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Traffic and transportation are main contributors to the global CO2 emissions and resulting climate change. Especially in urban areas, traffic flow is not optimal and thus offers possibilities to reduce emissions. The concept of a Green Wave, i.e., the coordinated switching of traffic lights in order to favor a single direction and reduce congestion, is often discussed as a simple mechanism to avoid breaking and accelerating, thereby reducing fuel consumption. On the other hand, making car use more attractive might also increase emissions. In this study, we use an agent-based model to investigate the benefit of a Green Wave in order to find out whether it can outweigh the effects of increased car use. We find that although the Green Wave has the potential to reduce emissions, there is also a high risk of heaving a net increase in emissions, depending on the specifics of the traffic system.
Elisabeth Bloder; Georg Jäger. Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies. Sustainability 2021, 13, 5411 .
AMA StyleElisabeth Bloder, Georg Jäger. Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies. Sustainability. 2021; 13 (10):5411.
Chicago/Turabian StyleElisabeth Bloder; Georg Jäger. 2021. "Is the Green Wave Really Green? The Risks of Rebound Effects When Implementing “Green” Policies." Sustainability 13, no. 10: 5411.
Inthisstudywe investigate the different effects of urban and rural mobility behaviour on congestion and emissions. For this we use a mesoscopic hybrid agent-based network traffic model to simulate traffic in a city on a 1:1 scale. The main advantage of the used model is that it does not need origin-destination data as an input, but rather calculates this information based on mobility behaviour. This makes it possible to produce a population of urban agents, but giving them typical rural mobility behaviour. This changes how much they travel, what method of transport they use, as well as the reason and length of their trips. We can directly compare the resulting congestion, CO\(_2\) emissions and NO\(_X\) emissions with local and temporal resolution and investigate the differences. We find that mobility behaviour has a paramount effect on the traffic system. Simulating an urban area, but using rural mobility behaviour, leads to an increase in emissions of roughly 70% inside the city limits and heavy congestion throughout the city. This result highlights the importance of understanding and shaping mobility behaviour when looking for a sustainable solution to the problems of transportation and mobility.
Simon Plakolb; Georg Jäger; Christian Hofer; Manfred Füllsack. The Effect of Urban and Rural Mobility Behaviour on Congestion and Emissions Resulting from Private Motorized Traffic. First Complex Systems Digital Campus World E-Conference 2015 2021, 541 -550.
AMA StyleSimon Plakolb, Georg Jäger, Christian Hofer, Manfred Füllsack. The Effect of Urban and Rural Mobility Behaviour on Congestion and Emissions Resulting from Private Motorized Traffic. First Complex Systems Digital Campus World E-Conference 2015. 2021; ():541-550.
Chicago/Turabian StyleSimon Plakolb; Georg Jäger; Christian Hofer; Manfred Füllsack. 2021. "The Effect of Urban and Rural Mobility Behaviour on Congestion and Emissions Resulting from Private Motorized Traffic." First Complex Systems Digital Campus World E-Conference 2015 , no. : 541-550.
How people react towards threatening information such as climate change is a non-trivial matter. While people with a high environmental self-identity tend to react approach-motivated by engaging in pro-environmental behaviour, people of low environmental self-identity may exhibit proximal defence behaviour, by avoiding and distracting themselves from potentially threatening stimuli caused by identified anxious thoughts and circumstances. This psychological theory has recently been tested in experimental studies in which results suggest that the promotion of climate change information can also backfire. Based on these findings, we propose an agent-based model to address influences on anxiety and correlated pro-environmental actions in relation to societal norms of climate change scepticism and environmental self-identity.
Marie L. Kapeller; Georg Jäger; Manfred Füllsack. Social Norms and the Threat of Climate Change: An Agent-Based Model to Investigate Pro-Environmental Behaviour. First Complex Systems Digital Campus World E-Conference 2015 2021, 445 -457.
