Abstract
One of the measures that can reduce the negative effects of road accidents is the quick arrive of emergency vehicles to the accident area. This measure requires an effective location in space and on time of these vehicles. This location can be decided after an analysis of the available data in order to find the spatial and temporal characteristics of road accidents.
The study presented in this paper uses time series accident data of the 15 districts of Rome Municipality, collected in four months in 2016. Results show that such analyses can be a powerful tool for identifying the temporal and spatial structure of road accidents in urban areas and that relevant differences exist in temporal patterns among different districts and types of road users. Further, such outcomes can be used as inputs to decide the optimal location on the urban area of mobile emergency units.
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References
World Health Organization (WHO): Global status report on road safety (2013)
Theofilatos, A.: Incorporating real-time traffic and weather data to explore road accident likelihood and severity in urban arterials. J. Saf. Res. 61, 9–21 (2017)
European Road Safety Observatory, ERSO: Traffic safety basic facts on urban areas. European Commission, Directorate General for Transport (2015)
White Paper: Roadmap to a Single European Transport Area – Towards a Competitive and Resource Efficient Transport System. European Commission, Brussels (2011)
Yannis, G., Antoniou, C., Papadimitriou, E.: Autoregressive nonlinear time-series modeling of traffic fatalities in Europe. Eur. Transp. Res. Rev. 3(3), 113–127 (2011)
Kumar, S., Toshniwal, D.: A novel framework to analyze road accident time series data. J. Big Data 3(8), 2–11 (2016)
Archer, J.: Methods for the Assessment and Prediction of Traffic Safety at Urban Intersections and Their Application in Micro-simulation Modelling. Royal Institute of Technology, Sweden (2004)
Elvik, R., Vaa, T., Erke, A., Sorensen, M.: The Handbook of Road Safety Measures. Emerald Group Publishing, Bingley (2009)
Bhardwaj, R., Ridhi, R., Kumar, R.: Modified approach of cluster algorithm to analysis road accident. Int. J. Comput. Appl. 166(2), 24–28 (2017)
Geurts, K., Thomas, I., Wets, G.: Understanding spatial concentrations of road accidents using frequent item sets. Accid. Anal. Prev. 37(4), 787–799 (2005)
Geurts, K., Wets, G., Brijs, T., Vanhoof, K.: Profiling high frequency accident locations using association rules. In: Proceedings of the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C. (2003)
Hughes, B.P., Newstead, S., Anund, A., Shud, C.C., Falkmera, T.: A review of models relevant to road safety. Accid. Anal. Prev. 74, 250–270 (2015)
Kumar, S., Toshniwal, D.: A data mining approach to characterize road accident locations. J. Mod. Transp. 24(1), 62–72 (2016)
Prato, C.G., Bekhor, S., Galtzur, A., Mahalel, D., Prashker, J.N.: Exploring the potential of data mining techniques for the analysis of accident patterns. In: Proceeding of 12th WCTR, Lisbon, Portugal (2010)
Tesema, T.B., Abraham, A., Grosan, C.: Rule mining and classification of road accidents using adaptive regression trees. Int. J. Simul. 6, 80–94 (2005)
Yannis, G., Dragomanovits, A., Laiou, A., et al.: Road traffic accident prediction modelling: a literature review. Transport 170(TR5), 245–254 (2017)
Marcianò, F.A., Vitetta, A.: Risk analysis in road safety: an individual risk model for drivers and pedestrians to support decision planning processes. Int. J. Saf. Secur. Eng. 1(3), 265–282 (2011)
Russo, F., Comi, A.: From the analysis of European accident data to safety assessment for planning: the role of good vehicles in urban area. Eur. Transp. Res. Rev. 9(9), 1–12 (2017)
Rome: OpenData Roma, Italy. http://dati.comune.roma.it. Accessed Sept 2017
Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice (2016). www.otexts.org
Comi, A., Nuzzolo, A., Brinchi, S., Verghini, R.: Bus travel time variability: some experimental evidences. Transp. Res. Procedia 27, 101–108 (2017). https://doi.org/10.1016/j.trpro.2017.12.072
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Comi, A., Persia, L., Nuzzolo, A., Polimeni, A. (2019). Exploring Temporal and Spatial Structure of Urban Road Accidents: Some Empirical Evidences from Rome. In: Nathanail, E., Karakikes, I. (eds) Data Analytics: Paving the Way to Sustainable Urban Mobility. CSUM 2018. Advances in Intelligent Systems and Computing, vol 879. Springer, Cham. https://doi.org/10.1007/978-3-030-02305-8_18
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DOI: https://doi.org/10.1007/978-3-030-02305-8_18
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