Integrated agent-based transport simulation and air pollution modelling in urban areas - the example of Munich
Bibliographic record
Abstract
Against the background of high air pollution levels in urban areas, which are often related to high traffic demand and lead to adverse effects on human health and the environment, there is a demand to develop transport policies to reduce air pollution in a sustainable way.This thesis focuses on the modelling of air pollutant concentration and, thereby, addresses three fields of research: transport modelling, air pollutant emission and atmospheric dispersion modelling.In each of these fields nowadays, specific methods and tools are increasingly developed to provide new valuable insights.However, owing to their complexity these approaches often cannot be integrated in order to analyse the impact of different transport policies on air pollution.Even though less detailed methods and tools are also available and suitable for decision making processes, they do not provide all the information necessary to understand the cause-and-effect chain.This thesis addresses this gap by presenting a new approach, an integrated air pollution model, which simulates the complete cause-and-effect chain from changes in transport behaviour and vehicle technology to the impact on air quality.The developed approach links agent-based transport modelling with vehicle specific emission factors based on traffic situations.As a result, it is possible to include driving dynamics, in terms of traffic situations, and vehicle attributes to calculate air pollutant emissions through the use of HBEFA emission factors.In this thesis, an emission calculation tool is first developed and integrated within the environment of the multiagent transport simulation, MATSim.It could be shown that simulated link travel times follow measured travel times and simulated emissions correlate with sophisticated emission simulations using measured driving cycles.Additionally, this methodology is projected on the real-world scenario of the Munich metropolitan area in Germany, through a large-scale simulation with MATSim.The emission level is linked to the agent causing it, as well as to where the emission level was caused forming the basis for following the cause-and-effect-chain.By mapping emissions back to their source, i.e. the road section, a disaggregated spatial analysis of air pollutant emissions is possible.Finally, the complete integrated approach from traffic activity to air pollution modelling is developed, validated and applied to the inner city area of Munich.A street canyon approach with respect to atmospheric dispersion modelling is applied and integrated with MATSim and the emission calculation tool.The integrated approach is able to simulate air pollutant concentrations for every street canyon and on an even more disaggregated level for several points distributed within the street canyon.Furthermore, locations with high air pollutant concentration levels, so-called hotspots, and the impact on this level due to changes in travel behaviour and vehicle technology can be determined.With the developed approach, transport policies can be evaluated.In the case of rising car user costs, for example, through the introduction of higher fuel taxes, traffic demand and the emission level decrease to different extents.This can be shown on an aggregated and spatially disaggregated level.Car user price elasticity of commuters differ from the one of inner-urban travel demand.The introduction of a speed limit in the inner city of Munich shows an overall decrease in car trips, a slight increase in car distance travelled by commuters and reduced air pollutant concentrations within a selected street canyon.The integrated air pollution modelling approach allows for the evaluation of a variety of further transport policies providing aggregated impacts of changes in transport behaviour and vehicle technology on traffic demand and the emission level as well as, with respect to a spatially disaggregated level, on air pollutant emissions and concentrations.As a result, it provides a basis for future research work in the modelling of agent-based transport and environmental effects as well as the application of this approach to other cities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".