AMA StyleMarie L. Kapeller, Georg Jäger, Manfred Füllsack. Social Norms and the Threat of Climate Change: An Agent-Based Model to Investigate Pro-Environmental Behaviour. First Complex Systems Digital Campus World E-Conference 2015. 2021; ():445-457.
Chicago/Turabian StyleMarie L. Kapeller; Georg Jäger; Manfred Füllsack. 2021. "Social Norms and the Threat of Climate Change: An Agent-Based Model to Investigate Pro-Environmental Behaviour." First Complex Systems Digital Campus World E-Conference 2015 , no. : 445-457.
Recent research suggests that new technologies are important drivers of empirically observed labour market polarisation. Many analyses in the field of economics are conducted to evaluate the changing share of employment in low-skill, medium-skill and high-skill occupations over time. This occupation-based approach, however, may neglect the relevance of specific skills and skill bundles, which potentially can be used to explain the observable patterns of labour market polarisation. This paper adds to the literature in two ways: First, we present the results of an analysis of data on job vacancies rather than the currently employed and, second, we derive occupation-defining skills using network analysis tools. The analysis and tool usage allowed us to investigate polarisation patterns in Austrian vacancy data from 2007 to 2017 and identify changes in the skills demanded in job vacancies in Austria. In contrast to most previous research, we find no evidence for polarisation, but rather a trend towards upskilling.
Laura S. Zilian; Stella S. Zilian; Georg Jäger. Labour market polarisation revisited: evidence from Austrian vacancy data. Journal for Labour Market Research 2021, 55, 1 -17.
AMA StyleLaura S. Zilian, Stella S. Zilian, Georg Jäger. Labour market polarisation revisited: evidence from Austrian vacancy data. Journal for Labour Market Research. 2021; 55 (1):1-17.
Chicago/Turabian StyleLaura S. Zilian; Stella S. Zilian; Georg Jäger. 2021. "Labour market polarisation revisited: evidence from Austrian vacancy data." Journal for Labour Market Research 55, no. 1: 1-17.
Traditional agent-based modelling is mostly rule-based. For many systems, this approach is extremely successful, since the rules are well understood. However, for a large class of systems it is difficult to find rules that adequately describe the behaviour of the agents. A simple example would be two agents playing chess: Here, it is impossible to find simple rules. To solve this problem, we introduce a framework for agent-based modelling that incorporates machine learning. In a process closely related to reinforcement learning, the agents learn rules. As a trade-off, a utility function needs to be defined, which is much simpler in most cases. We test this framework to replicate the results of the prominent Sugarscape model as a proof of principle. Furthermore, we investigate a more complicated version of the Sugarscape model, that exceeds the scope of the original framework. By expanding the framework we also find satisfying results there.
Georg Jäger. Using Neural Networks for a Universal Framework for Agent-based Models. Mathematical and Computer Modelling of Dynamical Systems 2021, 27, 162 -178.
AMA StyleGeorg Jäger. Using Neural Networks for a Universal Framework for Agent-based Models. Mathematical and Computer Modelling of Dynamical Systems. 2021; 27 (1):162-178.
Chicago/Turabian StyleGeorg Jäger. 2021. "Using Neural Networks for a Universal Framework for Agent-based Models." Mathematical and Computer Modelling of Dynamical Systems 27, no. 1: 162-178.
We investigate the possibility to apply a method of calculus analytics developed for predicting critical transitions in complex systems to social systems modelled with agent-based methods (ABMs). We introduce this method on the example of an equation-based modelled system and subsequently test it—to our knowledge for the first time—on ABMs. Our experiments show that the method may have wide applicability in the analysis of social systems. The method can help to approximate abrupt and thus unpredictable regime shifts, even though it may be constrained by stochastics and require a bit more experimentation in selecting suitable variables for making it work in ABMs.
Manfred Füllsack; Simon Plakolb; Georg Jäger. Predicting regime shifts in social systems modelled with agent-based methods. Journal of Computational Social Science 2020, 4, 163 -185.
