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Short-term traffic speed prediction is a key component of proactive traffic control in the intelligent transportation systems. The objective of this study is to investigate the short-term traffic speed prediction under different data collection time intervals. Traffic speed data was collected from an urban freeway in Edmonton, Canada. A seasonal autoregressive integrated moving average plus seasonal discrete grey model structure (SARIMA-SDGM) was proposed to perform the traffic speed prediction. The model performance of SARIMA-SDGM model was compared with that of the seasonal autoregressive integrated moving average (SARIMA) model, seasonal discrete grey model (SDGM), artificial neural network (ANN) model, and support vector regression (SVR) model. The results showed that SARIMA-SDGM model performs best with the lowest mean absolute error (MAE), mean absolute percentage error (MAPE), and the root mean square error (RMSE). The traffic speed prediction accuracy under different time intervals were compared based on the SARIMA-SDGM model. The results showed that the prediction accuracy improves with the increase in time interval. In addition, when the time interval is greater than 10 min, the prediction results yield stable prediction accuracy. [ABSTRACT FROM AUTHOR]

Highlights • Trajectory-based calibration of simulation model parameters for safety analysis is investigated. • Automated computer vision techniques are used to extract vehicle trajectories and traffic conflicts. • High correlation between simulated and field-measured conflicts was found after calibration. • The results show that using simulation to evaluate safety without proper calibration should be avoided. • The calibration parameters varied considerably among different traffic environments. Abstract The primary objective of this study is to investigate the relationship between field-measured conflicts and simulated conflicts estimated from the surrogate safety assessment model (SSAM) for two signalized intersections in Brisbane, Australia. Traffic video data collected from the two signalized intersections (left-hand traffic regulations) were used for analysis. Automated computer vision analysis techniques were used to extract vehicle trajectories and identify field-measured conflicts. Simulated conflicts were obtained using VISSIM and SSAM tool. A two-step calibration procedure was performed to test the correlation between simulated and field-measured conflicts. In addition, the spatial distributions of simulated and field-measured conflicts were compared. Finally, the calibration results for the left-hand traffic environment were compared with results for signalized intersections in Canada and China (right-hand traffic environment). The results indicated a good correlation between simulated and field-measured conflicts, especially at higher TTC thresholds. However, major differences between field-measured and simulated conflict spatial distributions indicate that despite the good correlation obtained via calibration, simulated conflicts do not adequately capture the actual conflict occurrence mechanism. It was also found that there were significant differences between the left-hand and right-hand traffic environments calibration results. [ABSTRACT FROM AUTHOR]

The safety of signalized intersections has traditionally been evaluated at an aggregate level by relating historical collision records for several years to the annual traffic volume and the geometric characteristics of the intersection. This is a reactive and macroscopic approach that gives little insight into how important dynamic signal cycle-related variables can affect intersection safety such as the arrival type and the shock wave characteristics. The objective of this study is to develop traffic conflict-based real-time safety models for signalized intersections using several state-of-the-art techniques. Traffic conflicts were measured by multiple indicators including time-to-collision (TTC), modified time-to-collision (MTTC), and deceleration rate to avoid collision (DRAC). Traffic conflict rate was employed as independent variable while traffic volume, queue length, shock wave area, shock wave speed, and platoon ratio of each cycle were used as covariates in the safety models. Four candidate Tobit models were developed and compared under the Bayesian framework: conventional Tobit model, grouped random parameters Tobit (GRP-Tobit) model, random intercept Tobit (RI-Tobit) model, and random parameters Tobit (RP-Tobit) model. The results showed that the GRP-Tobit model performs best with lowest Deviance Information Criteria (DIC), indicating that accounting for the unobserved heterogeneity across sites can significantly improve the model fit. The model estimation results showed that higher conflict rates were associated with various shock wave characteristics (positive sign for shock wave area, shock wave speed, and queue length) and higher traffic volume. Lower conflict rates were related with higher platoon ratio (favorable arrival patterns). The developed models can have potential applications in real-time safety evaluation, real-time optimization of signal control, and connected and autonomous vehicles (CAV) trajectories planning., Competing Interests: Declaration of Competing Interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (Copyright © 2020 Elsevier Ltd. All rights reserved.)