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Exploring Temporal and Spatial Structure of Urban Road Accidents: Some Empirical Evidences from Rome

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Data Analytics: Paving the Way to Sustainable Urban Mobility (CSUM 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 879))

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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

  1. World Health Organization (WHO): Global status report on road safety (2013)

    Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. European Road Safety Observatory, ERSO: Traffic safety basic facts on urban areas. European Commission, Directorate General for Transport (2015)

    Google Scholar 

  4. White Paper: Roadmap to a Single European Transport Area – Towards a Competitive and Resource Efficient Transport System. European Commission, Brussels (2011)

    Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. Kumar, S., Toshniwal, D.: A novel framework to analyze road accident time series data. J. Big Data 3(8), 2–11 (2016)

    Google Scholar 

  7. 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)

    Google Scholar 

  8. Elvik, R., Vaa, T., Erke, A., Sorensen, M.: The Handbook of Road Safety Measures. Emerald Group Publishing, Bingley (2009)

    Book  Google Scholar 

  9. Bhardwaj, R., Ridhi, R., Kumar, R.: Modified approach of cluster algorithm to analysis road accident. Int. J. Comput. Appl. 166(2), 24–28 (2017)

    Google Scholar 

  10. Geurts, K., Thomas, I., Wets, G.: Understanding spatial concentrations of road accidents using frequent item sets. Accid. Anal. Prev. 37(4), 787–799 (2005)

    Article  Google Scholar 

  11. 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)

    Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Kumar, S., Toshniwal, D.: A data mining approach to characterize road accident locations. J. Mod. Transp. 24(1), 62–72 (2016)

    Article  Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. Yannis, G., Dragomanovits, A., Laiou, A., et al.: Road traffic accident prediction modelling: a literature review. Transport 170(TR5), 245–254 (2017)

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Google Scholar 

  19. Rome: OpenData Roma, Italy. http://dati.comune.roma.it. Accessed Sept 2017

  20. Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice (2016). www.otexts.org

  21. 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

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Rome Tor Vergata, 00133, Rome, Italy

    Antonio Comi, Agostino Nuzzolo & Antonio Polimeni

  2. Centro di ricerca per il Trasporto e la Logistica, Sapienza University of Rome, Rome, Italy

    Luca Persia

Authors
  1. Antonio Comi
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  2. Luca Persia
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  3. Agostino Nuzzolo
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  4. Antonio Polimeni
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Corresponding author

Correspondence to Antonio Comi .

Editor information

Editors and Affiliations

  1. Department of Civil Engineering, Traffic, Transportation and Logistics Laboratory (TTLog), University of Thessaly, Volos, Greece

    Eftihia G. Nathanail

  2. Department of Civil Engineering, Traffic, Transportation and Logistics Laboratory (TTLog), University of Thessaly, Volos, Greece

    Ioannis D. Karakikes

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02304-1

  • Online ISBN: 978-3-030-02305-8

  • eBook Packages: EngineeringEngineering (R0)

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