Excessively high brake temperature may lead to brake fading and failure, resulting in truck runaway down a graded descent. The accurate prediction of the changes in the brake-drum temperature on downgrades can provide theoretical guidance for truck accident countermeasures, such as determining the maximum safe speeds and the locations of truck escape ramps. By analyzing truck accident mechanisms during graded descents and selecting the initial brake-drum temperature, downgrade percentage and length, and the truck weight and speed as independent variables, with the brake-drum temperature as a dependent variable, the downgrade process of a truck can be divided into two stages: speed control at the grade section and emergency braking at the grade end. The energy conversion process in the forms of brake and nonbrake forces in the two stages are analyzed, based on the energy conservation law. A prediction model for the brake-drum temperature of large trucks on consecutive mountain downgrade routes is established, using the heat quantity formula. The model’s numerical calculation explicitly demonstrates the effect of all the variables. The brake-drum temperature is positively related to the truck weight, and the percentage and length of the downgrade. The temperature increase in the control speed phase is negatively related to the truck speed, whereas that in the emergency braking phase is positively related. The relationship curves between the variables show that the brake-drum temperature does not change significantly with the truck speed. However, the brake-drum temperatures, under different truck weights, downgrade lengths, and percentages, at the same speed, differ considerably. Compared to the existing empirical fitting model based on specific test data, the proposed model clearly shows the effects of main variables. The proposed model can be used for determining the safe truck speeds and locations of truck escape ramps to provide guidance for drivers and builders.
Traffic flow patterns severely impact vehicle carbon emissions. A field test was conducted in this study to obtain fuel consumption and traffic volume data under various traffic flow patterns and to explore the relationship between traffic flow patterns and vehicle carbon emissions. Carbon emission data were obtained via the indirect carbon emission accounting method proposed by the Intergovernmental Panel on Climate Change. Carbon emission prediction models for diesel trucks and gasoline passenger cars were established respectively with volume to capacity ratio as an explanatory variable. The results show that carbon emissions are highest under the congested flow conditions, followed by unstable flow, free flow, and steady flow. The relationship between the volume to capacity ratio and carbon emissions is a cubic curve function. The carbon emissions of trucks and passenger cars with a volume to capacity ratio of 0.4 to 0.5 are relatively small. The proposed carbon emissions models effectively quantify the carbon emissions of vehicles under different traffic flow patterns. The results of this study may provide data to support and a workable reference for expressway operation management and future low-carbon expressway expansion construction projects.
The geometric longitudinal slope line of a given road significantly effects the carbon emissions of vehicles traversing it. This study was conducted to explore the carbon emission rules of passenger cars on various highway slopes. The law of conservation of mechanical energy, the first law of thermodynamics and the vehicle longitudinal dynamics theory were utilized to determine the influence of slope design indicators on fuel consumption. The energy conversion, fuel consumption, and carbon emission models of passenger cars on a flat straight road, uphill road, and downhill road sections were derived accordingly. Two types of passenger cars were selected for analysis. A field test was carried out to verify the proposed model where the vehicle maintained a cruise speed on flat straight road, uphill road and downhill road with equal gradient and mileage, and continuous longitudinal slope to gather fuel consumption data. The proposed model showed strong accuracy and a maximum error of 9.97%. The main factor affecting the vehicle’s carbon emissions on the continuous longitudinal slope was found to be the average gradient. For a round-trip longitudinal slope with a small gradient, the main factor affecting the vehicle’s carbon emissions is speed: higher speed results in higher carbon emissions. The results of this study are likely to provide the data for support and a workable reference for the low-carbon highway design and operation.
Carbon emissions are the primary reason that contributes to global warming. The gradient has a significant impact on the carbon dioxide (CO 2) emissions produced by trucks. The aim of the current paper is to propose a carbon emission quantification model for diesel trucks on longitudinal slope sections and investigate the influence of gradient on the carbon emissions of trucks for use in the low-carbon highway design. The law of conservation of mechanical energy, the first law of thermodynamics, and the vehicle longitudinal dynamics theory were adopted for deriving the carbon emission model of the trucks on the flat, uphill, downhill and round-trip longitudinal slope segments. Three kinds of common trucks were chosen to conduct the field test. Following the test data, the model demonstrates a high accuracy. The minimum gradient which is expected to impact carbon emissions of trucks on the round-trip longitudinal slope sections was the balance gradient as revealed. The gradient of the longitudinal slope is required to be avoided to be greater in comparison with the balance gradient for the achievement of the two-way traffic low carbon operation on a highway. The results of this study are valuable to researchers interested in low carbon road design and low carbon transportation control.
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