Limitations and Improvements of the Intelligent Driver Model (IDM)

This contribution analyzes the widely used and well-known "intelligent driver model" (briefly IDM), which is a second order car-following model governed by a system of ordinary differential equations. Although this model was intensively studied in recent years for properly capturing traffic phenomena and driver braking behavior, a rigorous study of the well-posedness of solutions has, to our knowledge, never been performed. First it is shown that, for a specific class of initial data, the vehicles' velocities become negative or even diverge to $-\infty$ in finite time, both undesirable properties for a car-following model. Various modifications of the IDM are then proposed in order to avoid such ill-posedness. The theoretical remediation of the model, rather than post facto by ad-hoc modification of code implementations, allows a more sound numerical implementation and preservation of the model features. Indeed, to avoid inconsistencies and ensure dynamics close to the one of the original model, one may need to inspect and clean large input data, which may result practically impossible for large-scale simulations. Although well-posedness issues occur only for specific initial data, this may happen frequently when different traffic scenarios are analyzed, and especially in presence of lane-changing, on ramps and other network components as it is the case for most commonly used micro-simulators. On the other side, it is shown that well-posedness can be guaranteed by straight-forward improvements, such as those obtained by slightly changing the acceleration to prevent the velocity from becoming negative.Comment: 29 pages, 23 Figure

Population Enumeration and Household Utilization Survey Methods in the Enterics for Global Health (EFGH): Shigella Surveillance Study

Background: Accurate estimation of diarrhea incidence from facility-based surveillance requires estimating the population at risk and accounting for case patients who do not seek care. The Enterics for Global Health (EFGH) Shigella surveillance study will characterize population denominators and healthcare-seeking behavior proportions to calculate incidence rates of Shigella diarrhea in children aged 6–35 months across 7 sites in Africa, Asia, and Latin America. Methods: The Enterics for Global Health (EFGH) Shigella surveillance study will use a hybrid surveillance design, supplementing facility-based surveillance with population-based surveys to estimate population size and the proportion of children with diarrhea brought for care at EFGH health facilities. Continuous data collection over a 24 month period captures seasonality and ensures representative sampling of the population at risk during the period of facility-based enrollments. Study catchment areas are broken into randomized clusters, each sized to be feasibly enumerated by individual field teams. Conclusions: The methods presented herein aim to minimize the challenges associated with hybrid surveillance, such as poor parity between survey area coverage and facility coverage, population fluctuations, seasonal variability, and adjustments to care-seeking behavior

A deep language model to predict metabolic network equilibria

We show that deep learning models, and especially architectures like the Transformer, originally intended for natural language, can be trained on randomly generated datasets to predict to very high accuracy both the qualitative and quantitative features of metabolic networks. Using standard mathematical techniques, we create large sets (40 million elements) of random networks that can be used to train our models. These trained models can predict network equilibrium on random graphs in more than 99% of cases. They can also generalize to graphs with different structure than those encountered at training. Finally, they can predict almost perfectly (>96% accuracy) the equilibria of a small set of known biological networks. Our approach is both very economical in experimental data and uses only small and shallow deep-learning model, far from the large architectures commonly used in machine translation. Even if there exists powerful computational methods for the problems addressed in this paper (such as [36] or MEMOTE [31] for instance), the fact that the neural network has no a priori built-in knowledge is an incitative to believe that the same approach could be applied to problems for which no such computational methods exist. Such results pave the way for larger use of deep learning models for problems related to biological networks in key areas such as quantitative systems pharmacology, systems biology, and synthetic biology

A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles

In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ring-road. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5\% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the stop-and-go waves. This in turn may reduce fuel consumption and emissions. The lane-changing mechanism is based on three components that we treat as parameters in the model: safety, incentive and cool-down time. The choice of these parameters in the lane-change mechanism is critical to modeling traffic accurately, because different parameter values can lead to drastically different traffic behaviors. In particular, the number of lane-changes and the speed variance are highly affected by the choice of parameters. Despite this modeling issue, when using sufficiently simple and robust controllers for AVs, the stabilization of uniform flow steady-state is effective for any realistic value of the parameters, and ultimately bypasses the observed modeling issue. Our approach is based on accurate and rigorous mathematical models, which allows a limit procedure that is termed, in gas dynamic terminology, mean-field. In simple words, from increasing the human-driven population to infinity, a system of coupled ordinary and partial differential equations are obtained. Moreover, control problems also pass to the limit, allowing the design to be tackled at different scales.Comment: 24p. 6 figure

Diarrhea Case Surveillance in the Enterics for Global Health <i>Shigella</i> Surveillance Study: Epidemiologic Methods.

BackgroundShigella is a leading cause of acute watery diarrhea, dysentery, and diarrhea-attributed linear growth faltering, a precursor to stunting and lifelong morbidity. Several promising Shigella vaccines are in development and field efficacy trials will require a consortium of potential vaccine trial sites with up-to-date Shigella diarrhea incidence data.MethodsThe Enterics for Global Health (EFGH) Shigella surveillance study will employ facility-based enrollment of diarrhea cases aged 6-35 months with 3 months of follow-up to establish incidence rates and document clinical, anthropometric, and financial consequences of Shigella diarrhea at 7 country sites (Mali, Kenya, The Gambia, Malawi, Bangladesh, Pakistan, and Peru). Over a 24-month period between 2022 and 2024, the EFGH study aims to enroll 9800 children (1400 per country site) between 6 and 35 months of age who present to local health facilities with diarrhea. Shigella species (spp.) will be identified and serotyped from rectal swabs by conventional microbiologic methods and quantitative polymerase chain reaction. Shigella spp. isolates will undergo serotyping and antimicrobial susceptibility testing. Incorporating population and healthcare utilization estimates from contemporaneous household sampling in the catchment areas of enrollment facilities, we will estimate Shigella diarrhea incidence rates.ConclusionsThis multicountry surveillance network will provide key incidence data needed to design Shigella vaccine trials and strengthen readiness for potential trial implementation. Data collected in EFGH will inform policy makers about the relative importance of this vaccine-preventable disease, accelerating the time to vaccine availability and uptake among children in high-burden settings