Physics

This research effort uses vehicular traffic flow techniques to model bicyclist longitudinal motion while accounting for bicycle interactions. Specifically, an existing car-following model, the Fadhloun-Rakha (FR) model is re-parametrized to model bicyclists. Initially, the study evaluates the performance of the proposed model formulation using experimental datasets collected from two ring-road bicycle experiments; one conducted in Germany in 2012, and the second in China in 2016. The validation of the model is achieved through investigating and comparing the proposed model outputs against those obtained from two state-of-the-art models, namely: the Necessary Deceleration Model (NDM), which is a model specifically designed to capture the longitudinal motion of bicyclists; and the Intelligent Driver Model, which is a car-following model that was demonstrated to be suitable for single-file bicycle traffic. Through a quantitative and qualitative evaluation, the proposed model formulation is demonstrated to produce modeling errors that are consistent with the other two models. While all three models generate trajectories that are consistent with empirically observed bicycle-following behavior, only the proposed model allows for an explicit and straightforward tuning of the bicyclist physical characteristics and the road environment. A sensitivity analysis, demonstrates the effect of varying the different model parameters on the produced trajectories, highlighting the robustness and generality of the proposed model.

Echo chambers and polarisation dynamics are as of late a very prominent topic in scientific communities around the world. As these phenomena directly affect our lives and seemingly more and more as our societies and communication channels evolve it becomes ever so important for us to understand the intricacies opinion dynamics in modern era. We build upon an existing echo chambers and polarisation model and extend it onto a bi-layer topology. This new topological context allows us to indicate possible consequences of interacting groups within this model. Four different cases are presented - symmetric negative and positive couplings, an asymmetric coupling and an external bias. We show both simulation results and mean field solutions for these scenarios outlining the possible consequences of such dynamics in real world societies.Our predictions show that there are conditions in which the system can reach states of neutral consensus, a polarised consensus, polarised opposition and even opinion oscillations. Transitions between these states in terms of bifurcation theory are identified and analysed using a mean field model.

The Braess paradox describes the counterintuitive situation that the addition of new roads to road networks can lead to higher travel times for all network users. Recently we could show that user optima leading to the paradox exist in networks of microscopic transport models. We derived phase diagrams for two kinds of route choice strategies that were externally tuned and applied by all network users. Here we address the question whether these user optima are still realized if intelligent route choice decisions are made based upon two kinds of traffic information. We find that the paradox still can occur if the drivers 1) make informed decisions based on their own past experiences or 2) use traffic information similar to that provided by modern navigation apps. This indicates that modern traffic information systems are not able to resolve Braess' paradox.

A detailed analysis of Twitter-based information cascades is performed, and it is demonstrated that branching process hypotheses are approximately satisfied. Using a branching process framework, models of agent-to-agent transmission are compared to conclude that a limited attention model better reproduces the relevant characteristics of the data than the more common independent cascade model. Existing and new analytical results for branching processes are shown to match well to the important statistical characteristics of the empirical information cascades, thus demonstrating the power of branching process descriptions for understanding social information spreading.

The demand for electricity and the need to replace fossil fuels by renewables have been growing steadily, and this transition will have significant implications to our world that are only beginning to be understood. Brazil is one important example of a big economy where the electricity is already supplied by renewables, such as hydro, wind and biomass-fired thermal power. In this work we investigated the electricity load curves in the last 20 years in Brazil, and four different scenarios for 2030 are proposed in order to evaluate the impact of increasing renewables in the national grid, at an hourly basis. The analysis shows that growing electricity demand and the expected reduction in the hydropower share will significantly increase the reliability of the national grid, due to higher peak load and also due to the intermittency of Solar and Wind. Without any gigawatt scale hydropower projected for the near future, increasing the share of these renewables should push hydropower to operate hundreds of hours every year above typical peak power levels experienced in the past. In order to avoid or reduce the threat related to this trend one of our scenarios suggests that solar water heaters could be massively deployed in Brazil, what would positively impact the system reliability by reducing the electricity demand mostly at peak loads during early evenings.

