Mind the Gap – Passenger Arrival Patterns in Multi-agent Simulations

In most studies mathematical models are developed finding the expected waiting time to be a function of the headway. These models have in common that the proportion of passengers that arrive randomly at a public transport stop is less as headway in-creases. Since there are several factors of influence, such as social demographic or regional aspects, the reliability of public transport service and the level of passenger information, the threshold headway for the transition from random to coordinated passenger arrivals vary from study to study. This study's objective is to investigate if an agent-based model exhibits realistic passenger arrival behavior at transit stops. This objective is approached by exploring the sensitivity of the agents' arrival behavior towards (1) the degree of learning, (2) the reliability of the experienced transit service, and (3) the service headway. The simulation experiments for a simple transit corridor indicate that the applied model is capable of representing the complex passenger arrival behavior observed in reality. (1) For higher degrees of learning, the agents tend to over-optimize, i.e. they try to obtain the latest possible departure time exact to the second. An approach is presented which increases the diversity in the agents' travel alternatives and results in a more realistic behavior. (2) For a less reliable service the agents' time adaptation changes in that a buffer time is added between their arrival at the stop and the actual departure of the vehicle. (3) For the modification of the headway the simulation outcome is consistent with the literature on arrival patterns. Smaller headways yield a more equally distributed arrival pattern whereas larger headways result in more coordinated arrival patterns

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)

Assessing the effectivness of policy measures with the help of qualitative modeling

The aim of a recent project of the UBA was to get a systemic and hence a better understanding of the effectiveness of measures that are meant to help achieve more sustainability in our country. Questions were raised as to why some measures had little effect and also why many measures known to be effective were not being implemented. It was also important to determine what additional measures could be taken. We began the cause and effect model by taking some predefined factors that described the overall goal of becoming a sustainable country. We then applied the KNOW WHY Method to systematically create a model that would include the crucial factors. We did this by repeatedly asking what would directly lead to more of a given factor, and what would directly hinder it, both today and in future. These are the so-called KNOW WHY questions, and we asked them for each and every factor in our model. This resulted in us being able to determine early cross connections, and through them feedback structures became apparent. The model included policy measures, social and psychological factors, as well as economical and environmental aspects. Qualitative modeling makes visible the connections that exist between so-called factors, which carry information about the direction of impact (positive or negative), the strength (weak, middle or strong) and any possible delays in terms of time (short term, medium term or long term). Taken all together, these connections can then be analyzed in so-called Insight Matrices that make it possible to compare the short, middle and long term impact of factors, and hence to see what factors are involved in creating a greater or a lesser impact – in the case of this project, this meant determining what measures promised to be more or less effective and what might hinder the success of these measures to a greater and lesser degree, both now and in the future. In our approach, the factors and connections are not mere visualizations of predefined knowledge gained by modeling experts, but the result of collaborative modeling done by experts from different fields with the aim of obtaining new insights and a deeper understanding of the complex challenge at hand. Therefore, the approach is comparable to that of grounded theory or qualitative social research where scenarios of possible developments cannot be based on empirical data from the past either. Ultimately, the model consisted of over 100 factors and had more than 1 million feedback loops. The results gained by taking this approach shed some light on why the process of change in our society on its way to becoming more sustainable is so slow. The results also explained how and why policymakers, consumers, companies and the media are dependent on each other, and made clear what obstacles the first movers among them face. The model offered an explanation for a widespread phenomenon: rationally knowing what should been done and yet. being emotionally satisfied by engaging in non-sustainable behavior. And finally, the model offered a lever, an entry into the cycle of passive, interdependent players: we need to make sustainable consumption and hence non-consumption emotionally felt through a system that scores behavior. In this short article we will provide one concrete example of how we reflected on the effectiveness of a common policy measure, i.e. the introduction of a resource tax, and how we then assessed it and determined possible impacts and constraints

How reliable are incidence estimates based on cross-sectional distributions? Evidence from simulations and linked employer-employee data

