The paper presents a microsimulation-based approach for roundabout safety performance evaluation. Based on a sample of Slovenian roundabouts, the vehicle trajectories exported from AIMSUN and VISSIM were used to estimate traffic conflicts using the Surrogate Safety Assessment Model (SSAM). AIMSUN and VISSIM were calibrated for single-lane, double-lane and turbo roundabouts using the corresponding empirical capacity function which included critical and follow-up headways estimated through meta-analysis. Based on calibration of the microsimulation models, a crash prediction model from simulated peak hour conflicts for a sample of Slovenian roundabouts was developed. A generalized linear model framework was used to estimate the prediction model based on field collected crash data for 26 existing roundabouts across the country. Peak hour traffic distribution was simulated with AIMSUN, and peak hour conflicts were then estimated with the SSAM applying the filters identified by calibrating AIMSUN and VISSIM. The crash prediction model was based on the assumption that the crashes per year are a function of peak hour conflicts, the ratio of peak hour traffic volume to average daily traffic volume and the roundabout outer diameter. Goodness-of-fit criteria highlighted how well the model fitted the set of observations also better than the SSAM predictive model. The results highlighted that the safety assessment of any road unit may rely on surrogate safety measures, but it strongly depends on microscopic traffic simulation model used.

Giuffrè, O., Granà, A., Tumminello, M.L., Giuffrè, T., Trubia, S., Sferlazza, A., et al. (2018). Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation. JOURNAL OF ADVANCED TRANSPORTATION, 2018, 1-14 [10.1155/2018/4915970].

Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation

Giuffrè, Orazio;Granà, Anna
;
Tumminello, Maria Luisa;Giuffrè, Tullio;Sferlazza, Antonino;
2018-01-01

Abstract

The paper presents a microsimulation-based approach for roundabout safety performance evaluation. Based on a sample of Slovenian roundabouts, the vehicle trajectories exported from AIMSUN and VISSIM were used to estimate traffic conflicts using the Surrogate Safety Assessment Model (SSAM). AIMSUN and VISSIM were calibrated for single-lane, double-lane and turbo roundabouts using the corresponding empirical capacity function which included critical and follow-up headways estimated through meta-analysis. Based on calibration of the microsimulation models, a crash prediction model from simulated peak hour conflicts for a sample of Slovenian roundabouts was developed. A generalized linear model framework was used to estimate the prediction model based on field collected crash data for 26 existing roundabouts across the country. Peak hour traffic distribution was simulated with AIMSUN, and peak hour conflicts were then estimated with the SSAM applying the filters identified by calibrating AIMSUN and VISSIM. The crash prediction model was based on the assumption that the crashes per year are a function of peak hour conflicts, the ratio of peak hour traffic volume to average daily traffic volume and the roundabout outer diameter. Goodness-of-fit criteria highlighted how well the model fitted the set of observations also better than the SSAM predictive model. The results highlighted that the safety assessment of any road unit may rely on surrogate safety measures, but it strongly depends on microscopic traffic simulation model used.
Settore ICAR/04 - Strade, Ferrovie Ed Aeroporti
Settore ING-INF/04 - Automatica
https://www.hindawi.com/journals/jat/2018/4915970/
Giuffrè, O., Granà, A., Tumminello, M.L., Giuffrè, T., Trubia, S., Sferlazza, A., et al. (2018). Evaluation of Roundabout Safety Performance through Surrogate Safety Measures from Microsimulation. JOURNAL OF ADVANCED TRANSPORTATION, 2018, 1-14 [10.1155/2018/4915970].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/337581
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