Graph-based many-to-one dynamic ride-matching for shared mobility services in congested networks.
Bibliographic record
Abstract
On-demand shared mobility systems require matching of one (one-to-one) or multiple riders (many- to-one) to a vehicle based on real-time information. We propose a novel graph-based algorithm (GMO- Match) for dynamic many-to-one matching problem in the presence of traffic congestion. The proposed algorithm, which is an iterative two-step method, provides high service quality and is efficient in terms of computational complexity. GMOMatch starts with a one-to-one matching in step 1 and is followed by solving a maximum weight matching problem is step 2 to combine the travel requests. To evaluate the performance of the proposed algorithm, it is compared with a ride-matching algorithm by IBM (Simonetto et al., 2019). Both algorithms are implemented in a micro-traffic simulator to assess their performance and also their impacts on traffic congestion. Downtown Toronto road network is chosen as the study area. In comparison to IBM algorithm, GMOMatch improves the service quality and traffic travel time by 32% and 4%, respectively. A sensitivity analysis is also conducted over different parameters to show their impacts on the service quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".