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Validation of Simulated Safety Indicators with Traffic Crash Data

Author

Listed:
  • Borja Alonso

    (Department of Transport and Project and Process Technology, University of Cantabria, Avda. De Los Castros, 44, 39005 Santander, Spain)

  • Vittorio Astarita

    (Department of Transport and Project and Process Technology, University of Cantabria, Avda. De Los Castros, 44, 39005 Santander, Spain)

  • Luigi Dell’Olio

    (Department of Transport and Project and Process Technology, University of Cantabria, Avda. De Los Castros, 44, 39005 Santander, Spain)

  • Vincenzo Pasquale Giofrè

    (Department of Civil Engineering, University of Calabria, 87036 Arcavacata, Italy)

  • Giuseppe Guido

    (Department of Civil Engineering, University of Calabria, 87036 Arcavacata, Italy)

  • Marcella Marino

    (Department of Civil Engineering, University of Calabria, 87036 Arcavacata, Italy)

  • William Sommario

    (Department of Civil Engineering, University of Calabria, 87036 Arcavacata, Italy)

  • Alessandro Vitale

    (Department of Civil Engineering, University of Calabria, 87036 Arcavacata, Italy)

Abstract

The purpose of this document is to validate a new methodology useful for the estimation of road accidents resulting from possible driver distractions. This was possible through a statistical comparison made between real accident data between 2016 and 2018 in the city of Santander (Spain) and simulated data resulting from the application of the methodology on two areas of study. The methodology allows us to evaluate possible collisions starting from the knowledge of vehicular trajectories extrapolated from microsimulation. Studies show that there are good correlations between the real data and the simulated data. The results obtained show that the proposed methodology can be considered reliable and, therefore, it could be of fundamental importance for designers, since it would simplify the choice between different possible intervention scenarios, determining which is the least risky in terms of road safety.

Suggested Citation

  • Borja Alonso & Vittorio Astarita & Luigi Dell’Olio & Vincenzo Pasquale Giofrè & Giuseppe Guido & Marcella Marino & William Sommario & Alessandro Vitale, 2020. "Validation of Simulated Safety Indicators with Traffic Crash Data," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:925-:d:313474
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    References listed on IDEAS

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    1. Papola, Andrea & Tinessa, Fiore & Marzano, Vittorio, 2018. "Application of the Combination of Random Utility Models (CoRUM) to route choice," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 304-326.
    2. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
    3. Marzano, Vittorio & Tocchi, Daniela & Papola, Andrea & Aponte, Dario & Simonelli, Fulvio & Cascetta, Ennio, 2018. "Incentives to freight railway undertakings compensating for infrastructural gaps: Methodology and practical application to Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 110(C), pages 177-188.
    4. Cantarella, Giulio E. & Watling, David P., 2016. "A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 3-21.
    5. Papola, Andrea, 2016. "A new random utility model with flexible correlation pattern and closed-form covariance expression: The CoRUM," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 80-96.
    6. Kucharski, Rafał & Gentile, Guido, 2019. "Simulation of rerouting phenomena in Dynamic Traffic Assignment with the Information Comply Model," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 414-441.
    7. Osorio, Carolina & Punzo, Vincenzo, 2019. "Efficient calibration of microscopic car-following models for large-scale stochastic network simulators," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 156-173.
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    Cited by:

    1. Jing Liu & Huapu Lu & Mingyu Chen & Jianyu Wang & Ying Zhang, 2020. "Macro Perspective Research on Transportation Safety: An Empirical Analysis of Network Characteristics and Vulnerability," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    2. Vittorio Astarita & Ciro Caliendo & Vincenzo Pasquale Giofrè & Isidoro Russo, 2020. "Surrogate Safety Measures from Traffic Simulation: Validation of Safety Indicators with Intersection Traffic Crash Data," Sustainability, MDPI, vol. 12(17), pages 1-21, August.

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