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.

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.

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.

To reduce the impact of a natural or man-made disaster, an evacuation is often implemented to transfer the population in the potentially impacted area to a safe zone. Evacuation is an effective measure for dealing with emergency events. This paper presents a multinomial logit model for modal choice behavior in a short-notice emergency evacuation, which incorporates spatial indicators into the utility function. The study examined the determinants of evacuees’ modal choice for three evacuation distances and analyzed determinants impacting the mechanism of the modal choice decision process. The data collected in Xi’an was used to provide empirical evidence for the relationship between spatial indicators and modal choice behavior. The findings of this study will help emergency planners and policy-makers develop strategies for evacuation planning and will enable a better understanding of emergency modal choice behaviors.