Suchergebnisse

From the transport economic literature, it is known that optimal pricing of (environmental) externalities improves the urban system. In contrast to theory-based optimal pricing strategies, real-world policy setting often follows so-called "backcasting" approaches where certain targets are set, and policy measures are implemented in order to reach those targets. An example for the latter approach is the EU goal to reduce global greenhouse gas emissions in the transport sector by 20% until 2020 with respect to 1990 levels. This article aims to (i) compare optimal pricing and backcasting approaches for a specific case study in a simulation environment by identifying the contribution of each approach in EU's 2020 emission reduction target, and (ii) to determine the costs required to reach the desired targets. For this purpose, an optimal emission pricing strategy is applied to a real-world scenario of the Munich metropolitan area in Germany. The highly differentiated tolls relate to individual exhaust emissions, i.e. they are calculated using damage cost estimates from the literature and vary over time of day, with traffic situation, and with vehicle type. The results indicate that the desired reduction in CO2 emissions is not reached for optimal pricing approach, and that the initial damage costs estimates need to be multiplied by a factor of 5 in order to reach the target, yielding a price of 350 EUR/ton CO2. When aiming at a decrease of the overall emission costs by 20% (CO2 and local pollutants), the initial cost estimates need to be multiplied by a factor of 10. Furthermore, it is shown that the major contribution to the overall emission reduction stems from behavioral changes of commuters and reverse commuters rather than from urban travelers; under some circumstances, urban travelers even increase their CO2 emission levels. Hence, the study rises awareness that conflicting trends for different types of pollutants and different types of individuals are very likely: an increase in non-methane hydrocarbon levels for urban travelers and freight depicts that pricing emissions does not necessarily result in a reduction of all pollutants or of the emissions levels of all travelers. It is shown how agent-based simulations can be used to provide valuable insights and decision support in such possibly counter-intuitive situations.

The German evaluation procedure for the Federal Transport Infrastructure Plan ('Bun-desverkehrswegeplan') is a large-scale and comprehensive modeling, simulation, and eval-uation effort. An important component of the evaluation procedure is a cost-benefit analy-sis, based on the concept of resource consumption. This concept means that new transport infrastructure causes changes in the consumption of time, money, safety, environment, etc. In this paper, we show that — assuming elastic demand for the facility under consideration — the current approach is not in line with basic consumer theory. This stems from incon-sistencies between the behavioral model and the evaluation method: ignoring unobserved attributes of the different transport modes in the evaluation can lead to quite different eco-nomic gains than when these attributes are considered. Current practice in other EU coun-tries avoids this problem typically by applying the so-called rule-of-half, or by directly deriving the logsum term from the underlying logit model. However, a change in the Ger-man assessment procedure towards one of these best-practice approaches for the upcoming Federal Transport Infrastructure Plan in 2015 seems politically not feasible. We therefore propose an easily applicable procedure to include the logic of the rule-of-half into the exist-ing evaluation approach. We show that the resulting calculation yields the same result as the rule-of-half while maintaining the rest of the former evaluation method. Finally, we discuss how another German assessment scheme for urban public transit projects, which is currently under revision, fits into the proposed procedure. ; BMVBS, 960974/2011, Review and further development of the assessment methodology with a focus on the benefits components of the benefit-cost-analysis of the German national transport assessment exercise ; DFG, 92485222, Detaillierte Evaluation verkehrlicher Maßnahmen mit Hilfe von Mikrosimulation

Standard economic policy evaluation allows the realization of projects if the aggregated economic benefit outweighs their costs. The use of one single aggregated welfare measure for evaluating and ranking projects has often been criticized for many reasons. A major issue is that differentiated effects on individuals or subgroups of the population are not taken into consideration. This leads to the need for transport planning tools that provide additional information for politicians and decision makers. The microscopic multi-agent simulation approach presented in this paper is capable of helping to design better solutions in such situations. In particular, it is shown that the inclusion of individual income in utility calculations allows a better understanding of problems linked to public acceptance. First, individual income-contingent utility functions are estimated based on survey data in order to describe human mobility behavior. Subsequently, using the MATSim framework, the implementation is tested in a test scenario. Furthermore, and going beyond Franklin (2006), it is shown that the approach works in a large-scale real world example. Based on a hypothetical speed increase of public transit, effects on the welfare distribution of the population are discussed. It is shown that the identification of winners and losers seems to be quite robust. However, results indicate that a conversion or aggregation of individual utility changes for welfare analysis is highly dependent on the functional form of the utility functions as well as on the choice of the aggregation procedure. ; BMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacities

In democratically organized societies, the implementation of measures with regressive effects on welfare distribution tends to be complicated because of low public acceptance. The microscopic multiagent simulation approach presented in this paper can help to design better solutions in such situations. Income can be included in utility calculations for a better understanding of problems linked to acceptability. This paper shows how the approach can be used in policy evaluation when income is included in user preferences. With the MATSim framework, the implementation is tested in a simple scenario. Furthermore the approach works in a large-scale, real-world example. On the basis of a hypothetical price and speed increase of public transit, effects on the welfare distribution of the population are discussed. This approach, in contrast with applied economic policy analysis, allows choice modeling and economic evaluation to be realized consistently. ; BMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacities

"The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations.The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core.
The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis.
Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references.
We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here."

Our chapter starts from the assumption that car user costs are about to increase in the forthcoming decades. This is likely to have impacts on aggregated air pollutant emissions and on the spatial distribution of emissions. The concentration of some air pollutants still exceeds the limiting values prescribed by the European Union, especially in urban areas. Thus, the main focus of this chapter is the question whether a decrease in car travel demand due to higher user costs would result in an overproportional reduction of air pollutant emissions. When it comes to the discussion of cost-related transport policies, large-scale transport models are needed. However, for the analysis of air pollutant emissions, a detailed investigation of the micro level is also necessary. In order to combine both objectives, we use a multi-agent transport model for our simulations. The multi-agent transport simulation MATSim1 is able to simulate large-scale scenarios. It is also particularly suitable for calculating air pollutant emissions on a detailed level as complete daily plans are modeled and the traveler's identity is kept throughout the simulation process. For illustration purposes of the impacts on air pollutant emissions, nitrogen dioxide (NO2) is chosen. Furthermore, the transport sector is the main source of NO2 emissions and NO2 concentration limits are still often exceeded.

Integrated land-use transport models are often accused of being too complex, too coarse or too slow. We tightly couple the microscopic land use model SILO (Simple Integrated Land Use Orchestrator) with the agent-based transport simulation model MATSim (Multi-Agent Transport Simulation). The integration of the two models is person-centric. It means, firstly, that travel demand is generated microscopically. Secondly, SILO agents can query individualized travel information to search for housing or jobs (and to choose among available modes). Consequently, travel time matrices (skim matrices) are not needed anymore. Travel time queries can be done for any time of the day (instead of for one or few time periods), any x/y coordinate (instead of a limited number of zones) and take into account properties of the individual. This way, we avoid aggregation issues (e.g., large zones that disguise local differences) and we can account for individual constraints (e.g., nighttime workers who cannot commute by public transport for lack of service). Therefore, the behavior of agents is represented realistically, which allows us to simulate their reaction to novel policies (e.g., emission-class-based vehicle restrictions) and to extract system-wide effects. The model is applied in two study areas: a toy scenario and the metropolitan region of Munich. We simulate various transport and land use policies to test the model capabilities, including public transport extensions, zones restricted for private cars and land use development regulations. The results demonstrate that the increase of the model resolution and model expressiveness facilitates the simulation of such policies and the interpretation of the results.