AMA StyleManfred Füllsack, Simon Plakolb, Georg Jäger. Predicting regime shifts in social systems modelled with agent-based methods. Journal of Computational Social Science. 2020; 4 (1):163-185.
Chicago/Turabian StyleManfred Füllsack; Simon Plakolb; Georg Jäger. 2020. "Predicting regime shifts in social systems modelled with agent-based methods." Journal of Computational Social Science 4, no. 1: 163-185.
We present results of attempts to expand and enhance the predictive power of Early Warning Signals (EWS) for Critical Transitions (Scheffer et al. 2009) through the deployment of a Long-Short-Term-Memory (LSTM) Neural Network on agent-based simulations of a Repeated Public Good Game, which due to positive feedbacks on experience and social entrainment transits abruptly from majority cooperation to majority defection and back. Our method extension is inspired by several known deficiencies of EWS and by lacking possibilities to consider micro-level interaction in the so far primarily used simulation methods. We find that
Manfred Füllsack; Marie Kapeller; Simon Plakolb; Georg Jäger. Training LSTM-neural networks on early warning signals of declining cooperation in simulated repeated public good games. MethodsX 2020, 7, 100920 .
AMA StyleManfred Füllsack, Marie Kapeller, Simon Plakolb, Georg Jäger. Training LSTM-neural networks on early warning signals of declining cooperation in simulated repeated public good games. MethodsX. 2020; 7 ():100920.
Chicago/Turabian StyleManfred Füllsack; Marie Kapeller; Simon Plakolb; Georg Jäger. 2020. "Training LSTM-neural networks on early warning signals of declining cooperation in simulated repeated public good games." MethodsX 7, no. : 100920.
In order to meet the challenges of sustainable development, it is of utmost importance to involve all relevant decision makers in this process. These decision makers are diverse, including governments, corporations and private citizens. Since the latter group is the largest and the majority of decisions relevant to the future of the environment is made by that group, great effort has been put into communicating relevant research results to them. The hope is that well-informed citizens make well-informed choices and thus act in a sustainable way. However, this common but drastic simplification that more information about climate change automatically leads to pro-environmental behaviour is fundamentally flawed. It completely neglects the complex social-psychological processes that occur if people are confronted with threatening information. In reality, the defence mechanisms that are activated in such situations can also work against the goal of sustainable development, as experimental studies showed. Based on these findings, we propose an agent-based model to understand the relation between threatening climate change information, anxiety, climate change scepticism, environmental self-identity and pro-environmental behaviour. We find that the exposure to information about climate change, in general, does not increase the pro-environmental intent unless several conditions regarding the individual’s values and information density are met.
Marie Lisa Kapeller; Georg Jäger. Threat and Anxiety in the Climate Debate—An Agent-Based Model to Investigate Climate Scepticism and Pro-Environmental Behaviour. Sustainability 2020, 12, 1823 .
AMA StyleMarie Lisa Kapeller, Georg Jäger. Threat and Anxiety in the Climate Debate—An Agent-Based Model to Investigate Climate Scepticism and Pro-Environmental Behaviour. Sustainability. 2020; 12 (5):1823.
Chicago/Turabian StyleMarie Lisa Kapeller; Georg Jäger. 2020. "Threat and Anxiety in the Climate Debate—An Agent-Based Model to Investigate Climate Scepticism and Pro-Environmental Behaviour." Sustainability 12, no. 5: 1823.
