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.

Owing to its mathematical elegance and empirical accuracy, the speed‐density model is critical in solving macroscopic traffic problems. This study developed an improved general‐logistic‐based speed‐density model, which is a new method in macroscopic traffic flow theory. This article extensively discusses the properties of the general‐logistic‐based speed‐density model. The physical meanings and values of all the parameters were determined based on the effect of heavy vehicles and the method for the linear and nonlinear regression analysis. The accuracy and versatility of the developed model were also found to be excellent based on the field data and relative error.

Driving fatigue is one of the main causes of traffic accidents. Thus, to prevent traffic accidents and ensure traffic safety, the properties of driving fatigue at the wheel must be determined. The Qinghai–Tibet Plateau in China is known for its high elevation, causing hypoxia, and presence of severely cold areas; all these easily lead to fatigue during driving. This, in turn, seriously affects the traffic safety on the high-altitude highway. Therefore, the factors leading to driving fatigue and the influence of high-altitude on driving fatigue affecting the driver must be further studied. In this study, we classified and quantified driving fatigue according to the driving fatigue degree. We determined three levels of driving fatigues (i.e., mild, moderate, and severe fatigues) to present their influence on drivers. Our study shows that in this high-altitude area, drivers became fatigued within a significantly shorter time.

Crop models are important for understanding and regulating agroecosystems. Although the CERES-Wheat model is an important tool for winter wheat research, it has some limitations under water stress conditions. To narrow the gap, this study aimed to improve the performance of the CERES-Wheat model under water stress in arid and semi-arid regions based on the winter wheat experimental data from 2012 to 2014. The Priestley–Taylor (PT) and FAO56 Penman–Monteith (PM) equations were used to calculate the reference crop evapotranspiration and further modified the crop coefficient of the CERES wheat model to improve the simulation accuracy of crop yield and evapotranspiration under water stress conditions. The results showed that: water stress before jointing seriously affected the accuracy of the CERES-Wheat model in simulating biomass and grain yield, so it was necessary to improve the original model. In the original and improved models, the accuracy of the PM equation was lower than that of PT. In addition, the simulation accuracy of the improved model was higher than that of the original model (the average RMAE and RRMSE are less than 30%). In general, among the four scenarios, the PT equation for calculating crop reference evapotranspiration and crop coefficient had the best performance. Water stress occurred at the heading and grain filling stages, and the simulated biomass was in good agreement with the observed results, which better simulated the soil water content under water stress at the later growth stages. Therefore, the change in water stress response function had positive effects on winter wheat growth under simulated water stress conditions. This study provided a reference for applying the CERES-Wheat model in arid and semi-arid areas.

The reduction in incident-induced delays on freeways is a main objective of transportation management. The use of travel time estimation model for freeway segments is an important method for estimating delays resulting from incidents on freeways. In this study, freeways with temporary partial lane closures were considered to simulate traffic accidents occupying lanes. Travel time, traffic volumes, and speeds under various traffic conditions on a few typical Chinese freeway segments under regular and simulated accident conditions were investigated through field experiments. The collected traffic data collected were used to establish travel time models based on the Bureau of Public Roads (BPR) function for basic freeway segments under both regular and accident conditions, and to obtain the model parameters. The results demonstrate that the calibrated BPR models established in this study fit the data well. In addition, this study proposes an application method for the established travel time models by which variations in travel time can be estimated rapidly and easily. The results of this study can be used to reduce travel time for road users and contribute to decision making of transportation management systems to improve traffic efficiency after incidents.

The highway tunnel plays a critical role in highway traffic flow, yet its sections are particularly susceptible to traffic accidents. The research shows that the safety measures in the tunnel have a certain effect on improving the safety in the tunnel, but there is a lack of evaluation methods for the use effect of safety measures in the tunnel. To study the application effect of safety measures in tunnels (mainly strobe lights and information boards), this paper takes the driver’s subjective feelings and vehicle speed changes as indicators to evaluate the application effect of safety facilities. The Xingshuliang Tunnel in Shaanxi Province, which has been operated and meets the test standards, is used as the test site, and the driver between the Yaozhou and Huangling sections is randomly selected as the test object for data collection. Subjective feelings are mainly obtained by social survey methods to obtain data samples, and the driving speed is collected by NC2000, non-contact five-wheel instrument, video recorder and other equipment. The statistical analysis method is used to study the driving speed of each section inside and outside the tunnel and the driver’s response. According to the changing trend of speed, the weight of each test section is calculated by the combination of analytic hierarchy process and quantitative statistical method, and the comprehensive influence degree of safety measures is evaluated. The results show that both the strobe light and the information board induce the driver to reduce the driving speed by 3.1%, which can effectively reduce the driving speed. The strobe light mainly acts on the tunnel entrance and the inside of the tunnel, with a maximum influence range of 205 m. The information board has the greatest effect at the tunnel entrance, with a maximum influence range of 200 m. The above results provide a useful reference for the arrangement of safety measures and put forward the arrangement method of tunnel safety measures in combination with the conclusion, to help improve the safety of the driving environment in the tunnel.