Intro -- Preface -- References -- Organization -- Contents -- Urban Systems Modelling -- Towards an Agent-Based Simulation of Housing in Urban Beirut -- 1 Introduction -- 2 Background and Related Work -- 3 Abstract Model -- 3.1 Locations: City Structure and Economic Dynamics -- 3.2 Agents: Residential Mobility and Cultural Exchange -- 4 Exploratory Results -- 4.1 Experiment 1: Impact of Refugee Influx -- 4.2 Experiment 2: Impact of Ongoing Migration -- 4.3 Experiment 3: Interventions -- 5 Summary and Future Work -- References -- Simulating Urban Growth with Raster and Vector Models: A Case Study for the City of Can Tho, Vietnam -- 1 Introduction -- 2 State of the Art of Urban Growth Models -- 3 Case Study: The City of Can Tho -- 3.1 The City of Can Tho -- 3.2 The 2020 Local Urban Masterplan of Can Tho City -- 3.3 Description of Data -- 4 Raster Model -- 4.1 Model Description -- 4.2 Model Dynamics -- 4.3 Results -- 4.4 Model Calibration -- 5 Vector Model -- 5.1 Hypotheses (based on ) -- 5.2 Model Description -- 5.3 Model Dynamics -- 5.4 Results -- 5.5 Calibration and Validation of the Models on Can Tho Data -- 6 Comparison and Discussion -- 7 Conclusion -- References -- Integrating Behavior and Microsimulation Models -- 1 Introduction -- 2 The Scenario: Modeling the Aftermath of a Disaster -- 3 Behavior Models -- 3.1 Health Behavior Models -- 3.2 Belief-Desire-Intention Model -- 3.3 Cognitive Architectures -- 3.4 Reinforcement Learning-Based Models -- 4 Agent Design and Behavior -- 4.1 The Synthetic Population -- 4.2 Integrating the Behavior and Microsimulation Models -- 4.3 Experiments and Results -- 5 Conclusion -- References -- Agent-Based Modelling for Urban Planning Current Limitations and Future Trends -- 1 Introduction -- 2 Current Limitations in Urban Modelling -- 3 Key Challenges for Urban Modelling.

Im Rahmen dieser Arbeit wurde ein Bürger:innenrat eingerichtet, um Feedback zu Maßnahmen zur Dekarbonisierung des Berliner Verkehrssektors zu erhalten. Das generelle Ziel der Dekarbonisierung fand breite Unterstützung. Auf der Grundlage bereits vorliegender Studien wurden jeweils Diskussionen für den privaten Personenverkehr, den kommerziellen Personenverkehr, den Güterverkehr und Sonderverkehre geführt. Für alle Segmente mit Ausnahme des privaten Personenverkehrs konnte eine Einigung über die Dekarbonisierungspfade erzielt werden. Für Letzteren werden Pull-Maßnahmen, wie zum Beispiel Verbesserungen des öffentlichen Nahverkehrs oder der Radwege, allgemein akzeptiert, was mit dem generellen Wunsch nach Umstellung auf nicht-fossile Antriebe und einer Gestaltung eines weniger autozentrierten Berlins einhergeht. Simulationen zeigen allerdings, dass diese Pull-Maßnahmen bei Weitem nicht ausreichen werden, um den privaten Personenverkehr zu dekarbonisieren. Wirksamere Push-Maßnahmen, wie höhere Preise oder Verbote fossiler Fahrzeuge, erzielten für dieses Segment jedoch keine klaren Mehrheiten.

Tolls are frequently discussed policies to reduce traffic in cities. However, road pricing measures are seldom implemented due to high investments and unpopularity. Transportation planning tools can support planning authorities by solving those problems if they take into account the following aspects: – Demographic attributes like income and time constraints – Time reactions to the policy – Schedule changes of population's individuals during the whole day Our approach uses multi-agent simulations to model and simulate full daily plans. Each of our agents has a utility function that appraises the performance of a typical, microscopically simulated day. The sum of all utility changes to a policy change can be interpreted as the change in the system's welfare thus the economic evaluation of a measure straightforward. The approach is tested with travel behavior of the Zurich metropolitan region in Switzerland. Several tolling schemes are investigated. It is shown that the simulation can be used to model travelers' reactions to time-dependent tolls in a way most existing transportation planning tools are not able to do. It is demonstrated that route adjustment only, as is done in many traditional transport planning packages, results in no economic gains from the tolls. As time-dependent tolls are a much-debated subject in transportation politics, the ability to fully model such tolls and the reactions of travelers may help to find better toll schemes. In a world where individuals have more and more freedom to schedule their daily plans, agent-based simulations offer an intuitive way to research complex topics with lots of interdependencies.

The Covid-19 disease has caused a world-wide pandemic with more than 60 million positive cases and more than 1.4 million deaths by the end of November 2020. As long as effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, self-isolation and quarantine as well as far-reaching shutdowns of economic activity and public life are the only available strategies to prevent the virus from spreading. These interventions must meet conflicting requirements where some objectives, like the minimization of disease-related deaths or the impact on health systems, demand for stronger counter-measures, while others, such as social and economic costs, call for weaker counter-measures. Therefore, finding the optimal compromise of counter-measures requires the solution of a multi-objective optimization problem that is based on accurate prediction of future infection spreading for all combinations of counter-measures under consideration. We present a strategy for construction and solution of such a multi-objective optimization problem with real-world applicability. The strategy is based on a micro-model allowing for accurate prediction via a realistic combination of person-centric data-driven human mobility and behavior, stochastic infection models and disease progression models including micro-level inclusion of governmental intervention strategies. For this micro-model, a surrogate macro-model is constructed and validated that is much less computationally expensive and can therefore be used in the core of a numerical solver for the multi-objective optimization problem. The resulting set of optimal compromises between counter-measures (Pareto front) is discussed and its meaning for policy decisions is outlined.

