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Proposal of geographic information systems methodology for quality control procedures of data obtained in naturalistic driving studies

Proposal of geographic information systems methodology for quality control procedures of data obtained in naturalistic driving studies

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The primary goal of naturalistic driving studies is to provide a comprehensive observation of the driver's behaviour under real-life conditions by measureing a great number of parameters at high temporal frequencies. Achieving this goal, however, is a complex endeavor that faces many challenges such as the complexity of the vehicle instrumentation during the phase of data collection, and the difficult handling of large data volumes during the phase of data analysis. These drawbacks often cause episodes of data losses. Improving the technical aspects of the collection of naturalistic data is of paramount importance to increase the return of the investment made in it. An aspect to consider is the control of the quality of data obtained. The procedures commonly applied often present a high workload and low levels of reliability in the results. For this reason, new and more efficient procedures should be implemented. This study proposes an innovative methodology for the quality control of these data through geographic information systems (GIS), which permit a fast, efficient and effective data evaluation. Relative and absolute errors can be located through cartographic representation obtained with GIS. Some graphical examples about the detection of the errors are shown.


    1. 1)
      • 20. Chisholm, S., Caird, J., Teteris, E., et al: ‘Novice and experienced driver performance with cell phones’. Proc. of Int. Conf. on 50th Annual Human Factors and Ergonomics Meeting, California, USA, October 2006, pp. 23542358.
    2. 2)
      • 24. Pilgerstorfer, M., Runda, K., Brandstätter, C., et al: ‘Small scale naturalistic driving pilot’. (Deliverable 6.3 of the EC FP7 project DaCoTA, 2011).
    3. 3)
      • 46. United States Geological Survey (USGS): ‘Fundamental Science Practices: Planning and Conducting Data Collection and Research’, Chapter 502.2. Available at, accessed November 2014.
    4. 4)
    5. 5)
      • 42. ArcGis Resource Center: ‘What is a shapefile?’ Available at, accessed November 2014.
    6. 6)
      • 1. UN Population Division. Available at, accessed November 2014.
    7. 7)
      • 39. Balsa-Barreiro, J., Pareja-Montoro, I., Tontsch, A., et al: ‘Preprocessing of data for recovery of positioning data in naturalistic driving trial’. Proc. of European Conf. on Human Centred Design for Intelligent Transport Systems, Valencia, Spain, June 2012, pp. 235245.
    8. 8)
      • 2. Sassen, S.: ‘Cities in a world economy’ (SAGE Publications, 1994, 2011, 4th edn.).
    9. 9)
      • 5. World Health Organization (WHO): ‘Global status report on road safety. Time for action’ (Department of Violence and Injury Prevention and Disability (VIP), World Health Organization, 2009).
    10. 10)
      • 23. Valero-Mora, P., Tontsch, A., Pareja-Montoro, I., et al: ‘Using a highly instrumented car for naturalistic driving research: a small-scale study in Spain’ (PROLOGUE Deliverable D3.5, Institute on Traffic and Road Safety, Valencia, Spain, 2010).
    11. 11)
      • 14. Barr, L., Yang, D., Hanowski, R., et al: ‘An assessment of driver drowsiness, distraction, and performance in a naturalistic setting’ (Federal Motor Carrier Safety Administration, US Department of Transportation, 2011).
    12. 12)
      • 25. Sagberg, F., Eenink, R., Hoedemaeker, M., et al: ‘Recommendations for a large-scale European naturalistic driving observation study’ (PROLOGUE Deliverable D4.1, TØI Institute of Transport Economics, Oslo, Norway, 2011).
    13. 13)
      • 37. Backer-Grøndahl, A., Phillips, R., Sagberg, F., et al: ‘Topics and applications of previous and current naturalistic driving studies’ (PROLOGUE Deliverable D1.1, TØI Institute of Transport Economics, Oslo, Norway, 2009).
    14. 14)