The footprint of tourism through travel is contributing significantly to the accumulation of human-made CO2. Due to different options in transportation, resulting emissions depend strongly on the choices of individuals on how to travel. In Austria, land travel is the main mode of transportation, though air travel has shown a significant increase during the last decades. We present a model to estimate past and future emission trends of land and air travel for domestic (inbound) and international (outbound) travel destinations. For this, we use a combination of two software models, a social-economic individual-based model to simulate the decision processes of holiday travel and an emission calculation model to estimate single travel-based CO2 emissions. Our model is supported by data (reference year 2016) on tourism demand, holiday destinations, household wealth and emissions of different transportation modes. Our model evaluation successfully reproduced historical data of travel demand in the period 2003–2019 and explores several future trends of (a) business-as-usual, (b) green transition and (c) aviation preference increase. We calculated a current CO2 footprint of 5.8 million tonnes in 2019, which could increase to 7.3 million tonnes by 2030 if the current trend continues. A necessary decrease of transportation emissions is only possible when reducing air travel. In case of a green transition towards more land travel, total emissions could be kept constant compared to current emission levels. However, an overall reduction of holiday travel related CO2 below 3.5 million tonnes has not been observed even under the best circumstances due to projected increases in the total population and increases in wealth.
Marie Lisa Kapeller; Manfred Füllsack; Georg Jäger. Holiday Travel Behaviour and Correlated CO2 Emissions—Modelling Trend and Future Scenarios for Austrian Tourists. Sustainability 2019, 11, 6418 .
AMA StyleMarie Lisa Kapeller, Manfred Füllsack, Georg Jäger. Holiday Travel Behaviour and Correlated CO2 Emissions—Modelling Trend and Future Scenarios for Austrian Tourists. Sustainability. 2019; 11 (22):6418.
Chicago/Turabian StyleMarie Lisa Kapeller; Manfred Füllsack; Georg Jäger. 2019. "Holiday Travel Behaviour and Correlated CO2 Emissions—Modelling Trend and Future Scenarios for Austrian Tourists." Sustainability 11, no. 22: 6418.
In the standard situation of networked populations, link neighbours represent one of the main influences leading to social diffusion of behaviour. When distinct attributes coexist, not only the network structure, but also the distribution of these traits shape the typical neighbourhood of each individual. While assortativity refers to the formation of links between similar individuals inducing the network structure, here, we separate the formation of links from the actual distribution of an attribute on the topology. This is achieved by first generating different network types (e.g., lattice, scale free, and small world), followed by the procedure of distributing attributes. With this separation, we try to isolate the effect that attribute distribution has on network diffusion from the effect of the network structure itself. We compare random distributions, where behaviour types are highly mixed, and homophilic distributions, where similar individuals are very likely to be linked, and examine the effects on social contagion in a population of mainly reciprocal behaviour types. In addition, we gradually mix homophilic distribution, by random rewiring, adding links and relocating individuals. Our main results is that attribute distribution strongly influences collective behaviour and the actual effect depends on the network type. Under homophilic distribution the equilibrium collective behaviour of a population tends to be more divers, implying that random distributions are limited for illustration of collective behaviour. We find that our results are robust when we use different gradual mixing methods on homophilic distribution.
Marie Lisa Kapeller; Georg Jäger; Manfred Füllsack. Homophily in networked agent-based models: a method to generate homophilic attribute distributions to improve upon random distribution approaches. Computational Social Networks 2019, 6, 1 -18.
AMA StyleMarie Lisa Kapeller, Georg Jäger, Manfred Füllsack. Homophily in networked agent-based models: a method to generate homophilic attribute distributions to improve upon random distribution approaches. Computational Social Networks. 2019; 6 (1):1-18.
Chicago/Turabian StyleMarie Lisa Kapeller; Georg Jäger; Manfred Füllsack. 2019. "Homophily in networked agent-based models: a method to generate homophilic attribute distributions to improve upon random distribution approaches." Computational Social Networks 6, no. 1: 1-18.
Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling’s segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling’s model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour.
Georg Jäger. Replacing Rules by Neural Networks A Framework for Agent-Based Modelling. Big Data and Cognitive Computing 2019, 3, 51 .
AMA StyleGeorg Jäger. Replacing Rules by Neural Networks A Framework for Agent-Based Modelling. Big Data and Cognitive Computing. 2019; 3 (4):51.
