Abstract

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 the traffic turbulence during specific hours would relate to crash occurrence. Further, the variables that represent the speed and occupancy differences between the HOV lanes/reversible lanes and general-purpose lanes were found to be positively associated with crash frequency. Moreover, the design of the reversible lane segments, the number of access points positively impacts the crash frequency. Secondly, this study proposed different methodologies to improve the prediction accuracy of time-specific SPFs. The model comparison including the negative binomial model, Poisson lognormal model and hierarchical Poisson lognormal model. The results showed that the Poisson lognormal model outperformed the negative binomial model. Moreover, the hierarchical models outperformed the corresponding Poisson lognormal model. Other than 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 crash rates for 18 states were calculated and the ANOVA test was applied to group the states with similar crash rates. Afterward, this study used FL and VA, which both have traffic data to test the proposed iterative imputation method. 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.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu

Graduation Date

2023

Semester

Spring

Advisor

Abdel-Aty, Mohamed

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering and Computer Science

Department

Civil, Environmental, and Construction Engineering

Degree Program

Civil Engineering

Identifier

CFE0009523; DP0027528

URL

https://purls.library.ucf.edu/go/DP0027528

Language

English

Release Date

May 2024

Length of Campus-only Access

1 year

Access Status

Doctoral Dissertation (Campus-only Access)

Download

Restricted to the UCF community until May 2024; it will then be open access.

Share

COinS