Advancements in multi-objective eco-routing solutions using connectivity
Bibliographic record
Abstract
Due to the significant adverse impact of transportation systems on the environment, topics related to alleviating greenhouse gas (GHG) emissions are gaining more attention. As potential solutions to mitigate GHG emissions, several approaches have been proposed to better control traffic and manage transportation systems. The employment of Intelligent Transportation System (ITS), which adopts the advancements in Information and Communication Technology (ICT), has been proposed as the most favourable approach to alleviate the undesirable impact of transportation systems on the environment. ITS can control several aspects of a network, such as speed, traffic signals, and route guidance. For the purpose of routing, this research aims to exploit the advancements in ICT by including connected and automated vehicles (CAVs) and sensing technology in an urban congested network. Anticipatory multi-objective eco-routing in a distributed routing framework was proposed and compared to myopic routing with a large case study on a congested network. The End-to-End Connected Autonomous Vehicles (E2ECAV) dynamic distributed routing framework was examined, and encouraging results were found based on the traffic and environmental perspectives. The impact of different market penetration rates (MPRs) of CAVs was examined for various traffic conditions. E2ECAV was adopted for both the myopic and anticipatory routing strategies in this dissertation. The best GHG costing approach was defined and was among the elements tackled in this research. For a robust anticipatory routing application, predictive models were developed based on Long-Short Term Memory (LSTM), a deep learning approach, while considering a high level of spatial (link level) and temporal (one minute) resolution. With regards to the LSTM predictive models, the impact was illustrated of using a deeper LSTM network and systematically tuning its hyper-parameters. The anticipatory routing strategy significantly outperformed the myopic routing strategy based on the the traffic and environmental perspectives. This research shows that ITS can help significantly reduce GHG emissions produced by transportation systems. The developed predictive models can be used while real-time data are collected from sensors within an urban network. Furthermore, the proposed anticipatory routing framework can be applied in a real-time situation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".