Calibrating stochastic traffic simulation models for safety and operational measures based on vehicle conflict distributions obtained from aerial and traffic camera videos

Di Sha, Jingqin Gao, Di Yang, Fan Zuo, Kaan Ozbay

Research output: Contribution to journalArticlepeer-review

Abstract

Proper calibration process is of considerable importance for traffic safety evaluations using simulation models. Allowing for a pure with and without comparison under identical circumstances that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, such as connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This paper proposes a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization so that the operational and safety measures can be calibrated simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. The proposed calibration methodology is implemented using an open-source software SUMO on a simulation network of the Flatbush Avenue corridor in Brooklyn, NY. 17 key parameters are calibrated using the SPSA algorithm and are compared with the real-world traffic conflicts extracted using vehicle trajectories from 14 h’ high-resolution aerial and traffic surveillance videos. Representative days are identified to create variation envelopes for performance measures. Four acceptability criteria, including control for time-variant outliers and inliers, bounded dynamic absolute and system errors are adopted for results analysis. The results show that the calibrated parameters can significantly improve the performance of the simulation model to represent real-world safety conditions (i.e., traffic conflicts) as well as operational conditions. The case study also demonstrates the usefulness of aerial imagery and the applicability of the proposed model calibration framework, so the calibrated model can be used to evaluate the safety benefits of CV applications more accurately.

Original languageEnglish (US)
Article number106878
JournalAccident Analysis and Prevention
Volume179
DOIs
StatePublished - Jan 2023

Keywords

  • Aerial imagery
  • Connected vehicle
  • Model calibration
  • Simultaneous perturbation stochastic approximation
  • Traffic conflicts
  • Traffic simulation

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Safety, Risk, Reliability and Quality
  • Public Health, Environmental and Occupational Health

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