Where and when do drivers speed? A feasibility study of using probe vehicle data for speeding analysis

Authors

  • Jiří Ambros CDV – Transport Research Centre Author https://orcid.org/0000-0003-2707-6243
  • Jan Elgner CDV – Transport Research Centre Author
  • Richard Turek CDV – Transport Research Centre Author
  • Veronika Valentova CDV – Transport Research Centre Author

DOI:

https://doi.org/10.5604/01.3001.0014.1747

Keywords:

vehicle data, speed, traffic safety

Abstract

Speed is a critical transportation concept – it is one of the most important factors that road users consider in relation to route convenience and efficiency; at the same time speed has been recognized as the most influential risk factor. To improve speeding analyses, an emerging data source – probe vehicle data (also known as floating car data), may be used. This data enables obtaining information on vehicle speeds, without being limited in time and space. To prove the feasibility of using this data, a study was conducted on a sample of Prague expressway and collector roads. Firstly, probe data sample validity was checked through comparison to a traditional speed measurement technique – average speed control. Secondly, descriptive analysis of speeding was performed, focusing on speeding differences across homogeneous road segments in individual hour intervals. Thirdly, statistical models were also developed to explain which road parameters contribute to speeding. Analysis utilized cross-section and geometry parameters, which may potentially be related to speed choice and driving speed and speeding. In general, the applied concept proved as feasible: particularly night time was found more prone to speeding, and the rates were significantly different between segments. Statistical models indicated the statistically significant influence on speeding: lower speed limit, lower number of lanes, absence of roadside activities, or presence of horizontal curves. Information on these factors may be generalized and used for planning adequate speeding countermeasures. Final discussion also identified and described several challenges for future research, including free-flow speed estimation uncertainty, quality of speed-safety models, and potential multicollinearity of explanatory variables.

References

Aarts, L., van Nes, N., Donkers, E., van der Heijden, D. (2010). Towards safe speeds and credible speed limits. 4th International Symposium on Highway Geometric Design, Valencia, Spain.

Ambros, J., Kyselý, M. (2016). Free-flow vs car-following speeds: Does the difference matter? Advances in Transportation Studies, 40, 17–26.

Ambros, J., Valentová, V., Gogolín, O., Andrášik, R., Kubeček, J., Bíl, M. (2017). Improving the Self-Explaining Performance of Czech National Roads. Transportation Research Record, 2635, 62–70.

Bar-Gera, H., Schechtman, E., Musicant, O. (2017). Evaluating the effect of enforcement on speed distributions using probe vehicle data. Transportation Research Part F, 46, 271–283.

Bekhor, S., Lotan, T., Gitelman, V., Morik, S. (2013). Free-flow travel speed analysis and monitoring at the national level using global positioning system measurements. Journal of Transportation Engineering, 139, 1235–1243.

Bessler, S., Paulin, T. (2013). Literature study on the state of the art of probe data systems in Europe. FTW Telecommunications Research Center, Vienna, Austria.

Boodlal, L., Donnell, E. T., Porter, R. J., Garimella, D., Le, T., Croshaw, K. et al. (2015). Factors Influencing Operating Speeds and Safety on Rural and Suburban Roads. Re-port FHWA-HRT-15-030. Federal Highway Administration, Washington, DC, USA.

Carlsson, A. (2009). Evaluation of 2+1-roads with cable barrier – Final report. Report 636A. Swedish National Road and Transport Research Institute, Linköping, Sweden.

Decina, L. E., Thomas, L., Srinivasan, R., Staplin, L. (2007). Automated Enforcement: A Compendium of Worldwide Evaluations of Results. Report DOT HS 810 763. National Highway Traffic Safety Administration, Washington, DC, USA.

Diependaele, K., Riguelle, F., Temmerman, P. (2016). Speed behavior indicators based on floating car data: Results of a pilot study in Belgium. Transportation Research Procedia, 14, 2074–2082.

Elvik, R. (2013). A re-parameterisation of the Power Model of the relationship between the speed of traffic and the number of accidents and accident victims. Accident Analysis and Prevention, 50, 854–860.

Elvik, R. (2018). How can the notion of optimal speed limits best be applied in urban areas? Transport Policy, 68, 170–177.

Familar, R., Greaves, S., Ellison, A. (2011). Analysis of Speeding Behavior: Multilevel Modeling Approach. Transportation Research Record, 2237, 67–77.

Field, A. (2018). Discovering statistics using IBM SPSS Statistics, 5th ed. SAGE, Thousand Oaks, CA, USA.

Fitzpatrick, C. D., McKinnon, I. A., Tainter, F. T., Knodler Jr., M. A. (2016). The application of continuous speed data for setting rational speed limits and improving roadway safety. Safety Science, 85, 171–178.

Fridstrøm, L. (2015). Disaggregate Accident Frequency and Risk Modelling: A Rough Guide. Report 1403/2015. Institute of Transport Economics, Oslo, Norway.

Gaca, S., Pogodzińska, S. (2017). Speed management as a measure to improve road safety on Polish regional roads. Archives of Transport, 43(3), 29–42.

Galin, D. (1981). Speeds on two-lane rural roads – a multiple regression analysis. Traffic Engineering and Control, 22, 453–460.

Gargoum, S. A., El-Basyouny, K., Kim, A. (2016). Towards setting credible speed limits: Identifying factors that affect driver compliance on urban roads. Accident Analysis and Prevention, 95, 138–148.

Giles, M. J. (2004). Driver speed compliance in Western Australia: a multivariate analysis. Transport Policy, 11, 227–235.