Chicago/Turabian StyleGeorg Jäger. 2019. "Replacing Rules by Neural Networks A Framework for Agent-Based Modelling." Big Data and Cognitive Computing 3, no. 4: 51.
Our current labour market is affected by massive changes like digitalization, automation and globalization, which gives rise to completely new forms of generating income. One such innovative idea is crowdworking, where many people (a so-called crowd) work on individual tasks for a firm in a way similar to a self-employed freelancer. This form of occupation is a recent development but gains acceptance, esteem and relevance quite rapidly. The risk potential for wage dumping and (self-) exploitation is still unknown. A crucial, but often neglected fact about crowdworking is that it exists in many variants which have completely different properties. We investigate how much these distinct versions of crowdworking differ by using an agent-based computer simulation. Wages, job security, workforce composition and other relevant indicators are calculated by simulating the micro scale to gain aggregated information on the macro-scale. We find that there is a significant difference between the versions of crowdworking. Our main finding is that especially variants where the crowdworkers are able to set their own wages are susceptible to wage dumping. Simulations suggest that this phenomenon is independent of the specifics of the labour market but rather a fundamental property of those variants of crowdworking.
Georg Jäger; Laura S. Zilian; Christian Hofer; Manfred Füllsack. Crowdworking: working with or against the crowd? Journal of Economic Interaction and Coordination 2019, 14, 761 -788.
AMA StyleGeorg Jäger, Laura S. Zilian, Christian Hofer, Manfred Füllsack. Crowdworking: working with or against the crowd? Journal of Economic Interaction and Coordination. 2019; 14 (4):761-788.
Chicago/Turabian StyleGeorg Jäger; Laura S. Zilian; Christian Hofer; Manfred Füllsack. 2019. "Crowdworking: working with or against the crowd?" Journal of Economic Interaction and Coordination 14, no. 4: 761-788.
The increasing use of electric vehicles, combined with the trend of higher charging currents, puts a significant strain on the electrical grid. Many solutions to this problem are being discussed, some relying on some form of smart grid, others proposing stricter regulations concerning charging electric vehicles. In this study, a different approach, called randomly delayed charging, is explored. The main idea is to charge a battery over night, but instead of starting the charging process as soon as possible, introduce a random delay, satisfying the boundary condition that the battery is sufficiently charged in the morning. Benefits of this technique are investigated by using an agent-based simulation that simulates commuters and calculates the electricity demand with temporal resolution. Results suggest that randomly delayed charging can have a significant effect on peak load caused by charging and that this benefit increases the higher the used charging current is. Randomly delayed charging can be a viable option for reducing the peak electricity demand that is caused by charging electric vehicles.
Georg Jäger; Christian Hofer; Manfred Füllsack. The Benefits of Randomly Delayed Charging of Electric Vehicles. Sustainability 2019, 11, 3722 .
AMA StyleGeorg Jäger, Christian Hofer, Manfred Füllsack. The Benefits of Randomly Delayed Charging of Electric Vehicles. Sustainability. 2019; 11 (13):3722.
Chicago/Turabian StyleGeorg Jäger; Christian Hofer; Manfred Füllsack. 2019. "The Benefits of Randomly Delayed Charging of Electric Vehicles." Sustainability 11, no. 13: 3722.
