Perspectives for Surrogate Safety Studies in East-European Countries

Authors

  • Rasa Ušpalytė-Vitkūnienė Dept of Roads, Vilnius Gediminas Technical University, Saulėtekio al. 11, LT−10223 Vilnius, Lithuania
  • Aliaksei Laureshyn Traffic and Roads, Dept of Technology and Society, Faculty of Engineering, LTH, Lund University, 22100 Lund, Sweden

DOI:

https://doi.org/10.3846/bjrbe.2017.19

Keywords:

East-European countries, road safety, surrogate safety measures, traffic conflicts, video analysis.

Abstract

The road safety development in East-European countries follows in general the trends of the western countries, even though there is a time lag. The fatality numbers are decreasing, though the improvements for the vulnerable road users are less impressive compared to other road user categories. The traditional road safety analysis based on accident history has many limitations related to accident under-reporting, low and random accident counts at individual sites and lack of details in the police or hospital reports. The surrogate safety methods are based on observation of other-than-accident occurrences that still have a strong relation to safety in traffic. Such methods are often more efficient (and pro-active) for safety assessments. The paper reviews the current status of the surrogate safety analysis methods, the challenges, and opportunities related to modern urban traffic conditions and the emerging technologies for more efficient data collection. The method is put in the context of the East-European countries to see how it contribute to the on-going road safety work and a better understanding of accidents risk factors necessary for producing effective safety countermeasures.

References

af Wåhlberg, A. E. 2004. The Stability of Driver Acceleration Behavior, and a Replication of Its Relation to Bus Accidents, Accident Analysis and Prevention 36(1): 83–92. https://doi.org/10.1016/S0001-4575(02)00130-6

Alhajyaseen, W. K. M. 2015. The Integration of Conflict Probability and Severity for the Safety Assessment of Intersections, Arabian Journal for Science and Engineering 40(2): 421–430. https://doi.org/10.1007/s13369-014-1553-1

Allen, B. L.; Shin, B. T.; Cooper, P. J. 1978. Analysis of Traffic Conflicts and Collisions, Transportation Research Record 667: 67−74. Alsop, J.; Langley, J. 2001. Under-Reporting of Motor Vehicle Traffic Crash Victims in New Zealand, Accident Analysis and Prevention 33(3): 353–359. https://doi.org/10.1016/S0001-4575(00)00049-X

Amoros, E.; Martin, J. L.; Laumon, B. 2006. Under-Reporting of Road Crash Casualties in France, Accident Analysis and Prevention 38(4): 627–635. https://doi.org/10.1016/j.aap.2005.11.006

Andersson, J. 2000. Image Processing for Analysis of Road User Behavior – a Trajectory Based Solution. PhD Thesis. Lund Institute of Technology, Dept of Technology and Society, Traffic Engineering. 84.

Antov, D. 1986. The Use of Traffic Conflicts Technique on Urban non Signalised Intersections, in Workshop on Traffic Conflicts and Other Intermediate Measures, 8–10 September 1986, Budapest, Hungary.

Archer, J. 2005. Indicators for Traffic Safety Assessment and Prediction and Their Application in Micro-Simulation Modeling: a Study of Urban and Suburban Intersections. PhD Thesis. Dept of Infrastructure, Royal Institute of Technology, Stockholm. 254.

Asmussen, E. 1984. International Calibration Study of Traffic Conflict Techniques, vol. 5. Springer Science and Business Media. https://doi.org/10.1007/978-3-642-82109-7

Bagdadi, O. 2013. Estimation of the Severity of Safety Critical Events, Accident Analysis and Prevention 50: 167–174. https://doi.org/10.1016/j.aap.2012.04.007

Bagdadi, O.; Várhelyi, A. 2011. Jerky Driving – an Indicator for Accident Proneness, Accident Analysis and Prevention 43(4): 1359–1363. https://doi.org/10.1016/j.aap.2011.02.009

Berntman, M.; Berntman, L.; Nilsson, S. A. 1995. Trafiksäkerhet-sproblemen i Lunds kommun: sjukhusets och polisens skadestatistik. 1988-10–1993-09. Bulletin vägbyggnad. Dept of Technology and Society, Lund University, Sweden. (in Swedish)

Campbell, K. L.; Joksch, H. C.; Green, P. E. 1996. A Bridging Analysis for Estimating the Benefits of Active Safety Technologies. The University of Michigan, Transportation Research Institute, Michigan, USA.