Gitelman, V., Doveh, E., Bekhor, S. (2018). The relationship between travel speeds, infra-structure characteristics, and crashes on two-lane highways. Journal of Transportation Safety and Security, 10, 545–571.

Goldenbeld, C., van Schagen, I. (2007). The credibility of speed limits on 80 km/h rural roads: the effects of road and person(ality) characteristics. Accident Analysis and Prevention, 39, 1121–1130.

Hauer, E. (2004). Statistical Road Safety Modeling. Transportation Research Record, 1897, 81–87.

Hrubeš, P., Blümelová, J. (2015). Comparative Analysis for Floating Car and Loop Detectors Data. 22nd ITS World Congress, Bordeaux, France.

Hydén, C. (2018). Speed in a high-speed society. ICoRSI International Symposium, Paris, France.

INRIX (2019). I-95 VPP Data Summary Vali-dation [online]. Available at: http://in-rix.com/case-studies/inrix-i-95-vpp-data-summary-validation-case-study/ [Accessed 1 March 2019].

Jun, J., Ogle, J., Guensler, R. (2007). Relation-ships between Crash Involvement and Temporal-Spatial Driving Behavior Activity Patterns: Use of Data for Vehicles with Global Positioning Systems. Transportation Re-search Record, 2019, 246–255.

Jurewicz, C., Espada, I., Makwasha, T., Han, C., Alawi, H., Ambros, J. (2017). Validation and applicability of floating car speed data for road safety. 2017 Australasian Road Safety Conference, Perth, Australia.

Jurewicz, C., Espada, I., Makwasha, T., Han, C., Alawi, H., Ambros, J. (2018). Use of connected vehicle data for speed management in road safety. 28th ARRB International Conference, Brisbane, Australia.

Kamla, J., Parry, T., Dawson, A. (2019). Analysing truck harsh braking incidents to study roundabout accident risk. Accident Analysis and Prevention, 122, 365–377.

Mannering, F. (2018). Cross-Sectional Modelling. In: Lord, D., Washington, S., eds., Safe Mobility: Challenges, Methodology and Solutions, Emerald, Bingley, UK, pp. 257–277.

NHTSA (2018). Traffic Safety Facts – Speeding (2016 Data) [online]. Available at: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812480 [Accessed 1 March 2019].

Nilsson, G. (2004). Traffic Safety Dimensions and the Power Model to Describe the Effect of Speed on Safety. Bulletin 221. Lund University, Lund, Sweden.

OECD/ITF (2018). Speed and Crash Risk. OECD/ITF, Paris, France.

Police of the Czech Republic (2018). Statistika nehodovosti [online]. Available at: http://www.policie.cz/clanek/statistika-nehodovosti-900835.aspx [Accessed 1 March 2019].

Porter, R. J., Donnell, E. T., Mason, J. M. (2012). Geometric Design, Speed, and Safety. Transporation Research Record, 2309, 39–47.

Remias, S. M., Brennan Jr., T. M. (2018). Enhancing Traffic Enforcement Strategies Using Speed Data. 97th Transportation Research Board Annual Meeting, Washington, DC, USA.

Richard, C., Campbell, J. L., Brown, J. L., Lichty, M. G., Chrysler, S. T., Atkins, R. (2013). Investigating Speeding Behavior with Naturalistic Approaches: Methodological Les-sons Learned. Transportation Research Record, 2365, 58–65.

RSA (2018). RSA observational study 2016 – Free Speed Summary [online]. Available at: http://www.rsa.ie/en/RSA/Road-Safety/RSA-Statistics/Surveys-Consultations/Speed/ [Accessed 1 March 2019].

Saponara, S. (2018). Sensing and Connection Systems for Assisted and Autonomous Driving and Unmanned Vehicles. Sensors, 18(7), 1999.

Smith, B. L., Zhang, H., Fontaine, M., Green, M. (2003). Cell-phone probes as an ATMS tool. Report STL-2003-01. Smart Travel Laboratory, Charlottesville, VA, USA.

Soole, D. W., Watson, B. C., Fleiter, J. J. (2013). Effects of average speed enforcement on speed compliance and crashes: A review of the literature. Accident Analysis and Prevention, 54, 46–56.

Tay, R., Churchill, A. (2007). Effect of Different Median Barriers on Traffic Speed. Canadian Journal of Transportation, 1, 56–66.

Temmerman, P. (2016). Speed(ing) in built-up areas: Results of the BRSI behavioural survey speed in built-up areas in 2015. Report 2016-R-02-SEN. Belgian Road Safety Institute, Brussels, Belgium.

TRB (2011). Modeling Operating Speed: Syn-thesis Report. Transportation Research Circular E-C151. Transportation Research Board (TRB), Washington, DC, USA.

TSK (2017). Intenzity dopravy [online]. Available at: https://www.tsk-praha.cz/wps/portal/root/dopravni-in-zenyrstvi/intenzity-dopravy [Accessed 1 March 2019].

University of Toronto (2019). Crosstabulation with Nominal Variables [online]. Available at: http://groups.chass.utoronto.ca/pol242/Labs/LM-3A/LM-3A_content.htm [Accessed 1 March 2019].

Wilson, C., Willis, C., Hendrikz, J. K., Le Brocque, R., Bellamy, N. (2012). Speed cam-eras for the prevention of road traffic injuries and deaths. Cochrane Review CD004607. John Wiley & Sons, Hoboken, NJ, USA

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Published

2020-03-31

Issue

Section

Original articles

How to Cite

Ambros, J., Elgner, J., Turek, R., & Valentova, V. (2020). Where and when do drivers speed? A feasibility study of using probe vehicle data for speeding analysis. Archives of Transport, 53(1), 103-113. https://doi.org/10.5604/01.3001.0014.1747

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