Motorized transport is one of the main contributors to anthropogenic CO 2 emissions, which cause global warming. Other emissions, like nitrogen oxides or carbon monoxide, are detrimental to human health. A prominent way to understand and thus be able to minimize emissions is by using traffic simulations to evaluate different scenarios. In that way, one can find out which policies, technical innovations, or behavioral changes can lead to a decrease in emissions. Since the effect of CO 2 is on a global scale, a macroscopic model is often enough to find reasonable results. However, NO x emissions can also have a direct, local effect. Therefore, it is interesting to investigate these emissions on a mesoscopic scale, to gain insight into the local distribution of this pollutant. In this study, we used a traffic model that, contrary to most other state-of-the-art traffic simulations, does not require an origin–destination matrix as an input, but calculates it from mobility behavior extracted from a survey. We then generated agents with realistic mobility behavior that perform their daily trips and calculate key features like congestion and emissions for every edge of the road network. Our approach has the additional advantage of allowing to investigate technical, juridical, as well as behavioral changes, all within the same framework. It is then possible to identify strategies that minimize NO x emissions caused by private motorized transport. Evaluation showed good agreement with reality in terms of local and temporal resolution. Especially when looking at the sum of emissions, the main feature for evaluating policies, and deviations between our simulation and available statistics were negligible. We found that, from all scenarios we investigated, the ban of old diesel cars is the most promising policy: By replacing all diesel cars built in 2005 or earlier with petrol cars of the same age, NO x emissions could drop by roughly a third.
Simon Plakolb; Georg Jäger; Christian Hofer; Manfred Füllsack. Mesoscopic Urban-Traffic Simulation Based on Mobility Behavior to Calculate NOx Emissions Caused by Private Motorized Transport. Atmosphere 2019, 10, 293 .
AMA StyleSimon Plakolb, Georg Jäger, Christian Hofer, Manfred Füllsack. Mesoscopic Urban-Traffic Simulation Based on Mobility Behavior to Calculate NOx Emissions Caused by Private Motorized Transport. Atmosphere. 2019; 10 (6):293.
Chicago/Turabian StyleSimon Plakolb; Georg Jäger; Christian Hofer; Manfred Füllsack. 2019. "Mesoscopic Urban-Traffic Simulation Based on Mobility Behavior to Calculate NOx Emissions Caused by Private Motorized Transport." Atmosphere 10, no. 6: 293.
Particulate matter pollution, especially in an urban environment, is a health risk that affects many people, and the current trend shows that these problems will increase in the near future. To combat this form of air pollution, many different strategies and policies are investigated: from reducing the emission of particulate matter to finding ways of filtering the air. In this study, we explore the idea of using trees in an urban area to reduce particulate matter concentration. Since the absorption of fine dust by trees is a complex problem, influenced by many factors, we use a computer model to simulate the effect of various trees throughout the year to find the optimal candidate. We find that coniferous trees have a significant advantage, since they also absorb during the winter months, where the air quality is worse. We also conclude that a large enough area of a well-suited tree species is a feasible way to increase air quality and in some cases even to reduce the particulate matter pollution to an acceptable level.
Chiara Letter; Georg Jäger. Simulating the potential of trees to reduce particulate matter pollution in urban areas throughout the year. Environment, Development and Sustainability 2019, 22, 4311 -4321.
AMA StyleChiara Letter, Georg Jäger. Simulating the potential of trees to reduce particulate matter pollution in urban areas throughout the year. Environment, Development and Sustainability. 2019; 22 (5):4311-4321.
Chicago/Turabian StyleChiara Letter; Georg Jäger. 2019. "Simulating the potential of trees to reduce particulate matter pollution in urban areas throughout the year." Environment, Development and Sustainability 22, no. 5: 4311-4321.
Many systems in various scientific fields like medicine, ecology, economics or climate science exhibit so-called critical transitions, through which a system abruptly changes from one state to a different state. Typical examples are epileptic seizures, changes in the climate system or catastrophic shifts in ecosystems. In order to predict imminent critical transitions, a mathematical apparatus called early warning signals has been developed and this method is used successfully in many scientific areas. However, not all critical transitions can be detected by this approach (false negative) and the appearance of early warning signals does not necessarily proof that a critical transition is imminent (false positive). Furthermore, there are whole classes of systems that always show early warning signals, even though they do not feature critical transitions. In this study we identify such classes in order to provide a safeguard against a misinterpretation of the results of an early warning signal analysis of such systems. Furthermore, we discuss strategies to avoid such systematic false positives and test our theoretical insights by applying them to real world data.
