Time-specific hierarchical models for predicting crash frequency of reversible and high-occupancy vehicle lanes. (March 2023)

• Record Type: Journal Article
• Authors: Fu, Jingwan; Abdel-Aty, Mohamed; Mahmoud, Nada
• Is Part Of: Accident analysis and prevention. Volume 181(2023)
• Publication Date: 2023-03
• Subjects: Safety performance function -- Freeway -- Time-specific -- High-occupancy vehicle lane -- Reversible lane -- Hierarchical model; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2022.106953
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: This study allows us to better understand the specific use of TSM&O strategies (i.e., RL and HOV) from not only the perspective of operation but also the safety point of view by developing time specific SPFs. The proposed variable that indicates the impact of entering/exiting the RL can be used to investigate the physically divided lanes on the freeway. Researchers could apply this method to analyze the expressway in multiple states. The models' results can help the operators make decisions or adjust the geometry design to improve traffic safety for the corresponding segments. This study considered the hierarchical data structure of the crash type, crash severity and crash frequency and successfully improved the accuracy of the prediction of crash frequency. Abstract: Time-specific Safety Performance Functions (SPFs) were proposed to achieve accurate and dynamic crash frequency predictions. This study contributes to the literature by developing time-specific SPFs for freeways that include reversible lanes (RL) and freeways that include High-Occupancy Vehicle lanes (HOV) using Microwave Vehicle Detection System (MVDS) data from Virginia, Arizona and Washington States. Variables that capture the time-specific traffic turbulence were prepared and considered in the developed SPFs. Moreover, two different hierarchical models were proposed to identify factors associated with the different crash types or severity in crash frequency prediction. The results indicated that the variables representing the volume difference between reversible and general-purpose lanes (GPL) were positively associated with crash frequency. Further, the variable that indicated the design of the access point of the reversible lane was positively associated with crash frequency. The models comparison results showed that the hierarchical models outperformed the corresponding Poisson lognormal model with lower AIC and MAE values. This study also tested the proposed hierarchical models on High-Occupancy Vehicle freeway sections and reached the same conclusion on model comparison results. The significant variables representing the logarithm of volume were found to be significant and positive with crash frequency. Moreover, the difference in average speed between the HOV lanes and GPL was also found to be positive and significant with the crash frequency. In general, this study successfully identified the factors associated with the different crash types or severity in crash frequency prediction models.