Single-vehicle crashes along rural mountainous highways in Malaysia: An application of random parameters negative binomial model

Rusli, Rusdi; Boyle, Linda Ng; King, Mark; Voon, Wong Shaw

doi:10.1016/j.aap.2017.03.002

Cited by 62 publications

(29 citation statements)

References 31 publications

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“…A review of previous studies found that the indicators related to road design characters [11]- [22], traffic conditions [11]- [15] and pavement conditions [23]- [25] have significant impacts on traffic safety. First, the impacts of traffic conditions on crashes was reviewed.…”

Section: A Crash Contributing Factorsmentioning

confidence: 99%

Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

Tang

Cai

et al. 2020

IEEE Access

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Highway tunnels have a higher risk of crashing than open roads, which require a systematic approach to tunnel safety. However, previous research had the following problems: (1) Studies have largely focused on open roads, with very little research on tunnels. (2) The collected crash contributing factors involve narrow ranges, with very little tunnel crash data including both tunnel design features, traffic conditions and pavement conditions. (3) None of the studies considered both excess zero observations and unobserved heterogeneity with its interactions. To address these issues, this paper first established an appropriate tunnel dataset containing 3 to 5 years of crash data from several highways in China and the influence factors of tunnel design features, traffic conditions and pavement conditions. A correlated random parameters negative binomial Lindley (CRPNB-L) model that considers both excess zero observations and unobserved heterogeneity with its interaction effects was then proposed. Compared to the uncorrelated random parameters negative binomial Lindley (URPNB-L) model, fixed parameters negative binomial Lindley (FPNB-L) model and fixed parameters negative binomial (FPNB) model, the CRPNB-L model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity with its interactions by introducing correlated random parameters. In the comparisons, the CRPNB-L model achieves the best effects in the goodness-of-fit. Furthermore, the estimated results of the CRPNB-L model showed that segment length, traffic volume, proportion of class 5 vehicle (heavy trucks and trailers), tunnel entrance and exit segments, and steep uphill and downhill segments were associated with higher crash frequency, while curvature, tunnel length, pavement damage condition index (PCI) and skid resistance index (SRI) were associated with lower crash frequency. In addition, the random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and SRI were identified and their intercorrelations were analyzed. INDEX TERMS Tunnel safety, correlated random parameters, negative binomial Lindley model, tunnel design features, traffic conditions, pavement conditions.

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Section: A Crash Contributing Factorsmentioning

confidence: 99%

Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

Tang

Cai

et al. 2020

IEEE Access

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“…The research methods of the influence of road factors on vehicle rollover are mostly concentrated on accident-prone incidents [16,[18][19][20][21][22][23][24]. It is difficult to determine the underlying causes behind the formation of black spots, and the results are not intuitive.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Road Geometry on Vehicle Rollover and Skidding

Yin

Wen

Sun

et al. 2020

IJERPH

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This paper analyzes the influence of single and combined unfavorable road geometry on rollover and skidding risks of D-class mid-sized sport utility vehicles (SUVs) with front-wheel drive for roads with design speeds at 80 km/h. A closed-loop simulation model of human-vehicle-road interactions is established to examine the systematic influence of road geometry on vehicle rollover and skidding. The effects of different road geometry on rollover and skidding on SUVs are studied for pavement surface with good and poor friction when vehicles are in the action of steady state cornering. The rollover and skidding risks of the most unfavorable road segments are assessed. The critical wheel is defined by the threshold of skidding during curve negotiation. The results found that SUVs are not easy to rollover on the most unfavorable roads, regardless of good or poor friction of pavement surface. The safety margin of rollover is greater than that of skidding. The safety margin of skidding is minimal on poor friction roads. Therefore, for the sake of driving safety, it is not recommended to design the roads with these unfavorable road geometry combinations

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“…Available evidences suggested no straightforward correlation between relative humidity and RTIs (Gao et al, 2016; Theofilatos & Yannis, 2014). One study suggested that rainfall is positively associated with RTIs (Rusli et al, 2017; Theofilatos & Yannis, 2014). Conversely, our data revealed a significant negative correlation between relative humidity and monthly mild RTIs.…”

Section: Discussionmentioning

confidence: 99%

“…According to the World Health Organization (WHO), RTIs were the ninth major cause of death globally in 2015 and the primary reason for death among people aged between 15 and 29 years (WHO, 2015a; Zhang et al, 2015). More than 1.2 million people have died and nearly 50 million people are injured annually worldwide (Gao et al, 2016; Rusli et al, 2017; Xie et al, 2016; Zhang et al, 2015; Zhang et al, 2011). Low- and middle-income countries account for approximately 90% of global RTIs (Alam & Mahal, 2016; Gao et al, 2016; Rusli et al, 2017; Xie et al, 2016; Zhang et al, 2015; Zhang et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…More than 1.2 million people have died and nearly 50 million people are injured annually worldwide (Gao et al, 2016; Rusli et al, 2017; Xie et al, 2016; Zhang et al, 2015; Zhang et al, 2011). Low- and middle-income countries account for approximately 90% of global RTIs (Alam & Mahal, 2016; Gao et al, 2016; Rusli et al, 2017; Xie et al, 2016; Zhang et al, 2015; Zhang et al, 2011). Without appropriate improvement in policies to prevent road traffic accidents (RTAs), RTIs are predicted to become the seventh leading cause of death worldwide by 2030, overtaking HIV/AIDS (Xie et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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The association between meteorological variables and road traffic injuries: a study from Macao

Lio¹,

Cheong²,

Un³

et al. 2019

PeerJ

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ObjectiveCorrelation analysis and multiple linear regression analysis were conducted to estimate the influence of meteorological factors on road traffic injuries stratified by severity. Crash rate was defined as mean monthly road traffic accidents per 1,000 vectors.DesignEcological time-series study.SettingMacao traffic accident registry database between January 1st, 2001 and November 31st, 2016.ParticipantsIn total, 393,176 traffic accidents and 72,501 cases of road traffic injuries (RTIs) were enrolled; patients’ severity was divided into mild injury, required hospitalisation, and death.ExposureVariation of monthly meteorological factors.Main outcome measureWeather-condition-related road traffic accidents, injuries, and deaths.ResultsWindy weather significantly correlated with increased number of traffic accidents among all transport vectors (r = .375 to .637; p < 0.001). Multiple linear regression showed temperature (B = 0.704; p < 0.05) and humidity (B = − 0.537; p < 0.001) were independent factors for mild injury. The role of windy weather was relatively more obvious among patients with severe injuries (B = 0.304; p < 0.001) or those who died (B = 0.015; p < 0.001). A longer duration of sunshine was also associated to RTI-related deaths (B = 0.015; p < 0.001). In total, 13.4% of RTIs were attributable to meteorological factors and may be preventable.ConclusionThe World Health Organization stated that RTIs are a major but neglected public health challenge. This study demonstrates meteorological factors have significant effects on any degree of RTIs. The results may not be generalized to other climates or populations while the findings may have implications in both preventing injuries and to announce safety precautions regarding trauma and motor vehicle collisions to the general public by public agencies.

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Single-vehicle crashes along rural mountainous highways in Malaysia: An application of random parameters negative binomial model

Cited by 62 publications

References 31 publications

Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

The Influence of Road Geometry on Vehicle Rollover and Skidding

The association between meteorological variables and road traffic injuries: a study from Macao

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