Under no-notice evacuation scenarios with limited time horizons, the effectiveness of evacuation can be negatively impacted by intermediate trips that are made by family members and the identification of vulnerable populations. The emergence of shared-mobility companies, such as Uber and DiDi, can be considered as a potential means to address above-mentioned concerns. The proposed study explores the utility of shared-mobility services under emergency-evacuation scenarios and makes recommendations to relevant bodies that are based on the obtained and they are discussed herein. The study investigates attitudes of the public, experts, and drivers towards the use of shared-mobility resources during emergency evacuations based on a stated preference survey. Results of questionnaires, driver interviews, and face-to-face expert interviews have been analyzed to validate the feasibility and identify potential problems of leveraging shared-mobility services during evacuation response, especially in metropolitan areas wherein such services are already ubiquitous. Numerical simulations have been performed to quantify potential improvements in the total trip distance and number of evacuees after incorporating the use of shared mobility into emergency-response operations. However, despite the observed improvement in emergency efficiency, certain realistic roadblocks must be overcome. Realization of the proposed objective heavily depends on actionable policy recommendations, provided herein as a reference for the government, emergency management agencies, and shared-mobility companies.

In the metro operation environment, guiding signs provide direction and route conversion instructions to pedestrians. In metro stations with massive passenger flow, the rationality of sign setting would exert distinct effects on the efficiency of passenger flow. Currently, most studies on guiding signs focus on architecture, aesthetics and simulation. However, perspectives from humanization of pedestrian guidance signs such as pedestrian behavior needs and pedestrian cognition were seldom proposed. In this paper, the microscopic behavior characteristics data of pedestrians at different positions in typical metro stations were collected through pedestrian tracking experiments. After analyzing the characteristics of pedestrians’ microscopic behavior in metro stations, otherness of walking speed was found out among pedestrians in different types of passageways. The walking speed of pedestrians in closed-type passageways is higher than other types. Moreover, pedestrian speed at the stairs adjacent to the platform is higher than that at the stairs not adjacent to the platform. With the increase of crowd density, the change of walking speed of pedestrians can be represented by a unimodal curve. Finally, the key points of optimal setting of guiding signs in different regions and different periods were obtained according to the result analysis of the experiment. The research results of this paper can provide theoretical support and technical guidance for the optimal establishment of pedestrian guiding signs in metro stations with massive passenger flow.

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.

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.