Barbeau, S.J., et al ‘Automating mode detection for travel behaviour analysis by using global positioning systems enabled mobile phones and neural networks’, IET Intell. Transp. Syst., 2010, 4, (1), pp. 37–49 (doi: 10.1049/iet-its.2009.0029).
    39. 39)
      • 10. Laureshyn, A., Ardo, H., Svensson, A., Jonsson, T.: ‘Application of automated video analysis for behavioural studies: concept and experience’, IET Intell. Transp. Syst., 2009, 3, (3), pp. 345–357 (doi: 10.1049/iet-its.2008.0077).
    40. 40)
      • 31. El-Rabbany, A.: ‘Introduction to GPS: the global positioning system’ (Artech House Publishers, 2006).
    41. 41)
      • 39. Goldsberry, K.: ‘Limitations and potentials of real-time traffic visualization for wayfinding’. Proc. Int. Conf. 22nd ICA/ACI Int. Cartographic Conf., A Coruna, Spain, July 2005.
    42. 42)
      • 40. http://gicentre.org/treemaptraffic, accessed February 2012.
    43. 43)
      • 29. Jiménez, F., Naranjo, J.E., García, F., et al.: ‘Limitations of positioning systems for developing digital maps and locating vehicles according to the specifications of future driver assistance systems’, IET Intell. Transp. Syst., 2011, 5, (1), pp. 60–69 (doi: 10.1049/iet-its.2010.0042).
    44. 44)
      • 23. Valero-Mora, P., Tontsch, A., Pareja-Montoro, I., Sánchez-García, M.: ‘Using a highly instrumented car for naturalistic driving research: a small-scale study in Spain’. PROmoting real Life Observations for Gaining Understanding of road user behaviour in Europe, 2010.
    45. 45)
      • 18. Olson, P.L., Battle, D.S., Aoiki, T.: ‘Driver eye fixations under different operating conditions’. Transportation Research Institute (UMTRI), University of Michigan, MI, 1989.
    46. 46)
      • 44. Alesheikh, A.A., Omidvari, M.: ‘Application of GIS in urban traffic noise pollution’, Int. J. Occup. Hyg., 2010, 2, (2), pp. 79–84.
    47. 47)
      • 28. Ostini, L.: ‘Analysis and quality assessment of GNSS-derived parameter time series’. PhD thesis, University of Bern, Switzerland, 2012.
    48. 48)
      • 49. Banet, A., Bellet, T.: ‘Risk awareness and criticality assessment of driving situations: a comparative study between motorcyclists and car drivers’, IET Intell. Transp. Syst., 2008, 2, (4), pp. 241–248 (doi: 10.1049/iet-its:20080037).
    49. 49)
      • 21. Cheng, B., Lin, Q., Song, T., Cui, Y., Wang, L., Kuzumaki, S.: ‘Analysis of driver brake operation in near-crash situation using naturalistic driving data’, Int. J. Autom. Engng., 2011, 2, (4), pp. 87–94.
    50. 50)
      • 42. Malgundkar, T., Rao, M., Mantha, S.S.: ‘GIS driven urban traffic analysis based on ontology’, Int. J. Manag. Inf. Technol., 2012, 4, (1), pp. 15–23.
    51. 51)
      • 6. Cody, D., Tan, S., Caird, J.K., Lees, M., Edwards, C.: ‘The naturalistic driver model: development, integration, and verification of lane change maneuver, driver emergency and impairment modules’. California PATH for advanced transit and highways. Working Paper, 2008.
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