      • 36. Balsa-Barreiro, J.: ‘Análise para a implementación dun SIX co fin da xestión de servizos en calquera nivel da Administración. Particularización e aplicacións de mellora para o caso da Dirección Xeral de Turismo-Turgalicia (Consellería de Innovación e Industria)’ (Escola Galega de Administración Pública, Monografía 09, 2008, 1st edn.).
    15. 15)
      • 8. Chapman, S., Haddad, S., Sindhusake, D.: ‘Do work-place smoking bans cause smokers to smoke ‘harder’? Results from a naturalistic observational study’, Addiction, 1997, 92, (5), pp. 607610.
    16. 16)
      • 51. Ericsson, E.: ‘Driving pattern in urban areas-descriptive analysis and initial prediction model’ (Lund University, Department of Technology and Society, 2000).
    17. 17)
    18. 18)
      • 28. Chapman, A.: ‘Principles and methods of data cleaning. Primary species and species occurrence data, version 1.0’. Report for the Global Biodiversity Information Facility, Copenhagen, 2005. Available at, accessed November 2014.
    19. 19)
      • 29. Maletic, J., Marcus, A.: ‘Data cleansing: beyond integrity checking’. Proc. of Conf. on Information Quality IQ, Boston, MA, USA, October 2012, pp. 200209.
    20. 20)
      • 38. PROLOGUE. Available at, accessed November 2014.
    21. 21)
      • 7. Community Road Accident Database (CARE). Available at, accessed November 2014.
    22. 22)
    23. 23)
      • 45. United States Geological Survey (USGS): ‘Data Management: Manage Quality’. Available at, accessed November 2014.
    24. 24)
      • 35. Strategic Highway Research Program (SHRP-2): ‘SHRP 2 S06. Technical coordination and quality control’. Available at, accessed November 2014.
    25. 25)
      • 16. Klauer, S., Dingus, T., Neale, V., et al: ‘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).
    26. 26)
    27. 27)
    28. 28)
    29. 29)
      • 44. Intergovernmental Oceanographic Commission (IOC): ‘Manual of quality control procedures for validation of oceanographic data’ (Commission of the European Communities, Manual and Guides 26, SC-93/WS-19, 1993).
    30. 30)
      • 41. Balsa-Barreiro, J.: ‘Aplicación de sistemas GNSS y SIG a infraestructuras de transporte. Estudio sobre conducción naturalista. PhD thesis, University of A Corunna, Spain – Politecnico di Torino, Italy, 2015.
    31. 31)
    32. 32)
    33. 33)
    34. 34)
      • 22. Dingus, T., Klauer, S., Neale, V., 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).
    35. 35)
    36. 36)
      • 6. European Commision: ‘Road safety vademecum. Road safety trends, statistics and challenges in the EU 2011–2012’ (European Commission DG for Mobility and Transport, 2013), 17pp..
    37. 37)
    38. 38)
      • 31. Saylam, K.: ‘Quality assurance of LiDAR system-mission planning’. Proc. of Int. ASPRS 2009 Annual Conf., Baltimore, MD, USA, March 2009, 13pp..
    39. 39)
      • 50. Zabic, M.: ‘GNSS-based road charging systems. Assessment of vehicle location determination’. PhD thesis, Technical University of Denmark, 2011.
    40. 40)
      • 49. Redman, T.: ‘Data quality: the field guide’ (Digital Press, 2001, 1st edn.).
    41. 41)
    42. 42)
      • 34. Welsh, R., Reed, S., Talbot, R., et al: ‘Data collection, analysis methods and equipment for naturalistic studies and requirements for the different application areas’ (PROLOGUE Deliverable D2.1, Loughborough University, Loughborough, UK, 2010).
    43. 43)
    44. 44)
      • 3. World Bank: ‘Motorization, demand & city development’. Available at,,contentMDK:20249477~menuPK:610224~pagePK:148956~piPK:216618~theSitePK:341449,00.html, accessed November 2014.
    45. 45)
      • 21. Strategic Highway Research Program (SHRP-2): ‘Analyzing driver behavior using data from the SHRP 2 naturalistic driving study’ (Transportation Research Board of the National Academies, May2013), 4pp. Available at, accessed November 2014.
    46. 46)
      • 30. webpage. Availabe at, accessed November 2014.
    47. 47)
      • 12. Higgs, B.: ‘Application of naturalistic truck driving data to analyze and improve car following models’. PhD master's thesis, Virginia Polytechnic Institute and State University, 2011.
    48. 48)
    49. 49)
    50. 50)
    51. 51)
      • 27. Chapman, A.: ‘Principles of data quality, version 1.0’. Report for the Global Biodiversity Information Facility, Copenhagen, 2005. Available at, accessed November 2014.

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