Agent-Based Demand-Modeling Framework for Large-Scale Microsimulations

Microsimulation is becoming increasingly important in traffic demand modeling. The major advantage over traditional four-step models is the ability to simulate each traveler individually. Decision-making processes can be included for each individual. Traffic demand is the result of the different decisions made by individuals; these decisions lead to plans that the individuals then try to optimize. Therefore, such microsimulation models need appropriate initial demand patterns for all given individuals. The challenge is to create individual demand patterns out of general input data. In practice, there is a large variety of input data, which can differ in quality, spatial resolution, purpose, and other characteristics. The challenge for a flexible demand-modeling framework is to combine the various data types to produce individual demand patterns. In addition, the modeling framework has to define precise interfaces to provide portability to other models, programs, and frameworks, and it should be suitable for large-scale applications that use many millions of individuals. Because the model has to be adaptable to the given input data, the framework needs to be easily extensible with new algorithms and models. The presented demand-modeling framework for large-scale scenarios fulfils all these requirements. By modeling the demand for two different scenarios (Zurich, Switzerland, and the German states of Berlin and Brandenburg), the framework shows its flexibility in aspects of diverse input data, interfaces to third-party products, spatial resolution, and last but not least, the modeling process itself

Mobility traces and spreading of COVID-19

We use human mobility models, for which we are experts, and attach a virus infection dynamics to it, for which we are not experts but have taken it from the literature, including recent publications. This results in a virus spreading dynamics model. The results should be verified, but because of the current time pressure, we publish them in their current state. Recommendations for improvement are welcome. We come to the following conclusions: 1. Complete lockdown works. About 10 days after lockdown, the infection dynamics dies down. This assumes that lockdown is complete, which can be guaranteed in the simulation, but not in reality. Still, it gives strong support to the argument that it is never too late for complete lockdown. 2. As a rule of thumb, we would suggest complete lockdown no later than once 10% of hospital capacities available for COVID-19 are in use, and possibly much earlier. This is based on the following insights: a. Even after lockdown, the infection dynamics continues at home, leading to another tripling of the cases before the dynamics is slowed. b. There will be many critical cases coming from people who were infected before lockdown. Because of the exponential growth dynamics, their number will be large. c. Researchers with more detailed disease progression models should improve upon these statements. 3. Our simulations say that complete removal of infections at child care, primary schools, workplaces and during leisure activities will not be enough to sufficiently slow down the infection dynamics. It would have been better, but still not sufficient, if initiated earlier. 4. Infections in public transport play an important role. In the simulations shown later, removing infections in the public transport system reduces the infection speed and the height of the peak by approximately 20%. Evidently, this depends on the infection parameters, which are not well known. – This does not point to reducing public transport capacities as a reaction to the reduced demand, but rather use it for lower densities of passengers and thus reduced infection rates. 5. In our simulations, removal of infections at child care, primary schools, workplaces, leisure activities, and in public transport may barely have been sufficient to control the infection dynamics if implemented early on. Now according to our simulations it is too late for this, and (even) harsher measures will have to be initiated until possibly a return to such a restrictive, but still somewhat functional regime will again be possible. Evidently, all of these results have to be taken with care. They are based on preliminary infection parameters taken from the literature, used inside a model that has more transport/movement details than all others that we are aware of but still not enough to describe all aspects of reality, and suffer from having to write computer code under time pressure. Optimally, they should be confirmed independently. Short of that, given current knowledge we believe that they provide justification for “complete lockdown” at the latest when about 10% of available hospital capacities for COVID-19 are in use (and possibly earlier; we are no experts of hospital capabilities). What was not investigated in detail in our simulations was contact tracing, i.e. tracking down the infection chains and moving all people along infection chains into quarantine. The case of Singapore has so far shown that this may be successful. Preliminary simulation of that tactic shows that it is difficult to implement for COVID-19, since the incubation time is rather long, people are contagious before they feel sick, or maybe never feel sufficiently sick at all. We will investigate in future work if and how contact tracing can be used together with a restrictive, but not totally locked down regime. When opening up after lockdown, it would be important to know the true fraction of people who are already immune, since that would slow down the infection dynamics by itself. For Wuhan, the currently available numbers report that only about 0.1% of the population was infected, which would be very far away from “herd immunity”. However, there have been and still may be many unknown infections (Frankfurter Allgemeine Zeitung GmbH 2020)

