Trip Reservation and Intelligent Planning (TRiP) for a Hyper-congestion-free Traffic System: In the Context of Pervasive Connectivity, Driving Automation and MaaS
Bibliographic record
Abstract
In most cities worldwide, demand for travel exceeds the existing limited road capacity, which causes extended hours of hyper-congestion every day. Hyper-congestion starts when and where demand exceeds capacity on a link, a road or an area, causing capacity loss and severe performance degradation. In this thesis, we design and develop a proof-of-concept system for a trip-level, link-based, in-advance trip reservation and intelligent planning system, dubbed “TRiP”. TRiP is a network-wide methodological system and software platform that facilitates the pacing of travel demand into the existing road network without exceeding the link-level capacity. The system is intended for real-time application, using a dynamic, disaggregated, spatiotemporal, in-advance reservation approach. This thesis introduces three different interconnected pillars. The first is TRiP Abstraction (TRiNAT), which is a set of network abstraction tools developed to facilitate micro traffic management systems. The second pillar is TRiP Assignment (TRiFree), which is a novel traffic assignment algorithm for trip-level, link-based, in-advance reservation traffic control systems that aim to keep all road links hyper-congestion-free. TRiFree, coupled with TRiNAT and a frontend reservation app, create the third pillar: the Trip Reservation and Intelligent Planning (TRiP) platform.To ensure the scalability of the proposed methodology to practical city and region sizes, TRiP was tested on the Toronto, Canada, road network, which includes more than 8,434 road links and serves more than 501,800 vehicles during the peak of the weekday morning commute. Our results show a very promising reduction in total travel time of 18.9-26.6% and a reduction in total travelled distance of 0-2%. The simulation results of the various scenarios outperform existing techniques and introduce a holistic robust solution for enjoying a hyper-congestion-free road network. Unlike other propositions in the literature, TRiP is scalable for larger road networks. Therefore, this thesis offers a major contribution to state-of-the-art and state-of-the-practice traffic control systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".