Epidemiological simulations as a method are used to better understand and predict the spreading of infectious diseases, for example of COVID-19. This paper presents an approach that combines a well-established approach from transportation modelling that uses person-centric data-driven human mobility modelling with a mechanistic infection model and a person-centric disease progression model. The model includes the consequences of different room sizes, air exchange rates, disease import, changed activity participation rates over time (coming from mobility data), masks, indoors vs. outdoors leisure activities, and of contact tracing. It is validated against the infection dynamics in Berlin (Germany). The model can be used to understand the contributions of different activity types to the infection dynamics over time. It predicts the effects of contact reductions, school closures/vacations, masks, or the effect of moving leisure activities from outdoors to indoors in fall, and is thus able to quantitatively predict the consequences of interventions. It is shown that these effects are best given as additive changes of the reproduction number R. The model also explains why contact reductions have decreasing marginal returns, i.e. the first 50% of contact reductions have considerably more effect than the second 50%. Our work shows that is is possible to build detailed epidemiological simulations from microscopic mobility models relatively quickly. They can be used to investigate mechanical aspects of the dynamics, such as the transmission from political decisions via human behavior to infections, consequences of different lockdown measures, or consequences of wearing masks in certain situations. The results can be used to inform political decisions. ; DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin

Aus den Mobilitätsdaten geht hervor, dass die Kontaktreduzierungen von der Bevölkerung diszipliniert eingehalten wurden. Sollte eine Inzidenz von 50/100.000 erreicht werden, sind leichte Lockerungen möglich. Jedoch: Zu starke Lockerungen würden zum jetzigen Zeitpunkt zu einem sofortigen Wiederanstieg der Infektionszahlen führen. Politischer und gesellschaftlicher Konsens muss sein, dass R<1 politisches Ziel ist. Wir freuen uns, dass Prof. Sophia Becker diesmal als Gastautorin am Bericht mitgearbeitet hat, u.a., um eine verhaltenspsychologische Perspektive einzubringen. ; BMBF, 01KX2022A, MODUS-COVID: Modellgestützte Untersuchung von Schulschließungen und weiteren Maßnahmen zur Eindämmung von Covid-19 - Teilprojekt 1: Urbane und regionale Simulation auf Basis von datengestützten, synthetischen Bewegungsprofilen

Durch die sehr viel stärker ansteckende Delta-Variante, die seit Mitte des Jahres in Deutschland die vorherrschende Variante ist, sind neue Herausforderungen entstanden. Wir untersuchen die Wirkungen verschiedener in Politik und Medien diskutierter Maßnahmen. Wir zeigen, dass generell solche Maßnahmen besser wirken, die auch explizit bereits immunisierte Menschen miteinbeziehen - etwa durch regelmäßiges Testen. Laut unserer Simulationen könnten die derzeit vorgesehenen Maßnahmen "2G+ in öffentlichen (Freizeit-)Einrichtungen" plus "3G/Homeoffice bei der Arbeit" plus "schnelles Boostern" in Bundesländern mit hohen Impfquoten ausreichen, um den R-Wert unter 1 zu drücken und damit die aktuell rasant ansteigenden Fallzahlen zu bremsen (vgl. Abschnitt 3 und 4). Zusätzlich erläutern wir, dass bei den Maßnahmen zur Infektionsbekämpfung alle Bevölkerungsgruppen bedacht werden sollten, da die verschiedenen Gruppen alle einen - wenn auch unterschiedlichen - Beitrag zur Belastung des Gesundheitssystems leisten (vgl. Abschnitt 5 und 6). ; BMBF, 01KX2022A, MODUS-COVID: Modellgestützte Untersuchung von Schulschließungen und weiteren Maßnahmen zur Eindämmung von Covid-19 - Teilprojekt 1: Urbane und regionale Simulation auf Basis von datengestützten, synthetischen Bewegungsprofilen