Preliminary Results of a Multiagent Traffic Simulation for Berlin

This paper provides an introduction to multi-agent traffic simulation. Metropolitan regions can consist of several million inhabitants, implying the simulation of several million travelers, which represents a considerable computational challenge. We reports on our recent case study of a real-world Berlin scenario. The paper explains computational techniques necessary to achieve results. It turns out that the difficulties there, because of data availability and because of the special situation of Berlin after the re-unification, are considerably larger than in previous scenarios that we have treated

Large-Scale Multi-Agent Simulations for Transportation Applications

In many transportation simulation applications including intelligent transportation systems (ITS), behavioral responses of individual travelers are important. This implies that simulating individual travelers directly may be useful. Such a microscopic simulation, consisting of many intelligent particles (= agents), is an example of a multi-agent simulation. For ITS applications, it would be useful to simulate large metropolitan areas, with ten million travelers or more. Indeed, when using parallel computing and efficient implementations, multi-agent simulations of transportation systems of that size are feasible, with computational speeds of up to 300 times faster than real time. It is also possible to efficiently implement the simulation of day-to-day agent-based learning, and it is possible to make this implementation modular and essentially “plug-and-play.” Unfortunately, these techniques are not immediately applicable for within-day replanning, which would be paramount for ITS. Alternative techniques, which allow within-day replanning also for large scenarios, are discussed

Converting a Static Macroscopic Model Into a Dynamic Activity-Based Model to Analyze Public Transport Demand in Berlin

Transport models demanded by public transport companies today should not only deliver the basis for future planning of the regional transport system, but also provide detailed information about passenger flows of different user groups. This paper presents the successful transformation of a static macroscopic model (built using PTV VISUM) into an integrated activity based demand and dynamic assignment model (MATSim) performed for a real application in the Berlin/Brandenburg metropolitan region. While the two models clearly differ in their methodology, overall key values can be reproduced showing similar results. It is shown that by the use of the activity chain distributions and their timing activity based demand can be reproduced with respect to the trip distribution of the origin-destination matrices from the macroscopic model. The process flow defined in this paper allows to use both models for planning purpose, case studies and effect analysis, enabling public transport companies to analyze effects on the macroscopic level of detail as well as on the agent based level to capture specific customer groups and/or time ranges during the day. The microscopic model is then used for further analyses, of which a selection is presented in this paper. Notably, the model allows for researching effects generated by the interaction of public transport vehicles and regular private car traffic, or for researching user-group specific behavior

Agent-Oriented Coupling of Activity-Based Demand Generation with Multiagent Traffic Simulation

The typical method to couple activity-based demand generation (ABDG) and dynamic traffic assignment (DTA) is time-dependent origin-destination (O-D) matrices. With that coupling method, the individual traveler's information gets lost. Delays at one trip do not affect later trips. However, it is possible to retain the full agent information from the ABDG by writing out all agents' plans, instead of the O-D matrix. A plan is a sequence of activities, connected by trips. Because that information typically is already available inside the ABDG, this is fairly easy to achieve. Multiagent simulation (MATSim) takes such plans as input. It iterates between the traffic flow simulation (sometimes called network loading) and the behavioral modules. The currently implemented behavioral modules are route finding and time adjustment. Activity resequencing or activity dropping are conceptually clear but not yet implemented. Such a system will react to a time-dependent toll by possibly rearranging the complete day; in consequence, it goes far beyond DTA (which just does route adaptation). This paper reports on the status of the current Berlin implementation. The initial plans are taken from an ABDG, originally developed by Kutter; to the authors' knowledge, this is the first time traveler-based information (and not just O-D matrices) is taken from an ABDG and used in a MATSim. The simulation results are compared with real-world traffic counts from about 100 measurement stations

Magnetic toys: forbidden for pediatric patients with certain programmable shunt valves?