The paper validates an empirical approach developed by Alvaredo and Saez (2007) which estimates the economic incidence of social security contributions (SSC) on the basis of cross-sectional earnings distributions. The method utilizes discontinuities at earnings caps where the marginal SSC rate drops. It does not rely on policy reforms, panel data, or hours information. We demonstrate on the basis of simulations that this comes at the cost of ruling out optimization frictions and measurement error destroying identification. Behavioral responses and incidence heterogeneity are secondary problems. Exploiting German linked employer-employee data that provide separate measures of gross and net earnings we find substantial negative discontinuities in net earnings. Together with small, erratic discontinuities in gross earnings this provides consistent empirical evidence that legal and economic incidence of SSC coincide in Germany

Material efficiency and global pathways towards 100% renewable energy systems – system dynamics findings on potentials and constraints

Global climate mitigation requires a renewable energy transition. Due to interactions between energy demand and material use, improvements in material efficiency promise to contribute to climate mitigation. To analyse such potentials, system dynamics modelling was applied to test four different scenarios towards a 100% renewable energy world. The model findings show that a 100% renewable energy world with zero greenhouse gas emissions seems feasible. However, the chosen pathway matters. While material efficiency reduces emissions and increases availability of secondary raw materials for renewable energy generation, only absolute reductions in energy demand through sufficiency-oriented lifestyles and sustainable choices in food, housing, and mobility seem able to achieve emission reductions needed to stay within 1.5-degree warming. Here, international policies are needed to create globally equitable opportunities for decent lifestyles in a safe and just planetary space

The economic incidence of social security contributions: A discontinuity approach with linked employer-employee data

We estimate economic incidence of social security contributions (SSC) on the basis of cross-sectional earnings distributions. The approach exploits discontinuities in earnings distributions at kinks in the budget set which are informative about tax incidence. Contrary to most research on SSC incidence, it does not rely on policy reforms, panel data, or hours information. When the location of kinks does not change significantly, estimates represent equilibrium incidence and are less affected by short-run adjustment frictions than results based on policy reforms. We refine the framework proposed by Alvaredo and Saez (2007), discuss identifying assumptions and related problems in empirical applications. We also suggest parametric and non- parametric estimators. The approach is applied to earnings caps of SSC in Germany where the marginal SSC rate drops to zero. The linked employer-employee data used provide precise measures of gross and net earnings. Utilizing two separate earnings distributions improves identification in the presence of measurement error. We find substantial negative discontinuities at most earnings caps of SSC in the distribution of observed net earnings. Together with smooth gross earnings distributions around the caps this provides consistent empirical evidence that legal and economic incidence of SSC coincide

OpenStreetMap for traffic simulation

Micro-simulations of traffic systems are becoming more important as highly disaggregated data, such as mobility diaries or GPS traces, become available. For accurate results, a high-quality model of the road network is required. Recently, OpenStreetMap has proven to be a valuable data source, often on par with and in some respects surpassing proprietary network models provided by administrations in usefulness

Familienarbeitszeit Reloaded: Vereinfachung durch pauschalierte Leistung und Flexibilisierung durch Arbeitszeitkorridor

Endbericht. Expertise im Auftrag der Friedrich-Ebert Stiftung

Is The Equal Sharing Of Market Work And Family Duties Hampered By Financial Means Or Constraints? Evidence From A Structural Labor Supply Model With Involuntary Unemployment And Hours Constraints

This paper analyzes the question why desired and actual sharing of market work and family duties among parents with young children in Germany fall apart. Potential explanations include financial incentives favoring the single-earner model, as well as constraints in choosing working hours due to lack of childcare or labor demand restrictions. In order to analyze these explanations, we extend the standard model of labor supply by different types of constraints. We estimate preferences based on desired instead of actual working hours and specify restrictions as a double hurdle model with the first stage representing the risk of involuntary unemployment. The second step pertains the probability of being constrained in specific hours categories. We apply this model to simulate a recent reform proposal that subsidizes parents who both work around 30 hours per week. We find that taking into account constraints in working hours is crucial: The pure incentive effect is almost cut by half. Our approach also allows to simulate the removal of hours constraints which almost triples the labor supply reaction. Hours constraints are thus even more important than adverse incentives in explaining the asymmetric division of work and care within families