Time-specific Safety Performance Functions (SPFs) were proposed to achieve accurate and dynamic crash frequency predictions and bridge the gap between annual crash frequency prediction and real-time crash likelihood prediction. This research proposed time-specific SPFs considering the temporal variation in crashes and traffic characteristics. Firstly, the developed time-specific SPFs that include different ATM strategies (i.e., HOV, merge, diverge and reversible lanes segments) were investigated in this study. The results indicate that traffic turbulence during specific hours would relate to the crash occurrence. Further, the variables representing the speed and occupancy differences between the HOV lanes/reversible lanes and general-purpose lanes were positively associated with crash frequency. Moreover, the design of the reversible lane segments and the number of access points positively impact the crash frequency. Secondly, this
study proposed different methodologies to improve the prediction accuracy of time-specific SPFs. The model comparison includes the negative binomial model, Poisson lognormal model and hierarchical Poisson lognormal model. The results showed that the proposed hierarchical models outperformed the corresponding Poisson lognormal and Negative binomial models.
Besides prediction accuracy, this study also successfully identified the factors associated with the different crash types or severity in crash frequency prediction models. Finally, this study proposed a novel iterative imputation method to impute the 100% missing volume and speed data from the different states with similar crash rates. The results indicated that the imputed traffic data could capture the same traffic pattern as the real-collected traffic data. Further, the MAE between the imputed volume and the real-collected volume for FL is 2.47 vehicles/3hrs/segment. The MAPE
between the imputed and real-collected volumes for FL is 11.07%. Moreover, this study applied the proposed iterative imputation method to develop time-specific SPFs for the state without traffic data and compared the results. The results illustrated that the time-specific SPFs developed by imputed traffic data perfectly reflected the significant variables for both morning and afternoon peak models, with a prediction accuracy of 87.1% for the morning peak model. This could help the traffic operators in the states without high-resolution traffic data to determine the factors contributing to crash occurrence on freeway segments during a specific time period.
Major: Civil Engineering
Educational Career:
Bachelor's of Transportation, BS, 2009, Southwest Jiaotong University
Master's of Transportation Engineering, MS, 2015, The George Washington University
Committee in Charge:
Mohamed Abdel—Aty, Chair, Department of Civil, Environmental, and Construction Engineering
Nada Mahmoud , Civil, Environmental and Construction Engineering
Xin Yan, Statistics and Data Science
Naveen Eluru, Civil, Environmental and Construction Engineering
Approved for distribution by Mohamed Abdel-Aty, Committee Chair, on February 16, 2023.
The public is welcome to attend.