While AAPT and many other physics organizations have been introducing a series of effective practices around diversity and inclusion at the national level in recent years, it was wondered if these were being adopted at the local level. It is hoped that section members and Section Representatives will decide to further expand the actions of national leadership to make their own section meeting more inclusive. In order to assess if this was in fact the case, a survey on diversity practices that have been used the the national level and can be implemented at the section level was sent to AAPT's Section Reps mailing list in the spring of 2018, with a follow-up survey in winter 2020. Feedback in both cycles suggested that a guide for section leadership would be useful. The Northern California/Nevada section has made progress in implementing some of the effective practices from the national meetings into our local section meetings, we share these efforts in the hope that they assist our fellow sections.

Millions of people have been infected and lakhs of people have lost their lives due to the worldwide ongoing novel Coronavirus (COVID-19) pandemic. It is of utmost importance to identify the future infected cases and the virus spread rate for advance preparation in the healthcare services to avoid deaths. Accurately forecasting the spread of COVID-19 is an analytical and challenging real-world problem to the research community. Therefore, we use day level information of COVID-19 spread for cumulative cases from whole world and 10 mostly affected countries; US, Spain, Italy, France, Germany, Russia, Iran, United Kingdom, Turkey, and India. We utilize the temporal data of coronavirus spread from January 22, 2020 to May 20, 2020. We model the evolution of the COVID-19 outbreak, and perform prediction using ARIMA and Prophet time series forecasting models. Effectiveness of the models are evaluated based on the mean absolute error, root mean square error, root relative squared error, and mean absolute percentage error. Our analysis can help in understanding the trends of the disease outbreak, and provide epidemiological stage information of adopted countries. Our investigations show that ARIMA model is more effective for forecasting COVID-19 prevalence. The forecasting results have potential to assist governments to plan policies to contain the spread of the virus.

This study empirically investigates the complex interplay between the severity of the coronavirus pandemic, mobility changes in retail and recreation, transit stations, workplaces, and residential areas, and lockdown measures in 88 countries of the word. To conduct the study, data on mobility patterns, socioeconomic and demographic characteristics of people, lockdown measures, and coronavirus pandemic were collected from multiple sources (e.g., Google, UNDP, UN, BBC, Oxford University, Worldometer). A Structural Equation Modeling (SEM) technique is used to investigate the direct and indirect effects of independent variables on dependent variables considering the intervening effects of mediators. Results show that lockdown measures have significant effects to encourage people to maintain social distancing. However, pandemic severity and socioeconomic and institutional factors have limited effects to sustain social distancing practice. The results also explain that socioeconomic and institutional factors of urbanity and modernity have significant effects on pandemic severity. Countries with a higher number of elderly people, employment in the service sector, and higher globalization trend are the worst victims of the coronavirus pandemic (e.g., USA, UK, Italy, and Spain). Social distancing measures are reasonably effective at tempering the severity of the pandemic.

Policy-makers require data-driven tools to assess the spread of COVID-19 and inform the public of their risk of infection on an ongoing basis. We propose a rigorous hybrid model-and-data-driven approach to risk scoring based on a time-varying SIR epidemic model that ultimately yields a simplified color-coded risk level for each community. The risk score Γ t that we propose is proportional to the probability of someone currently healthy getting infected in the next 24 hours. We show how this risk score can be estimated using another useful metric of infection spread, R t , the time-varying average reproduction number which indicates the average number of individuals an infected person would infect in turn. The proposed approach also allows for quantification of uncertainty in the estimates of R t and Γ t in the form of confidence intervals. Code and data from our effort have been open-sourced and are being applied to assess and communicate the risk of infection in the City and County of Los Angeles.

Due to immature treatment and rapid transmission of COVID-19, mobility interventions play a crucial role in containing the outbreak. Among various non-pharmacological interventions, community infection control is considered to be a quite promising approach. However, there is a lack of research on improving community-level interventions based on a community's real conditions and characteristics using real-world observations. Our paper aims to investigate the different responses to mobility interventions between communities in the United States with a specific focus on different income levels. We produced six daily mobility metrics for all communities using the mobility location data from over 100 million anonymous devices on a monthly basis. Each metric is tabulated by three performance indicators: "best performance," "effort," and "consistency." We found that being high-income improves social distancing behavior after controlling multiple confounding variables in each of the eighteen scenarios. In addition to the reality that it is more difficult for low-income communities to comply with social distancing, the comparisons between scenarios raise concerns on the employment status, working condition, accessibility to life supplies, and exposure to the virus of low-income communities.