Improved Car-Following Model Considering Modified Backward Optimal Velocity and Velocity Difference with Backward-Looking Effect

In this paper, a new traffic flow model called the forward-backward velocity difference (FBVD) model based on the full velocity difference model is proposed to investigate the backward-looking effect by applying a modified backward optimal velocity using generalized backward maximum speed. The FBVD model belongs to the family of microscopic models that consider spatiotemporally continuous formulations. Neutral stability conditions of the discrete car-following model are derived using the linear stability theory. The stability analysis results prove that the modified backward optimal velocity has a significant positive effect in stabilizing the traffic flow. Through nonlinear analysis, a kink-antikink solution is derived from the modified Korteweg-de Vries equation of the FBVD model to explain traffic congestion of the model. The validity of this theoretical model is checked using numerical results, according to which traffic jams were found to have been significantly diminished by the introduction of the modified backward optimal velocity.

Analytical Solutions of Fukui-Ishibashi (FI) Model and Quick-Start (QS) Model

Through straightforward deduction procedure, we explicitly show analytical solutions for both Fukui-Ishibashi (FI) model and Quick-Start (QS) model, which are fundamental deterministic Cellular Automaton (CA), applied to traffic flow.

A Game-Theoretic Modeling Approach to Comprehend the Advantage of Dynamic Health Interventions in Limiting the Transmission of Multi-Strain Epidemics

In the situation of inadequate vaccines and rapid mutation of virulent strains, alternative health interventions play a crucial role in the containment of emerging epidemics. This study elucidates the critical aspects of health interventions to control epidemic resurgence. Besides, human behavioral response to epidemics plays an instrumental role in bringing the success of control efforts. The appearance of multi-strain epidemics has become a global health concern that requires special attention. Here, we introduce a novel mean-field epidemic game approach to predict the evolutionary dynamics of flu-like epidemics having multiple disease strains. Our model illustrates the importance of multiple provisions alongside their timely execution for better disease attenuation. In addition to vaccination, we introduce self-protection as a potential alternative that yields safeguard against either strain. Both these imperfect provisions render better efficacy against primary (resident) strain than secondary (mutant) to contain epidemic transmission. The simulation-backed model analysis further sheds some light on the crucial impacts of control interventions to limit the invasion of virulent strains from qualitative and quantitative viewpoints. It explicates how vaccination and self-protection complement each other as per situation demands. Our full-fledged theoretical approach further illustrates the dynamic trade-off between the cost and efficacy of a certain intervention. We confirm that the disease dies out when the basic reproduction number of individual strains is less than one and becomes endemic if it is greater than one. Finally, the model addresses the evolutionary consequences when mutation takes place from primary to secondary strain. Some impressive facts while employing dual provisions have been reinforced using a game-theoretic framework.

Time delay of the appearance of a new strain can affect vaccination behavior and disease dynamics:An evolutionary explanation

The emergence of a novel strain during a pandemic,like the current COVID-19,is a major concern to the healthcare system.The most effective strategy to control this type of pandemic is vaccination.Many previous studies suggest that the existing vaccine may not be fully effective against the new strain.Additionally,the new strain's late arrival has a significant impact on the disease dynamics and vaccine coverage.Focusing on these issues,this study presents a two-strain epidemic model in which the new strain appears with a time delay.We considered two vaccination provisions,namely preinfection and post-infection vaccinations,which are governed by human behavioral dynamics.In such a framework,individuals have the option to commit vaccination before being infected with the first strain.Additionally,people who forgo vaccination and become infected with the first train have the chance to be vaccinated(after recovery)in an attempt to avoid infection from the second strain.However,a second strain can infect vaccinated and unvaccinated individuals.People may have additional opportunities to be vaccinated and to protect themselves from the second strain due to the time delay.Considering the cost of the vaccine,the severity of the new strain,and the vaccine's effectiveness,our results indicated that delaying the second strain decreases the peak size of the infected individuals.Finally,by estimating the social efficiency deficit,we discovered that the social dilemma for receiving immunization decreases with the delay in the arrival of the second strain.