Using Macrolevel Collision Prediction Models in Road Safety Planning Applications

Lovegrove, Gord; Sayed, Tarek

doi:10.3141/1950-09

Cited by 31 publications

(20 citation statements)

References 9 publications

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“…Wang et al (2) reported on a 2006 study by Lovegrove and Sayed in which it was assumed that errors that occurred because of traditional aggregation were "reasonably acceptable and would not significantly influence the aggregation of data into the TAZ level" (30). However, Lovegrove and Sun in 2010 considered prorating crashes that occurred at intersections and zonal boundaries to minimize allocation bias (3).…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…walking and cycling); and, 2) By promoting safer intersections than just four-way intersections, including roundabouts and T-intersections (Elvik, 2003;Lovegrove and Sayed 2006;Tanner, 1953;. What follows is a scientific review of the STS principles and how the FG design reflects them.…”

Section: R a F T 4 Sustainable Transport Safety Principlesmentioning

confidence: 99%

Building sustainably safe and healthy communities with the Fused Grid development layout

et al. 2015

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“…The models developed in this study are unique, as they incorporate an actual bicycle exposure indicator, that is, the number of bicycle kilometers traveled (BKT), unlike similar studies, which rely on exposure proxies, such as pollution or the length of the bicycle network. communitywide proactive planning for road safety, a growing body of research on the development of macrolevel CPMs has been produced (9)(10)(11)(12)(13)(14). Macrolevel CPM input data have replaced the single-facility, individual link and node data used for microlevel CPM development and prediction and are aggregated to the level of the traffic analysis zone (TAZ).…”

mentioning

confidence: 99%

“…For model development, generalized linear regression with the assumption of a nonnormal error structure distribution, usually a Poisson or a negative binomial error structure, has become the norm described in recent literature, as it overcomes the limitations of linear regression models and produces a better fit to the observed collision data (13,24). Previous research has noted that the model form should satisfy the following conditions ( 25): (a) the model must produce logical results; for example, at zero exposure, collisions must equal zero; and (b) to use generalized linear regression, a recognized link function that can linearize the model form to estimate coefficients during the generalized linear regression process must be present (16,24,25).…”

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confidence: 99%

Developing Macrolevel Collision Prediction Models to Evaluate Bicycle Safety in Vancouver, British Columbia, Canada

Popescu

Sayed

2017

Transportation Research Record: Journal of the Transportation R

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To encourage greener cities while reducing the impacts of the transportation system—such as impacts on climate change, traffic congestion, and road safety—governments have been investing in sustainable modes of transportation, such as cycling. A safe and comfortable cycling environment is critical to encourage bicycle trips because cyclists are usually subject to greater safety risks. Engineering approaches to road safety management have traditionally addressed road safety by reacting to existing collision records. For bicycle collisions, which are rare events, a proactive approach is more appropriate. This study described the use of bicycle-related macrolevel (i.e., neighborhood or zonal-level) collision prediction models as empirical tools in road safety diagnosis and planning. These models incorporated an actual bicycle exposure indicator (the number of bicycle kilometers traveled). The macrolevel bicycle–vehicle collisions models were applied at the zonal level to a case study of Vancouver, British Columbia, Canada. Collision-prone zones in Vancouver were identified, and the highest-ranked zones were diagnosed to identify bicycle safety issues and to recommend potential safety countermeasures. The findings from this study suggest that the safety issues may be a result of high density and commercial land use type, coupled with a high traffic volume, particularly on arterial routes, and high bicycle volumes on routes with mixed vehicle and bicycle traffic. The case study demonstrated the use of the models to enhance bicycle safety proactively.

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Using Macrolevel Collision Prediction Models in Road Safety Planning Applications

Cited by 31 publications

References 9 publications

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

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

Building sustainably safe and healthy communities with the Fused Grid development layout

Developing Macrolevel Collision Prediction Models to Evaluate Bicycle Safety in Vancouver, British Columbia, Canada

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