Davis, G. A.; Hourdos, J.; Xiong, H.; Chatterjee, I. 2011. Outline for a Causal Model of Traffic Conflicts and Crashes, Accident Analysis and Prevention 43(6): 1907–1919. https://doi.org/10.1016/j.aap.2011.05.001

Elvik, R.; Høye, A.; Vaa, T.; Sørensen, M. 2009. The Handbook of Road Safety Measures. Emerald Group Publishing Limited, Bingly, UK. https://doi.org/10.1108/9781848552517

ERSO 2016a. Traffic Safety Basic Facts 2016: Cyclists. European Road Safety Observatory.

ERSO 2016b. Traffic Safety Basic Facts 2016: Main Figures. European Road Safety Observatory.

ERSO 2016c. Traffic Safety Basic Facts 2016: Pedestrians. European Road Safety Observatory.

Fu, T.; Stipancic, J.; Zangenehpour, S.; Miranda-Moreno, L. F.; Saunier, N. 2017. Automatic Traffic Data Collection under Varying Lighting and Temperature Conditions in Multimodal Environments: Thermal versus Visible Spectrum Video-based Systems, Journal of Advanced Transportation 2017 (Article ID 5142732). https://doi.org/10.1155/2017/5142732

Gimm, K. 2014. AIM Research Intersection–Infrastructure for Research on Interacting Urban Traffic, 27th ICTCT Workshop, 16–17 October 2014, Maribor, Slovenia.

Grayson, G. B. 1984. The Malmö Study: a Calibration of Traffic Conflict Techniques. Institute for Traffic Safety Research SWOV.

Hauer, E. 1997. Observational before-after Studies in Road Safety. Pergamon. ISBN 9780080430539

Hauer, E., Gårder, P. 1986. Research into the Validity of the Traffic Conflict Technique, Accident Analysis and Prevention 18(6): 471–481. https://doi.org/10.1016/0001-4575(86)90020-5

Hayward, J. C. 1971. Near Misses as a Measure of Safety at Urban Intersections. Master Degree Thesis. Dept of Civil Engineering, The Pennsylvania State University, Pennsylvania Transportation and Traffic Safety Center, USA.

Hupfer, C. 1997. Deceleration to Safety Time (DST) – a Useful Figure to Evaluate Traffic Safety? in ICTCT Conference Proceedings of Seminar vol. 3. 5–7 November 1997, Lund, Sweden.

Hydén, C. 1987. The Development of a Method for Traffic Safety Evaluation: the Swedish Traffic Conflict Technique. PhD Thesis. Dept of Traffic Planning and Engineering, Lund University. 57 p.

Johansson, R. 2009. Vision Zero – Implementing a Policy for Traffic Safety, Safety Science 47(6): 826–831. https://doi.org/10.1016/j.ssci.2008.10.023

Laureshyn, A. 2010. Application of Automated Video Analysis to Road User Behaviour. PhD Thesis. Transport and Roads, Dept of Technology and Society, Faculty of Engineering, LTH, Lund University. 202.