Background: Inadvertent adjustments and malfunctions of programmable valves have been reported in cases in which patients have encountered powerful electromagnetic fields such as those involved in magnetic resonance imaging, but the potential effects of magnetic toys on programmable valves are not well known. Materials and methods: The magnetic properties of nine toy magnets were examined. To calculate the effect of a single magnet over a distance, the magnetic flux density was directly measured using a calibrated Hall probe at seven different positions between 0 and 120mm from the magnet. Strata II small (Medtronic Inc.), Codman Hakim (Codman & Shurtleff), and Polaris (Sophysa) programmable valves were then tested to determine the effects of the toy magnets on each valve type. Results: The maximal flux density of different magnetic toys differed between 17 and 540mT, inversely proportional to the distance between toy and measurement instrument. Alterations to Strata and Codman valve settings could be effected with all the magnetic toys. The distances that still led to an alteration of the valve settings differed from 10 to 50mm (Strata), compared with 5 to 30mm (Codman). Valve settings of Polaris could not be altered by any toy at any distance due to its architecture with two magnets adjusted in opposite directions. Conclusion: This is the first report describing changes in the pressure setting of some adjustable valves caused by magnetic toys in close contact. Parents, surgeons, neurologists, pediatric oncologists, and paramedics should be informed about the potential dangers of magnetic toys to prevent unwanted changes to pressure setting

Simmel and Shakespeare on Lying and Love

From SAGE Publishing via Jisc Publications RouterHistory: epub 2021-01-31Publication status: PublishedThis article contributes to the development of the sociology of lying by exploring some of the earliest comments on the topic, which are to be found amongst Georg Simmel’s writings about secrecy. We outline Simmel’s broader approach to interaction, as an experience that is conditioned upon non-knowledge, and work towards the attribution to him of the discovery of an aesthetic of concealment and revelation. This, we argue, can be used as a founding block in the sociology of lying. We then examine what Simmel has to say about lying specifically and find he falls into contradiction as he tries to link lying to other social forms, such as love, and to the shifting patterns of life which he understood to define modernity. To refine his approach, we look back to the period of early modernity during which questions of self-revelation and concealment are being explored in literature and lived uncertainly. Specifically, we take a detailed look at William Shakespeare’s Sonnet 138, for it clearly articulates the complex, relational dynamics of lying and love and allows us to read this back into Simmel’s account and explain why he falls into confusion. We then conclude by posing a series of questions and taking the position that sociologists should study lying as a relational phenomenon

MATSim-T : Architecture and Simulation Times

Micro-simulations for transport planning are becoming increasingly important in traffic simulation, traffic analysis, and traffic forecasting. In the last decades the shift from using typically aggregated data to more detailed, individual based, complex data (e.g. GPS tracking) andthe continuously growing computer performance on fixed price level leads to the possibility of using microscopic models for large scale planning regions. This chapter presents such a micro-simulation. The work is part of the research project MATSim (Multi Agent Transport Simulation, http://matsim.org). In the chapter here the focus lies on design and implementation issues as well as on computational performance of different parts of the system. Based on a study of Swiss daily traffic – ca. 2.3 million individuals using motorized individual transport producing about 7.1 million trips, assigned to a Swiss network model with about 60,000 links, simulated and optimized completely time-dynamic for a complete workday – it is shown that the system is able to generate those traffic patterns in about 36 hours computation time

Constrictive tuberculous pericarditis in a HIV-positive patient

Constrictive pericarditis is a relatively rare clinical manifestation nowadays. We present the case of an HIV-positive patient with constrictive calcified pericarditis due to an infection with Mycobacterium tuberculosis. Pericardectomie was performed. The therapeutical approach is discussed and the literature is reviewe