Relating Safety and Capacity on Urban Freeways

Daniel, Janice; Maina, Eugene Vida

doi:10.1016/j.sbspro.2011.04.453

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…However, the crash data are often over-dispersed, that is, the mean is less than the variance, which violates the basic assumption of the Poisson model that the mean is equal to the variance (24). The NB model, in contrast, can deal with over-dispersion crash data because of the introduction of an error term in the equation (25).…”

Section: Traffic Safety Models For Freewaysmentioning

confidence: 99%

Analyzing Freeway Safety Influencing Factors Using the CatBoost Model and Interpretable Machine-Learning Framework, SHAP

Li,

Wang,

Yang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Exploring and analyzing safety influencing factors can guide targeted traffic safety management. Traditional traffic safety models are aimed at specific data problems and making adjustments to the model structure, which lack focus on predictive ability and have limited information on the analysis of influencing factors. In recent years, machine-learning methods have opened new avenues in modeling that have higher prediction accuracy, can identify complex nonlinear relationships, and can overcome over- and under-dispersion and correlation. Machine-learning methods, however, pose the problem of limited interpretability. The interpretable machine-learning framework SHAP can be an effective solution, which can not only reflect the influence of features in each sample but also generate global interpretation. This study established gradient boosting models including the CatBoost and XGBoost models as traffic safety models, which were compared with a traditional NB regression model and a zero-inflated negative binomial regression model. SHAP was used to analyze several safety influencing factors, including geometric design features, traffic operation characteristics, time of day, and land use. Results confirmed that the CatBoost model has better prediction ability and is a more suitable traffic safety model than the traditional negative binomial regression model. Among the key findings were that ramp type is the most important factor in freeway crash frequency; curve presence has a great positive impact, while truck proportion has a great negative impact; and traffic volume is highly correlated with truck proportion. These findings can provide theoretical support for safety operation management and targeted improvement measures for freeways.

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Section: Traffic Safety Models For Freewaysmentioning

confidence: 99%

Analyzing Freeway Safety Influencing Factors Using the CatBoost Model and Interpretable Machine-Learning Framework, SHAP

Li,

Wang,

Yang

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…With respect to the best mobility measure to relate to crash rates, the literature indicates that the v/c or traffic density (Chang et al, 2000;Daniel and Maina, 2011;Lord et al, 2005) is preferable over flow. The use of traffic density has the additional advantage of better handling undersaturated and oversatured flows (Martin, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…Likewise, by modeling the relationship between v/c and crash rates on freeways using the Poisson model, Daniel and Maina (2011) showed that geometry characteristics that affect capacity also affect safety. Arona et al (2015) went further, by investigating the possible effect of daylight, average traffic speed, occupancy, average headways and combinations of speed, relative speed and gaps in ___________________________________________________________________________ Literature Review establishing relationships between prevailing traffic conditions and crash rates on French urban freeways.…”

Section: Safety-operations Relationshipmentioning

confidence: 99%

Relationship between traffic operations and road safety

Andrade¹

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Since before the release of the Highway Safety Manual research has been indicating the need to incorporate mobility and control aspects to road safety analysis. The first part of this work developed and implement in an existing computational engine a signal timing optimization method that considers mobility, safety, and emissions measures simultaneously. A sensitivity analysis was conducted to provide insight on the practical effects and order of relevance of 20 key input variables. Mobility improvement performance usually coincides with emissions improvements, but sometimes at the expense of safety. The second part of this work investigated the relationship between hourly traffic density and crash rates on Brazilian expressways with different characteristics, based on a database containing over 20,000 crashes and more than 35 million traffic volume observations and. The resulting curves for urban expressways follow a U shape, with minimum values associated with LOS B to C, while the relationships for rural expressways were found to be continuously increasing, suggesting that low volume rural roads are safer than the higher volume ones. The analysis of other influencing factors revealed that nighttime conditions, weaving segments and urban multilane highways could be related to higher crash rates. The third part of the project extends the analysis to crash severity modeling, using an ordered response choice model. The framework that better fit this database led to the development of two different models: single-vehicle crashes (SV) and multiple-vehicle crashes (MV), since the factors that explain the severity of crashes varies widely between these models. For instance, guardrails and barriers proved to effectively reduce severity for SV crashes, for which runoffs are the most severe crash type. The unique database used in this study also allowed for an investigation of the influence of prevailing traffic conditions on crash severity, while still controlling for all other factors. The results suggested that multiple-vehicle crash severity is negatively related with traffic density, while single-vehicle crashes are more closely related to speed. The findings of this work have implications to policy and design decisions, and the produced equation could be incorporated to active traffic management (ATM) and HCM reliability analysis.

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