Laureshyn, A.; Ardö, H.; Jonsson, T.; Svensson, Å. 2009. Application of Automated Video Analysis for Behavioural Studies: Concept and Experience, IET Intelligent Transport Systems 3(3): 345–357. https://doi.org/10.1049/iet-its.2008.0077

Laureshyn, A.; De Ceunynck, T.; Karlsson, C.; Svensson, Å.; Daniels, S. 2017. In Search of the Severity Dimension of Traffic Events: Extended Delta-V as a Traffic Conflict Indicator, Accident Analysis and Prevention 98: 46–56. https://doi.org/10.1016/j.aap.2016.09.026

Laureshyn, A.; Johnsson, C.; De Ceunynck, T.; Svensson, Å.; de Goede, M.; Saunier, N.; Włodarek, P.; van der Horst, A. R. A.; Daniels, S. 2016. Review of Current Study Methods for VRU Safety. Appendix 6 – Scoping Review: Surrogate Measures of Safety in Site-Based Road Traffic Observations. Deliverable 2.1 – Part 4. InDeV, Horizon 2020 project.

Madsen, T. K. O. 2016. RUBA – Video Analysis Software for Road User Behaviour Analysis, in 29th ICTCT Workshop, 20– 21 October 2016, Lund, Sweden.

Nygård, M. 1999. A Method for Analysing Traffic Safety with Help of Speed Profiles. Master Degree Thesis. Dept of Civil Engineering, Tampere University of Technology, Tampere, Finland.

Perkins, S. R.; Harris, J. I. 1967. Criteria for Traffic Conflict Characteristics: Signalized Intersections. Research Laboratories, General Motors Corporation.

Saunier, N.; Sayed, T.; Ismail, K. 2010. Large Scale Automated Analysis of Vehicle Interactions and Collisions, Transportation Research Record 2147: 42–50. https://doi.org/10.3141/2147-06

Sayed, T.; Ismail, K.; Zaki, M. H.; Autey, J. 2012. Feasibility of Computer Vision-Based Safety Evaluations: Case Study of a Signalized Right-Turn Safety Treatment, Transportation Research Record 2280: 18–27. https://doi.org/10.3141/2280-03

Songchitruksa, P.; Tarko, A. 2006. The Extreme Value Theory Approach to Safety Estimation, Accident Analysis and Prevention 38(4): 811–822. https://doi.org/10.1016/j.aap.2006.02.003

Tarko, A.; Davis, G.; Saunier, N.; Sayed, T.; Washington, S. 2009. Surrogate Measures of Safety. White Paper, ANB20 (3) Sub- Committee on Surrogate Measures of Safety.

Tarko, A. P. 2012. Use of Crash Surrogates and Exceedance Statistics to Estimate Road Safety, Accident Analysis and Prevention 45: 230–240. https://doi.org/10.1016/j.aap.2011.07.008

Tarko, A. P.; Romero, M. A.; Bandaru, V. K.; Ariyur, K. B.; Lizarazo, C. G. 2017. Feasibility of Tracking Vehicles at Intersections with a Low-End LiDAR, in Proc. of the 96th TRB Annual Meeting, 8–12 January 2017, Washington D.C., USA.

T-Analyst. 2016. Software for Semi-Automated Video Processing. Available from the Internet: www.tft.lth.se/software

Zheng, L.; Ismail, K.; Meng, X. 2014a. Freeway Safety Estimation Using Extreme Value Theory Approaches: a Comparative Study, Accident Analysis and Prevention 62: 32–41. https://doi.org/10.1016/j.aap.2013.09.006

Zheng, L., Ismail, K.; Meng, X. 2014b. Traffic Conflict Techniques for Road Safety Analysis: Open Questions and Some Insights, Canadian Journal of Civil Engineering 41(7): 633–641. https://doi.org/10.1139/cjce-2013-0558

Downloads

Published

25.09.2017

How to Cite

Ušpalytė-Vitkūnienė, R., & Laureshyn, A. (2017). Perspectives for Surrogate Safety Studies in East-European Countries. The Baltic Journal of Road and Bridge Engineering, 12(3), 161–166. https://doi.org/10.3846/bjrbe.2017.19