A framework of boundary collision data aggregation into neighbourhoods

Cui, Ge; Wang, Xin; Dae-Won, Kwon，

doi:10.1016/j.aap.2015.06.003

Cited by 15 publications

(10 citation statements)

References 14 publications

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“…However, the efficiency of macro-level traffic safety analysis may be subject to the well-known modifiable areal unit problem Lee et al, 2014b;Xu et al, 2014) and boundary issue Cui et al, 2015). From another perspective, the safety problem is anyhow a microscopic problem and the direct contributing factors could be related to micro-level factors for a specific road segment or intersection, or the driver-vehicle units involved.…”

Section: Introductionmentioning

confidence: 98%

Macro and micro models for zonal crash prediction with application in hot zones identification

Huang

Song

et al. 2016

Journal of Transport Geography

109

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Section: Introductionmentioning

confidence: 98%

Macro and micro models for zonal crash prediction with application in hot zones identification

Huang

Song

et al. 2016

Journal of Transport Geography

109

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“…Research [35] found that children in Canada are at a much higher risk of major injuries when involved in a back-over collision. The physical aspect of a collision is studied in a few cities in Canada, such as Edmonton [36] and Ottawa [37]. These studies analyse the proximity of two vehicles and its effect on the collisions.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Shanshal

Babaoglu

Başar

2020

PROMET

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Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

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“…Cui et al proposed a collision density ratio (CDR) method to aggregate boundary crashes (9). They conducted a case study of two areas in the city of Edmonton, Alberta, Canada, and compared the CDR method with two other methods (the half-to-half and oneto-one ratio methods) by developing nonspatial negative binomial (NB) crash prediction models and a chi-square test.…”

mentioning

confidence: 99%

Geographical Boundary Dependency Versus Roadway Hierarchy in Macroscopic Safety Modeling: Analysis with Motor Vehicle Crash Data

Siddiqui

Abdel-Aty

2016

Transportation Research Record: Journal of the Transportation R

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This study investigated two methodologies for allocating crashes within a zone and compared them in light of crash prediction models. The assumption that crashes within a zone were influenced only by the characteristics of that zone was examined with motor vehicle crash data. Models were specified by a hierarchical structure with submodels that provided separate estimates of covariate sets for crashes that occurred at or near boundaries and that occurred within a zone away from zonal boundaries. The proposed model structure was compared with spatial and nonspatial statistical models fitted for a wide array of independent variables. Two layers of zonal influences were investigated: ( a) the influence of immediate neighbors and ( b) the additional influence of neighbors of immediate neighbors. The roadway network hierarchy was considered in model sublevel specifications. Comparison of the candidate models showed that the complex nested model structure specifying interior and boundary crashes did not necessarily provide the best fit. Spatial models with submodels based on roadway network hierarchy were found to have the best goodness-of-fit measures for both total and severe crashes. Significant variables were common between total and severe crashes. However, only on-system crashes were significantly associated with the exposure variable, indicating that socioeconomic characteristics and land use types play an important role in off-system crash propensity.

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A framework of boundary collision data aggregation into neighbourhoods

Cited by 15 publications

References 14 publications

Macro and micro models for zonal crash prediction with application in hot zones identification

Macro and micro models for zonal crash prediction with application in hot zones identification

Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Geographical Boundary Dependency Versus Roadway Hierarchy in Macroscopic Safety Modeling: Analysis with Motor Vehicle Crash Data

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