A Comparative Study of Count Models: Application to Pedestrian-Vehicle Crashes Along Malaysia Federal Roads

Hosseinpour, Mehdi; Prasetijo, Joewono; Yahaya, Ahmad Shukri; Ghadiri, Seyed Mohammadreza

doi:10.1080/15389588.2012.736649

Cited by 23 publications

(17 citation statements)

References 44 publications

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“…Considerable studies have been conducted on estimating pedestrian crash prediction models (Lee and Abdel-Aty, 2005;Al-Ghamdi, 2002;Garder, 2004;Zajac and Ivan, 2003;Lyon and Persaud, 2002;Hosseinpour et al, 2013). Some have focused on understanding pedestrian safety problems at signalized intersections with different types of pedestrian crossing behavior.…”

Section: Introductionmentioning

confidence: 99%

Safety effects of exclusive and concurrent signal phasing for pedestrian crossing

Zhang

Mamun

Ivan

et al. 2015

Accident Analysis & Prevention

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Section: Introductionmentioning

confidence: 99%

Safety effects of exclusive and concurrent signal phasing for pedestrian crossing

Zhang

Mamun

Ivan

et al. 2015

Accident Analysis & Prevention

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“…As an alternative to zero-inflated models, hurdle models have some inherent advantages on model assumptions. By relaxing the structural zero assumption, hurdle models assume that all zeroes in the crash data are sampling zeroes [29,30,31,32]. In contrast to the structural zero assumption presuming an inherently safe condition with no crashes, sampling zero assumption implies that all segments have crash potential and the zero state does not remain permanently on any road segment [29,30,31,32].…”

Section: Introductionmentioning

confidence: 99%

“…By relaxing the structural zero assumption, hurdle models assume that all zeroes in the crash data are sampling zeroes [29,30,31,32]. In contrast to the structural zero assumption presuming an inherently safe condition with no crashes, sampling zero assumption implies that all segments have crash potential and the zero state does not remain permanently on any road segment [29,30,31,32]. Existing crash studies using hurdle models [29,30,31,32,33] were primarily developed based on cross-sectional data (as opposed to panel data) and did not consider random effects.…”

Section: Introductionmentioning

confidence: 99%

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data

Chen

et al. 2016

IJERPH

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Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.

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“…However, they have rarely been adopted in road safety literature (Boucher, Santolino 2010;Hosseinpour et al 2013;Son et al 2011). …”

Section: Hurdle Modelsmentioning

confidence: 98%

Evaluating the Effects of Road Geometry, Environment, and Traffic Volume on Rollover Crashes

et al. 2016

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There are a number of factors that cause motor vehicles to rollover. However, the impacts of roadway characteristics on rollover crashes have rarely been addressed in the literature. This study aims to apply a set of crash prediction models in order to estimate the number of rollovers as a function of road geometry, the environment, and traffic conditions. To this end, seven count-data models, including Poisson (PM), negative binomial (NB), heterogeneous negative binomial (HTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models, were developed and compared using crash data collected on 448 segments of Malaysian federal roads. The results showed that the HTNB was the best-fit model among the others to model the frequency of rollovers. The variables Light-Vehicle Traffic (LVT), horizontal curvature, access points, speed limit, and centreline median were positively associated with the crash frequency, while UnPaved Shoulder Width (UPSW) and Heavy-Vehicle Traffic (HVT) were found to have the opposite effect. The findings of this study suggest that rollovers could potentially be reduced by developing road safety countermeasures, such as access management of driveways, straightening sharp horizontal curves, widening shoulder width, better design of centreline medians, and posting lower speed limits and warning signs in areas with higher rollover tendency.

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A Comparative Study of Count Models: Application to Pedestrian-Vehicle Crashes Along Malaysia Federal Roads

Cited by 23 publications

References 44 publications

Safety effects of exclusive and concurrent signal phasing for pedestrian crossing

Safety effects of exclusive and concurrent signal phasing for pedestrian crossing

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data

Evaluating the Effects of Road Geometry, Environment, and Traffic Volume on Rollover Crashes

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