Georg Jäger; Manfred Füllsack. Systematically false positives in early warning signal analysis. PLOS ONE 2019, 14, e0211072 .
AMA StyleGeorg Jäger, Manfred Füllsack. Systematically false positives in early warning signal analysis. PLOS ONE. 2019; 14 (2):e0211072.
Chicago/Turabian StyleGeorg Jäger; Manfred Füllsack. 2019. "Systematically false positives in early warning signal analysis." PLOS ONE 14, no. 2: e0211072.
Georg Jäger; University of Graz. Using Elementary Cellular Automata to Model Different Research Strategies and the Generation of New Knowledge. Complex Systems 2018, 27, 145 -158.
AMA StyleGeorg Jäger, University of Graz. Using Elementary Cellular Automata to Model Different Research Strategies and the Generation of New Knowledge. Complex Systems. 2018; 27 (2):145-158.
Chicago/Turabian StyleGeorg Jäger; University of Graz. 2018. "Using Elementary Cellular Automata to Model Different Research Strategies and the Generation of New Knowledge." Complex Systems 27, no. 2: 145-158.
Understanding traffic is an important challenge in different scientific fields. While there are many approaches to constructing traffic models, most of them rely on origin–destination data and have difficulties when phenomena should be investigated that have an effect on the origin–destination matrix. A macroscopic traffic model is introduced that is novel in the sense that no origin–destination data are required as an input. This information is generated from mobility behavior data using a hybrid approach between agent-based modeling to find the origin and destination points of each vehicle and network techniques to find efficiently the routes most likely used to connect those points. The simulated road utilization and resulting congestion is compared to traffic data to quantitatively evaluate the results. Traffic jam avoidance behavior is included in the model in several variants, which are then all evaluated quantitatively. The described model is applied to the City of Graz, a typical European city with about 320,000 inhabitants. Calculated results correspond well with reality. The introduced traffic model, which uses mobility data instead of origin–destination data as input, was successfully applied and offers unique advantages compared to traditional models: Mobility behavior data are valid for different systems, while origin–destination data are very specific to the region in question and more difficult to obtain. In addition, different scenarios (increased population, more use of public transport, etc.) can be evaluated and compared quickly.
Christian Hofer; Georg Jäger; Manfred Füllsack. Including traffic jam avoidance in an agent-based network model. Computational Social Networks 2018, 5, 1 -12.
AMA StyleChristian Hofer, Georg Jäger, Manfred Füllsack. Including traffic jam avoidance in an agent-based network model. Computational Social Networks. 2018; 5 (1):1-12.
Chicago/Turabian StyleChristian Hofer; Georg Jäger; Manfred Füllsack. 2018. "Including traffic jam avoidance in an agent-based network model." Computational Social Networks 5, no. 1: 1-12.
Christian Hofer; Georg Jäger; Manfred Füllsack. Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach. Journal of Cleaner Production 2018, 183, 1 -10.
AMA StyleChristian Hofer, Georg Jäger, Manfred Füllsack. Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach. Journal of Cleaner Production. 2018; 183 ():1-10.
Chicago/Turabian StyleChristian Hofer; Georg Jäger; Manfred Füllsack. 2018. "Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach." Journal of Cleaner Production 183, no. : 1-10.
Christian Hofer; Georg Jäger; Manfred Füllsack. Critical transitions and Early Warning Signals in repeated Cooperation Games. Journal of Dynamics & Games 2018, 5, 223 -230.
AMA StyleChristian Hofer, Georg Jäger, Manfred Füllsack. Critical transitions and Early Warning Signals in repeated Cooperation Games. Journal of Dynamics & Games. 2018; 5 (3):223-230.
Chicago/Turabian StyleChristian Hofer; Georg Jäger; Manfred Füllsack. 2018. "Critical transitions and Early Warning Signals in repeated Cooperation Games." Journal of Dynamics & Games 5, no. 3: 223-230.