Developing Commercial Motor Vehicle Crash-Specific Safety Performance Functions at Interchange Ramp Terminals in Kentucky

Abstract

Commercial motor vehicle (CMV) drivers often experience difficulties in turning and crossing at ramp terminals. CMV-involved crashes could cause queue spillback at ramp terminals and possibly nearby freeway mainline and crossroads. The safety and mobility of ramp terminals for accommodating CMVs is thus of significant importance. However, interchange ramp terminals have rarely been investigated in previous safety studies, especially with regard to CMV-involved crashes. This study aimed at examining CMV-related crashes that occurred at ramp terminals. Heterogeneous negative binomial (HTNB) and traditional negative binomial (NB) models were fitted and compared while developing a safety performance function (SPF) for predicting CMV crashes and identifying high-risk ramp terminals. Information on crash history and site-specific characteristics was collected at 285 ramp terminals in the state of Kentucky between 2015 and 2019. The results showed that the HTNB model outperformed the NB model in relation to various goodness-of-fit measures (e.g., likelihood ratio test “LRT”, Akaike information criterion “AIC” and McFadden Pseudo R-squared statistic). The predicted crash frequencies while applying the HTNB model were then used to identify and rank high-risk ramp terminals. Ramp terminals with signalized traffic control type, with greater average daily traffic on the exit ramp, with two or more traffic lanes on the exit ramp, and adjacent to commercial or industrial areas were found to experience more CMV crashes. Several safety countermeasures were proposed to alleviate CMV-involved crashes at ramp terminals. One example is ensuring the presence of physical medians on crossroads (or major roads) ahead of exits ramps.

Developing Economic Loss-Based Thresholds for Improving Context-Specific Crash Prediction Models

“…While numerous studies have conducted advanced statistical analyses to develop innovative crash prediction models, such as multilayer perceptrons (MLPs), support vector regressions (SVRs), random forests (RFs), boosting and Tobit models, and heterogeneous negative binomial (HTNB) models, and demonstrated the superiority of these methods over HSM-SPFs ( 27 – 29 ), the current study proposes a method for incorporating the current version of HSM-SPFs into the recent FDOT context-based classification method. There has been extensive literature focusing on calibration methods applied on HSM-SPFs ( 30 ).…”