Safety Performance Functions Reflecting Categorical Impact of Exposure Variables for Freeways

Kim, Ducknyung; Kim, Dong Kyu; Lee, Chungwon

doi:10.3141/2398-08

Cited by 11 publications

(2 citation statements)

References 26 publications

(7 reference statements)

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“…The system is a representative database with numerous previous studies concerning traffic, pedestrian, and transit accidents (28,29). It is based on reports from police, insurance companies, and mutual aid associations.…”

Section: Data Bus Crash Datamentioning

confidence: 99%

Effect of Regional Characteristics on Injury Severity in Local Bus Crashes

Yoon

Kho

Kim

2017

Transportation Research Record: Journal of the Transportation R

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As the importance of public transportation increases, the management of bus-involved crashes has become a crucial issue for traffic safety. However, there are relatively few studies on crash severity for buses in South Korea. This study investigated factors that influence the severity of injuries that occur in local bus crashes. The study used commercial vehicle crash data from a 5-year period from 2010 through 2014 in South Korea. To determine unobserved regional effects on crash severity, a hierarchical ordered model was applied to the analysis. Individual crash characteristics were set to lower-level variables, and regional characteristics were adopted as upper-level variables. At the lower level, the factors affecting severity of injuries included vehicle speed, vehicle age, road alignment, surface status, road class, and traffic light installation, as found in previous studies. At the upper level, the factors included pavement, emergent medical environment, traffic rate of compliance, and ratio of elderly in the community. There was a 5.1% unobserved variation between regions from the intraclass correlation analysis. The validity of a hierarchical model for local bus crashes was verified by applying the model to other long-distance buses, and it appeared there were no regional effects. This study found a regional effect for local bus crash severity, and thus this factor is important when developing prevention plans to reduce local bus crashes. These results contribute to the study of traffic safety.

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Section: Data Bus Crash Datamentioning

confidence: 99%

Effect of Regional Characteristics on Injury Severity in Local Bus Crashes

Yoon

Kho

Kim

2017

Transportation Research Record: Journal of the Transportation R

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“…As the crash-frequency data are non-negative integers, traditional studies started with the Poisson regression model (2)(3)(4). Afterward, many studies extended the Poisson regression model to the negative binomial model to eliminate the impact of the possible overdispersion in crash-frequency data (5)(6)(7)(8)(9). The negative binomial model is the recommended modeling approach in the Highway Safety Manual (HSM), and it is the most frequently used model in previous crash frequency modeling research (10).…”

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confidence: 99%

Short-Term Safety Performance Functions for Freeways Including High Occupancy Vehicle Lanes

Abdel-Aty

Mahmoud

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Short-term 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. The proposed short-term SPFs consider the temporal variation in crashes and traffic characteristics. This study contributes to the literature by developing short-term SPFs at hourly aggregation levels for freeways that include high-occupancy vehicle (HOV) lanes using loop detector data from Arizona State, U.S. Variables that capture the short-term traffic turbulence were prepared and considered in the developed SPFs. Further, this study investigated the factors contributing to crash frequency using three different ways to represent the hourly traffic: annual average hourly traffic, annual average weekday hourly traffic (AAWDHT), and annual average weekday peak hour traffic (AAWDPT). The results indicated that the traffic volume variable was found to be significant in all the developed models. Further, the variables that represent the speed and occupancy differences between HOV lanes and general-purpose lanes were positively associated with crash frequency. This study proposed a series of variables that reflect the short-term traffic turbulence. The models comparison results showed an improvement in [Formula: see text] from 2.4% to 12.8% when including the proposed variables. Further, the results indicated that the Poisson-lognormal approach outperformed the basic negative binomial model in both AAWDHT, and AAWDPT models. Further, the AAWDPT model was found to have the best performance in relation to Akaike information criterion and [Formula: see text].

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