Analysis of Truck Braking Accidents

Limpert, Rudolf; Andrews, Dennis F.

doi:10.4271/870504

Cited by 11 publications

(6 citation statements)

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“…For special road sections where the safe speed is affected by sight distance, the ''speed profile'' can be used to compare multiple safe speeds and the minimum value selected. (8) The safe speed prediction model is established under the assumption of good weather conditions, without limiting tire-pavement friction.…”

Section: Methodsmentioning

confidence: 99%

“…The existing brake drum temperature prediction model assumed that there is no temperature gradient inside the brake drum; therefore, in each Riemann integral approximation interval, the outer surface temperature of the brake drum, t wjk is equal to the brake drum temperature, T j , that is, the calculation result of the k À 1 ð Þ th Riemann integral interval, T j is used as the input parameter of the k th calculation interval. According to existing research results, T lim 1 and T lim 2 are approximately 315°C and 426°C, respectively (8,31). It should be noted that in engineering applications, the actual truck driving speed may differ from the recommended speed owing to uncertainties such as the driver's perception, driving skills, and personality.…”

Section: Speed Recovery Stagementioning

confidence: 99%

“…Some researchers have examined the causes of truck collision accidents on continuous downhill sections. The causes are complex, but some of the major ones include brake failure resulting from high temperatures (8,9); lack of downhill driving experience, brake drum problems, and incomplete traffic signs (10,11); driving above the recommended speed limit (12,13); horizontal alignment (14,15); curve length, curve curvature, longitudinal gradient, and average daily traffic volume (16); length of the longitudinal slope, annual average daily traffic volume, gradient of the longitudinal slope, number of lanes, and width of the right shoulder (17); failing to downshift, improper gear or very high speed (82%), driver lacking driving experience or being unfamiliar with road conditions (43%), incomplete traffic signs (14%), poor braking or inappropriate braking operation (36%), and drunk driving or fatigue driving (21%) (18). These research findings provide a foundation to formulate and improve truck accident prevention strategies on continuous downhill sections of highways.…”

mentioning

confidence: 99%

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Safe Speed Prediction Model for Heavy Trucks on Consecutive Mountain Downgrade Routes Based on Energy Conservation Law

Yan

Han

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Safe vehicle speed estimation is essential for road traffic management and traffic safety. Improper truck speed on downgrades may lead to brake fade and/or failure, resulting in severe truck accidents such as runaway or out-of-control. This paper presents a model for predicting the safe speed of trucks from the perspective of preventing brake failure. The proposed model can be used to control the speed limit or as a reference for the revision of highway geometric design standards. In addition, by considering the downgrade design speeds recommended by AASHTO, the study offers an approach to deciding the downgrades that need escape ramps, those that need different speed limits, and those that may need exclusions for trucks over a certain weight. First, we simplify the downgrade driving status of a heavy truck into three types: speed control, emergency braking, and speed recovery according to the speed change status. The prediction model of the brake drum temperature with speed variables has been established in our previous study based on Newton’s energy conservation law. Finally, the boundary conditions for brake fade and failure temperature are determined. Results of parameter analysis show that the gross truck weight is the most significant variable. Other significant variables are grade, grade length, and emergency braking time. Compared with previous research results, the advantage of this method is that the permitted speed can be derived using the brake temperature prediction model supported by theory, without the need for extensive field tests.

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Section: Methodsmentioning

confidence: 99%

Section: Speed Recovery Stagementioning

confidence: 99%

mentioning

confidence: 99%

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Safe Speed Prediction Model for Heavy Trucks on Consecutive Mountain Downgrade Routes Based on Energy Conservation Law

Yan

Han

et al. 2021

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…p μ (the unit is Mpa) is up to the peak and down slightly. When there is pressure to the brakes, slippage is usually 20% 50% of the peak [ 23 ], and then a slight upward trend appears. The reason for p μ is at early stage of the brake, p μ is increasing with the increases of tire-road adhesion coefficient φ ; p μ is declining when there is a big difference between the adhesion coefficient and sliding adhesion coefficient.…”

Section: Methodsmentioning

confidence: 99%

Driving style recognition method using braking characteristics based on hidden Markov model

Deng

Lyu

et al. 2017

PLoS ONE

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Since the advantage of hidden Markov model in dealing with time series data and for the sake of identifying driving style, three driving style (aggressive, moderate and mild) are modeled reasonably through hidden Markov model based on driver braking characteristics to achieve efficient driving style. Firstly, braking impulse and the maximum braking unit area of vacuum booster within a certain time are collected from braking operation, and then general braking and emergency braking characteristics are extracted to code the braking characteristics. Secondly, the braking behavior observation sequence is used to describe the initial parameters of hidden Markov model, and the generation of the hidden Markov model for differentiating and an observation sequence which is trained and judged by the driving style is introduced. Thirdly, the maximum likelihood logarithm could be implied from the observable parameters. The recognition accuracy of algorithm is verified through experiments and two common pattern recognition algorithms. The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style.

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“…While skidding contributes to a substantial portion of heavy vehicle crashes and is therefore worth looking into vehicle safety studies, there are also many other factors relevant to truck stability and braking performance (Harwood, Torbic, Richard, Glauz, & Elefteriadou, 2003;Limpert & Andrews, 1987). Various studies have shown that the heavy vehicles' characteristic, loading condition, and speed play significant roles in road safety (Fancher & Campbell, 1995;Gobbi, Mastinu, & Previati, 2014;Seipel, Baumann, Hermanutz, & Winner, 2013).…”

Section: Introductionmentioning

confidence: 99%

Heavy Vehicle Multi-Body Dynamic Simulations to Estimate Skidding Distance

Zamzamzadeh

Saifizul

Ramli

et al. 2018

The Baltic Journal of Road and Bridge Engineering

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Abstract. The skid mark is valuable for accident reconstruction as it provides information about the braking behaviour drivers and the speed of heavy vehicles. However, despite its importance, there is currently no mathematical model available to estimate skidding distance (SD) as a function of vehicle characteristics and road conditions. This paper attempts to develop a non-linear regression model that is capable of reliably predicting the skidding distance of heavy vehicles under various road conditions and vehicle characteristics. To develop the regression model, huge data sets were derived from complex heavy vehicle multi-body dynamic simulation. An emergency braking simulation was conducted to examine the skidding distance of a heavy vehicle model subject to various Gross Vehicle Weight (GVW) and vehicle speeds, as well as the coefficient of friction of the road under wet and dry conditions. The results suggested that the skidding distance is significantly affected by Gross Vehicle Weight, speeds, and coefficient of friction of the road. The improved non-linear regression model provides a better prediction of the skidding distance than that of the conventional approach thus suitable to be employed as an alternative model for skidding distance of heavy vehicles in accident reconstruction.

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Analysis of Truck Braking Accidents

Cited by 11 publications

References 0 publications

Safe Speed Prediction Model for Heavy Trucks on Consecutive Mountain Downgrade Routes Based on Energy Conservation Law

Safe Speed Prediction Model for Heavy Trucks on Consecutive Mountain Downgrade Routes Based on Energy Conservation Law

Driving style recognition method using braking characteristics based on hidden Markov model

Heavy Vehicle Multi-Body Dynamic Simulations to Estimate Skidding Distance

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