Crash analysis and development of safety performance functions for Florida roads in the framework of the context classification system

Al-Omari, Ma’en Mohammad Ali; Abdel-Aty, Mohamed; Cai, Qing

doi:10.1016/j.jsr.2021.08.004

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…FDOT, in particular, has advocated the use of the new context classification guide that categorizes roadways and intersections into eight categories in accordance with land use, development patterns, and roadway connectivity, as well as other primary and secondary measures in crash prediction models, to achieve higher predictive accuracy ( 11 ). The effectiveness of the context-based approach has been extensively investigated in the previous literature ( 12 , 24 , 25 ). For instance, Al-Omari et al ( 24 ) validated the performance of FDOT context classification-based SPFs in comparison with other levels of modeling; Li et al ( 26 ) utilized this approach to develop CMFs for alternative cross-sections of rural four-lane roadways in a variety of contexts.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The effectiveness of the context-based approach has been extensively investigated in the previous literature ( 12 , 24 , 25 ). For instance, Al-Omari et al ( 24 ) validated the performance of FDOT context classification-based SPFs in comparison with other levels of modeling; Li et al ( 26 ) utilized this approach to develop CMFs for alternative cross-sections of rural four-lane roadways in a variety of contexts. While the aforementioned contexts were characterized by traffic volume, truck percentage, and access point density ( 26 ), the present research study intends to focus specifically on an innovative context-based method that enables us to classify roadway segments in Florida according to their surrounding land use types, design criteria, and roadway characteristics.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Koloushani

Abazari

Khayamim

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Negative binomial-based safety performance functions (SPFs) have been extensively used by United States Department of Transportation professionals for predictive crash analysis. Recently, the Florida Department of Transportation (FDOT) has developed a context classification approach and incorporated it into crash prediction models, which has the potential to significantly enhance their accuracy and reliability. The additional modeling contexts and parameters make it more challenging to diagnose and remedy modeling problems, however. Particularly for roadway segments with low annual average daily traffic (AADT), short lengths, or low counts of severe crashes, the SPF models significantly underestimate the actual number of crashes. This uncertainty in SPF predictions can lead FDOT practitioners to reach misleading conclusions, such as failing to detect sites with genuinely high crash rates. This project intends to establish thresholds for certain SPF parameters to ensure reliable crash predictions are obtained across various context classes. For this purpose, we (a) developed a functional statistical model that quantifies economic loss relative prediction errors as a function of AADT volume and (b) calculated the minimum context-specific AADT threshold for each segment length group, roadway category, context classification, and crash severity combination. Employing the developed AADT thresholds confirmed up to 89% reduction in SPF prediction errors for the most represented context class. In light of the results obtained, we are able to conclude that context-specific AADT thresholds perform well in significantly reducing prediction errors for the thresholded segments and contexts on Florida roadways.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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

Koloushani

Abazari

Khayamim

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Negative binomial regression was also used in [36] to examine the impact of risk factors independent of exposure when analyzing the risk of cycling crashes. The study of [37] utilized the generalized linear model with negative binomial distributions to effectively handle the dispersion present in the crash data.…”

mentioning

confidence: 99%

Safety in Traffic Management Systems: A Comprehensive Survey

Dash

et al. 2023

Designs

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Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

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“…The NB model appropriately adjusts overdispersion in the data and the Poisson model is used to predict the crash frequency (10)(11)(12). Additionally, researchers have stated that localized SPFs can better represent traffic-crash frequencies and associated factors (such as AADT and road segment length), which can be attributed to spatial dependence between crash observations (9,(13)(14)(15).…”

mentioning

confidence: 99%

“…Such factors include road geometry and conditions, environmental factors, geographic characteristics, crash characteristics, and reporting thresholds, all of which can be unique to specific jurisdictions ( 6 , 7 ). Based on the suggestion of HSM, the specified overdispersion in crash data can be well predicted using the empirical Bayes method with the negative binomial model (NB) ( 8 , 9 ). The NB model appropriately adjusts overdispersion in the data and the Poisson model is used to predict the crash frequency ( 10 – 12 ).…”

mentioning

confidence: 99%

Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate: Case Study of Hamedan, Iran

Almasi,

Bakhshi Lomer,

Khaksar

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Road crashes are a major cause of fatalities worldwide, posing significant challenges for road-safety experts in selecting appropriate crash-frequency estimation models. This study introduces localized safety performance functions (C-SPFs), which explore the spatial variation of crash frequency and the spatial correlation between dependent variables. The exploratory spatial regression method is employed to identify optimal spatial associations. The study further predicts crashes using geographically weighted Poisson regression (GWPR) and generalized Poisson regression. Results indicate that C-SPFs offer greater accuracy than do models calibrated solely on annual average daily traffic. Moreover, the proposed model is especially relevant for jurisdictions facing higher heavy-vehicle traffic and frequent crashes. The development of C-SPFs and the use of GWPR provide valuable tools for policymakers and road-safety experts in enhancing crash-frequency estimation accuracy. Implementing these techniques can aid in prioritizing safety measures and countermeasures, especially in regions with significant heavy-vehicle traffic and crash occurrences. Additionally, the integration of spatial-analysis techniques and localized models can lead to more effective transportation planning and targeted road-safety interventions, ultimately contributing to reducing the burden of road crashes on a global scale.

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Crash analysis and development of safety performance functions for Florida roads in the framework of the context classification system

Cited by 10 publications

References 20 publications

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

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

Safety in Traffic Management Systems: A Comprehensive Survey

Using Spatial Modeling for Identifying Determinants Influencing Crash Estimate: Case Study of Hamedan, Iran

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