Road Safety in Great Britain: An Exploratory Data Analysis

Authors: Jatin Kumar Choudhary, Naren Rayala, Abbas Eslami Kiasari, Fahimeh Jafari

Abstract:

Great Britain has one of the safest road networks in the world. However, the consequences of any death or serious injury are devastating for loved ones, as well as for those who help the severely injured. This paper aims to analyse Great Britain's road safety situation and show the response measures for areas where the total damage caused by accidents can be significantly and quickly reduced. For the past 30 years, the UK has had a good record in reducing fatalities over the past 30 years, there is still a considerable number of road deaths. The government continues to scale back road deaths empowering responsible road users by identifying and prosecuting the parameters that make the roads less safe. This study represents an exploratory analysis with deep insights which could provide policy makers with invaluable insights into how accidents happen and how they can be mitigated. We use STATS19 data published by the UK government. Since we need more information about locations which is not provided in STATA19, we first expand the features of the dataset using OpenStreetMap and Visual Crossing. This paper also provides a discussion regarding new road safety methods.

Keywords: Road safety, data analysis, OpenStreetMap, feature expanding.

References:

[1] https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries
[2] https://www.gov.uk/government/statistics/reported-road-casualties-great-britain-annual-report-2021/reported-road-casualties-great-britain-annual-report-2021
[3] Reported road casualties Great Britain: 2015 annual report, https://www.gov.uk/government/statistics/reported-road-casualties-great-britain-annual-report-2015
[4] https://www.racfoundation.org/research
[5] Road Safety Data. https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data
[6] https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data/datafile/36f1658e-b709-47e7-9f56-cca7aefeb8fe/preview
[7] https://patents.google.com/patent/WO2021191168A1/en
[8] Silva, P.B., Andrade, M. and Ferreira, S., 2020. Machine learning applied to road safety modeling: a systematic literature review. Journal of traffic and transportation engineering (English edition).
[9] Li, H., Zhu, M., Graham, D.J. and Ren, G., 2021. Evaluating the speed camera sites selection criteria in the UK. Journal of safety research, 76, pp.90-100.
[10] Tonhauser, M. and Ristvej, J., 2021. Implementation of new technologies to improve safety of road transport. Transportation research procedia, 55, pp.1599-1604.
[11] Nogayeva, S., Gooch, J. and Frascione, N., 2020. The forensic investigation of vehicle-pedestrian collisions: a review. Science & Justice.
[12] Jaikishan Damani, Perumal Vedagiri, “Safety of motorised two wheelers in mixed traffic conditions: Literature review of risk factors”, Journal of Traffic and Transportation Engineering (English Edition), Volume 8, Issue 1, 2021, Pages 35-56
[13] Edwards, Phil, Judith Green, Ian Roberts, Chris Grundy and Kate Lachowycz. “Deprivation and road safety in London - a report to the London Road Safety Unit.” (2008).
[14] Cerca, A.; Ferreira, A.; Lourenço, A. “Increasing Road Safety with Machine Learning - A Fatigue and Drowsiness Detection System”, Proc Portuguese Conf. on Pattern Recognition - RecPad, Evora, Portugal, Vol., pp. -, October, 2020.
[15] Musunuru A, Porter RJ. Applications of Measurement Error Correction Approaches in Statistical Road Safety Modeling. Transportation Research Record. 2019;2673(8):125-135. doi:10.1177/0361198119841856
[16] Baklanova, K V; Voevodin, E S; Fomin, E V; Kashura, A S; Cheban, E P. “Identification of factors affecting accidents on the intercity road network”, IOP Conference Series. Materials Science and Engineering; Bristol Vol. 1061, ISS. 1, (Feb 2021). DOI:10.1088/1757-899X/1061/1/012005
[17] https://www.ucl.ac.uk/mathematical-physical-sciences/news/2018/aug/half-london-car-crashes-take-place-5-citys-junctions#:~:text=Transport%20for%20London%20figures%20show,where%20a%20road%20accident%20is
[18] https://www.fleetnews.co.uk/news/fleet-industry-news/2020/09/30/tfl-data-shows-12-increase-in-london-road-deaths
[19] Reported road casualties in Great Britain: 2019 annual report, https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/922717/reported-road-casualties-annual-report-2019.pdf
[20] https://ec.europa.eu/transport/road_safety/statistics-and-analysis/statistics-and-analysis-archive/esafety/vehicle-technologies-and-road-casualty-reduction_en
[21] M.P. Basgalupp, A.C.P.L.F. Carvalho, R.C. Barros, et al. “Lexicographic multi-objective evolutionary induction of decision trees”, International Journal of Bio-Inspired Computation, 1 (1) (2009), pp. 105-117
[22] O. Chapelle, B. Scholkopf and A. Zien, Eds., "Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) (Book reviews)," in IEEE Transactions on Neural Networks, vol. 20, no. 3, pp. 542-542, March 2009, doi: 10.1109/TNN.2009.2015974.
[23] K.M. Decker, S. Focardi, Technology Overview: a Report on Data Mining Technical Report CSCS TR-95-02 Swiss Scientific Computing Center, Bern (1995)