Temporal transferability and updating of zonal level accident prediction models

Hadayeghi, Alireza; Shalaby, Amer; Persaud, Bhagwant; Cheung, Carl

doi:10.1016/j.aap.2005.12.003

Cited by 87 publications

(30 citation statements)

References 5 publications

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“…Linear regression (Clark and Cushing, 2004); linear regression with a spatial lag parameter (Levine et al, 1995); Poisson models (Chatterjee et al, 2004); negative binomial (NB) models (Hadayeghi et al, 2003(Hadayeghi et al, , 2007Maher and Summersgill, 1996;Abdel-Aty and Radwan, 2000;Miaou and Lord, 2003;Qadeer Memon, 2006;Mitra and Washington, 2007;Noland and Quddus, 2004); zero-inflated Poisson and NB models (ZIP/ZINB) (Qin et al, 2004;Shankar et al, 1997); simultaneous estimation of NB models (de Guevara et al, 2004); full Bayes hierarchical models with spatial effects (Aguero-Valverde and Jovanis, 2006;Quddus, 2008), geographically weighted regression (GWR) (Hadayeghi et al, 2003); log-linear models using ordinary least squares (OLS) estimation technique (Washington et al, 2006;Wier et al, 2009) were used successfully by previous researchers in different approaches to crash studies. Hadayeghi et al (2006) tested temporal transferability of the macro-models and concluded that model forecasts are within acceptable range in application context.…”

Section: Model Postulationmentioning

confidence: 97%

Aggregate crash prediction models: Introducing crash generation concept

Naderan

Shahi

2010

Accident Analysis & Prevention

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Section: Model Postulationmentioning

confidence: 97%

Aggregate crash prediction models: Introducing crash generation concept

Naderan

Shahi

2010

Accident Analysis & Prevention

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“…The literature is replete with studies showing that areas with more residents, more employment and more arterial lane miles experience more crashes (Hadayeghi et al, 2003(Hadayeghi et al, , 2006Kmet et al, 2003;Ladro´n de Guevara et al, 2004;Levine et al, 1995aLevine et al, , 1995bLovegrove and Sayed, 2006). Such studies may be useful for crash prediction on individual facilities.…”

Section: Crash Risk Factorsmentioning

confidence: 99%

Urban sprawl as a risk factor in motor vehicle crashes

2014

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A decade ago, compactness/sprawl indices were developed for metropolitan areas and counties which have been widely used in health and other research. In this study, we first update the original county index to 2010, then develop a refined index that accounts for more relevant factors, and finally seek to test the relationship between sprawl and traffic crash rates using structural equation modelling. Controlling for covariates, we find that sprawl is associated with significantly higher direct and indirect effects on fatal crash rates. The direct effect is likely due to the higher traffic speeds in sprawling areas, and the indirect effect is due to greater vehicle miles driven in such areas. Conversely, sprawl has negative direct relationships with total crashes and non-fatal injury crashes, and these offset (and sometimes overwhelm) the positive indirect effects of sprawl on both types of crashes through the mediating effect of increased vehicle miles driven. The most likely explanation is the greater prevalence of fender benders and other minor accidents in the low speed, high conflict traffic environments of compact areas, negating the lower vehicle miles travelled per capita in such areas.

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“…Hadayeghi et al (Hadayeghi 2009;Hadayeghi et al 2003;Hadayeghi et al 2006;Hadayeghi et al 2007;Hadayeghi et al 2010aHadayeghi et al , 2010b have been working on ZCPMs for several years. In one of their first studies, it was shown that the number of accidents in a TAZ increases when the VKT, major and minor road length, total employed labor force, household population, and intersection density increase whereas it decreases with a higher posted speed and a higher level of congestion in the TAZ (Hadayeghi et al 2003).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In one of their first studies, it was shown that the number of accidents in a TAZ increases when the VKT, major and minor road length, total employed labor force, household population, and intersection density increase whereas it decreases with a higher posted speed and a higher level of congestion in the TAZ (Hadayeghi et al 2003). Hadayeghi et al (2006) investigated the temporal transferability of the ZCPMs by applying models constructed on 1996 data to predict the NOCs for each TAZ in 2001 for the City of Toronto. They concluded that the models are not transferable statistically but that VKT, socioeconomic and demographic parameters are significantly stable over time.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling

Pirdavani

Brijs

Bellemans

et al. 2013

Accident Analysis & Prevention

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Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system's effectiveness by changing travel behavior. The primary objective to implement such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to evaluate the traffic safety impact of conducting a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) in Flanders, Belgium. Since TDM strategies are usually conducted at an aggregate level, Crash Prediction Models (CPMs) should also be developed at a geographically aggregated level. Therefore Zonal Crash Prediction Models (ZCPMs) are considered to present the association between observed crashes in each zone and a set of predictor variables. To this end, an activitybased transportation model framework is applied to produce exposure metrics which will be used in prediction models. This allows us to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models unlike traditional models in which the impact of TDM strategies are assumed. The crash data used in this study consist of fatal and injury crashes observed between 2004 and 2007. The network and socio-demographic variables are also collected from other sources. In this study, different ZCPMs are developed to predict the Number of Injury Crashes (NOCs) (disaggregated by different severity levels and crash types) for both the null and the fuel-cost increase scenario. The results show a considerable traffic safety benefit of conducting the fuel-cost increase scenario apart from its impact on the reduction of the total Vehicle Kilometers Travelled (VKT). A 20% increase in fuel price is predicted to reduce the annual VKT by 5.02 billion (11.57% of the total annual VKT in Flanders), which causes the total NOCs to decline by 2.83%.

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Temporal transferability and updating of zonal level accident prediction models

Cited by 87 publications

References 5 publications

Aggregate crash prediction models: Introducing crash generation concept

Aggregate crash prediction models: Introducing crash generation concept

Urban sprawl as a risk factor in motor vehicle crashes

Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling

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