During infectious disease outbreaks, early warning is crucial to prevent and control the further spread of the disease. While the different waves of the Covid-19 pandemic have demonstrated the need for continued compliance, little is known about the impact of warning messages and risk perception on individual behavior in public health emergencies. To address this gap, this paper uses data from the second wave of Covid-19 in China to analyse how warning information influences preventive behavior through four categories risk perception and information interaction. Drawing on the protective action decision model (PADM) and the social amplification of risk framework (SARF), risk warning information (content, channel, and type), risk perception (threat perception, hazard- and resource-related preparedness behavior perception and stakeholder perception), information interaction, and preparedness behavior intention are integrated into a comprehensive model. To test our model, we run a survey with 724 residents in Northern China. The results show that hazard-related preparedness behavior perception and stakeholder perception act as mediators between warning and preventive action. Stakeholder perception had much stronger mediating effects than the hazard-related attributes. In addition, information interaction is effective in increasing all categories risk perception, stimulating public response, while functioning as a mediator for warning. The risk warning information content, channel, and type are identified as key drivers of risk perception. The research found that information channel was more related to different risk perception than other characteristics. Overall, these associations in our model explain core mechanisms behind compliance and allow policy-makers to gain new insights into preventive risk communication in public health emergencies.

This research proposed an eco-driving system for an isolated signalized intersection under partially Connected and Automated Vehicles (CAV) environment. This system prioritizes mobility before improving fuel efficiency and optimizes the entire traffic flow by optimizing speed profiles of the connected and automated vehicles. The optimal control problem was solved using Pontryagin's Minimum Principle. Simulation-based before and after evaluation of the proposed design was conducted. Fuel consumption benefits range from 2.02% to 58.01%. The CO₂ emissions benefits range from 1.97% to 33.26%. Throughput benefits are up to 10.80%. The variations are caused by the market penetration rate of connected and automated vehicles and v/c ratio. No adverse effect is observed. Detailed investigation reveals that benefits are significant as long as there is CAV and they grow with CAV's market penetration rate (MPR) until they level off at about 40% MPR. This indicates that the proposed eco-driving system can be implemented with a low market penetration rate of connected and automated vehicles and could be implemented in a near future. The investigation also reveals that the proposed eco-driving system is able to smooth out the shock wave caused by signal controls and is robust over the impedance from conventional vehicles and randomness of traffic. The proposed system is fast in computation and has great potential for real-time implementation.