The entrance and exit sections of a tunnel are the accident black-spots in an expressway. For a safe operation of road tunnels, it is necessary to understand a driver’s physiological indices and driving performance when driving through tunnels. In this study, the UC-Win/Road simulation software was used to build 12 tunnel models of different lengths. A simulated driving experiment was carried out in a 6-DoF motion platform. The lateral position of vehicles characterizing the driving performance was measured using the motion platform. Electrocardiogram and eye movement data of 25 recruited drivers were collected simultaneously through the experiment. The spatial changes in a driver’s heart rate (HR) growth rate, RMSSD, pupil diameter growth rate and vehicle lateral deviation within 300 m before and after the tunnel entrance and exit were analyzed to determine the variation rules in the different tunnels. The study identified the length range in the tunnel entrance and exit sections that influences the drivers. A quantitative analysis was further carried out to analyze the relationship between the physiological indices and the driving performance indicator. The results showed that a driver’s heart rate fluctuates significantly 250 m before the tunnel entrance and 50 m before the exit. In this region, the pupil diameter increases gradually, and drivers tend to shift the vehicle to the left. At the tunnel exit, the HR and RMSSD are affected significantly by the tunnel length, and the variation is higher in longer tunnels. In comparison, the tunnel length has no significant effect on the physiological indicators and driving performance of the drivers at the entrance and exit.

Recently, subways have become an important part of public transportation and have developed rapidly in China. In the subway station setting, pedestrians mainly rely on visual short-term memory to obtain information on how to travel. This research aimed to explore the short-term memory capacities and the difference in short-term memory for different information for Chinese passengers regarding subway signs. Previous research has shown that people’s general short-term memory capacity is approximately four objects and that, the more complex the information, the lower people’s memory capacity. However, research on the short-term memory characteristics of pedestrians for subway signs is scarce. Hence, based on the STM theory and using 32 subway signs as stimuli, we recruited 120 subjects to conduct a cognitive test. The results showed that passengers had a different memory accuracy for different types of information in the signs. They were more accurate regarding line number and arrow, followed by location/text information, logos, and orientation. Meanwhile, information type, quantity, and complexity had significant effects on pedestrians’ short-term memory capacity. Finally, according to our results that outline the characteristics of short-term memory for subway signs, we put forward some suggestions for subway signs. The findings will be effective in helping designers and managers improve the quality of subway station services as well as promoting the development of pedestrian traffic in such a setting.

Heavy-duty trucks contribute a significant component of all transportation in cargo terminals, such as Shaanxi Province, China. The emissions from these vehicles are the primary source of carbon emissions during highway operations. While several studies have attempted to address emission issues by improving traffic operations, a few focused on the relationship between emissions and highway geometric design, especially for heavy-duty trucks. The primary goal of this research was to understand the impact of circular curve on carbon dioxide (CO₂) emissions produced by heavy-duty diesel trucks. Firstly, appropriate parameters were specified in MOVES (motor vehicle emission simulator) model according to the geometrical characteristics. Fuel consumption, speed and location data were collected by hiring five skilled drivers on the automotive proving ground located at Chang’an University, Shaanxi Province. The associated carbon emission data were derived from fuel consumption data by applying the IPCC (Intergovernmental Panel on Climate Change) method. After this, the applicability of MOVES model was verified by the field experiment. Moreover, a multiple regression model for CO₂ emissions incorporated with roadway segment radius, circular curve length, and initial vehicle speed was established with data generated by the MOVES model. The proposed CO₂ emission model was also verified by field experiment with relative error of 6.17%. It was found that CO₂ emission had monotone decreasing property with radius increasing, and the minimum radius that influenced diesel CO₂ emission was 550 m. The proposed quantitative CO₂ emission model can provide a reference for low-carbon highway design, leading to environment-friendly transportation construction.

Road geometric design is a key factor impacting driving safety and efficiency. In highway profile design, speed reduction is used to determine critical length of grade. Previous research generally concentrated on the relationship between speed reduction and crash involvement rate to establish the recommended value. Limited research results have been reported at this point concerning speed reduction and traffic efficiency. This study aims to fill the gap by investigating tolerable speed reduction with different vertical slopes considering traffic efficiency. Firstly, appropriate experimental sections were determined after field survey. Traffic data including vehicle count, timely speed, vehicle type, and headway time were then collected on an expressway in Shaanxi Province. The associated traffic efficiency was derived from traffic volume and average speed. After this, the modeling between speed reduction and traffic efficiency was processed with different slopes. The correlation between speed reduction and traffic efficiency was therefore verified. Finally, the prediction model of optimum speed reduction concerning traffic efficiency under different vertical slopes was introduced. It was found that the critical length of grade can be longer with traffic efficiency as the major design control incorporated with slopes of 3–3.5%. The existing regulation in critical length of grade at 3.5–5% can benefit both safety and efficiency. The findings can provide a reference for vertical alignment design, leading to high-efficiency road systems.

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.