Leveraging Macroscopic Fundamental Diagrams (MFDs) and Control Strategies for Sustainable Transportation Networks
Bibliographic record
Abstract
Urban transportation networks form the backbone of modern societies, enabling essential mobility and economic activities while presenting intricate challenges associated with traffic congestion, emissions, and environmental sustainability. This thesis presents a comprehensive and integrated study framework to enhance the robustness and sustainability of urban transportation infrastructure. With a primary focus on the Macroscopic Fundamental Diagram (MFD) and its associated control strategies, the research focuses on the complex dynamics of network performance, emphasizing the critical interplay between congestion management, bottleneck identification, and the reduction of environmental impact within complex urban environments. The initial chapters provide a comprehensive overview of the challenges inherent within contemporary urban transportation systems, emphasizing the adverse impacts of traffic congestion and emissions on network efficiency and the environment. Leveraging the principles of the MFD, the study investigates the diverse dynamics of network performance, demonstrating the pivotal role of network configurations and congestion distributions in shaping traffic flow patterns and emissions. Subsequent chapters extend the research framework by introducing novel concepts such as the heterogeneity-aware emission Macroscopic Fundamental Diagram (e-MFD) and the innovative eMFD controller. The study showcases the efficacy of these novel approaches in mitigating environmental impact and optimizing network performance by leveraging the MFD, eMFD, and the Model Predictive Controller (MPC). Developing comprehensive decision tree models for bottleneck identification and management further enhances the reliability and applicability of traffic control strategies within complex urban environments. In conclusion, this thesis serves as a seminal contribution to the field of transportation engineering, offering a comprehensive and integrated framework for policymakers and transportation authorities to develop sustainable strategies for enhancing urban transportation infrastructure's performance and environmental sustainability. The findings presented within this research provide a solid foundation for future research endeavours and underscore the imperative of holistic approaches in shaping the future of sustainable urban transportation networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".