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.

Road traffic safety can be ensured by preventing and controlling the potential risks in road traffic operations. The relevant literature was systematically reviewed to identify the research context and status quo in the road traffic operation risk prevention and control field and identify the key study contents needing further research. As research material, the related English and Chinese literature published between 1996 and 2021 (as of 31st December 2021) was obtained through the Web of Science Core Collection and Chinese Science Citation Database. These research materials include 22,403 English and 7876 Chinese papers. Based on the bibliometrics, this study used CiteSpace software to conduct keyword co-occurrence analysis in the field. The results show that the relevant research topics mainly covered the risks of drivers, vehicles, roads, and the traffic environment. In the aspect of driver risks, the studies focused on driving behavior characteristics. In terms of vehicle risks, the related studies were mainly about the vehicle control system, driving assistance system, hazardous material transportation, automated driving technology, safe driving speed, and vehicle collision prediction. For the road risks, the safe driving guarantee of high-risk road sections, driving risks at intersections, and safe road alignment design were the three study hotspots. In terms of traffic environment risks, identifying traffic risk locations and driving safety guarantees under adverse weather conditions were the two main research highlights. Moreover, mathematical modeling was the main method for studying road traffic operation risk. Furthermore, the impact of environmental factors on drivers, the emergency rescue system for road traffic accidents, the connection between automated driving technology and safe driving theory, and the man–machine hybrid traffic flow characteristics are the subjects needing further research.

Speed dispersion is an important indicator to portray the quality of traffic flow and is closely related to the road safety operation level. In order to clarify the influence of posted speed limits on the dispersion of traffic flow speed, three sections with speed limits of 80 km/h, 100 km/h and 120 km/h on the same expressway were selected for observation, and traffic volume, speed and other parameters were collected. The characteristic speeds, such as average speed, V₁₅ and V₈₅, were evaluation indicators, where V₁₅ and V₈₅ are the speeds of the 15th and 85th percentiles measured at the feature points of the road when the traffic is in a free-flow state and the weather is good. The relationship between different posted speed limit values and the above indicators was analyzed using the statistical analysis software, SPSS. The results show that the speed limit has a high correlation with the average speed of traffic flow, V₁₅ and V₈₅ in free-flow state, with the coefficient of determination being as high as 0.84, 0.85 and 0.92, respectively. In the restricted flow state, the factors affecting the driver’s driving speed are mainly the decrease in driving freedom caused by the increase of traffic volume rather than the speed limit value. In a free-flow state, when the posted speed limit is increased and the average speed and the V₈₅ also increased by approximately the same magnitude. The posted speed limit values of 80 km/h, 100 km/h and 120 km/h correspond to the 90, 88 and 97 percentile speeds of the traffic flow, respectively. The higher the speed limit is, the larger the speed difference between V₁₅ and V₈₅ becomes. The results of the study are very useful for rationally determining the speed limit scheme under different traffic flows.

Truck rollover crashes on horizontal curves have been recognized as one of the most serious types of crashes. Driver’s instantaneous emergency steering maneuvers (DIESM) play an important role in truck rollover crashes, but have not received much attention. In the present study, the radius of curvature of the actual vehicle travel path (AVTP) under DIESM was calculated based on the transient bicycle model. Rollover margins were used to evaluate the truck-rollover potential under DIESM. To calculate rollover margins, the lateral acceleration under DIESM was calculated based on the radius of the curvature of the AVTP. A rollover threshold formula was introduced to calculate vehicle’s rollover thresholds by distinguishing two turning conditions. According to rollover margins, the maximum safe instantaneous input of the steering wheel against rollover for trucks was obtained. Moreover, theoretical results were verified by computer simulation. Results showed: (1) The maximum safe instantaneous inputs of the steering wheel were 259°, 212°, 182°, 162°and 147°, respectively, at speeds of 60 km/h, 70 km/h, 80 km, 90 km and 100 km when the superelevation rate was 0, and (2) superelevation significantly affected truck-rollover potential; the worst turning condition was turning from the inside to the outside of the curve. Due to the consideration of the wheelbase, the centroid position, the tire’s cornering stiffness and the suspension roll gain, the prediction results were more accurate.

The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload (SW). Objective workload (OW) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW, and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DWs in optimizing traffic safety.

Understanding the relationship between speed limit and characteristic speed of expressway traffic flow is of great significance for formulating a reasonable speed limit scheme and improving highway safety and transportation efficiency. In this study, the speed data of the same traffic flow passing through speed limits of 80, 100, and 120 km/h were continuously collected through a field test. The 85th, 15th, and 50th percentile speeds were considered the characteristic speed parameters of the traffic flow. A regression analysis was performed to establish a relationship between the characteristic speed parameters of the traffic flow and the speed limit. Under a free-flow state, the characteristic speed exhibited a strong linear relationship with the speed limit, where the variation ranges of the 85th and 50th percentile speeds were approximately consistent with that of the speed limit. However, a slight inconsistency was found for the 15th percentile speed, which was approximately half the speed limit increase; under a non-free-flow state, the correlation between the speed limit and the vehicle speed was no longer significant.