Bibliographic record
Abstract
With the popularity of Big Data and urban informatics, there is increasing interest in ways to use real time data to improve transportation system operations. In many real-wold applications, demand is revealed dynamically over time, and consequently the routes are determined dynamically as well. In this thesis, contributions are made to several key components of a “smart” transit system framework where dynamic operations are driven by real time information. The first component is in dynamic routing and pricing of a fleet of vehicles. A new dynamic dial-a-ride policy is introduced that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method. By including social optimal pricing, the social welfare of the resulting system outperforms a pricing policy based on the marginal cost increase of a passenger over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20% - 31% range. The second component is in the informatics. Effective dynamic optimization of a system (routing, scheduling, fare setting, etc.) requires effective short term prediction of traveler/customer arrival using real-time data. Several recent methods for arrival process prediction, both offline and online, are investigated using real taxi data from New York. An experiment is conducted using the same data set to draw comparisons for arrival process modeling, suggesting that the temporal seasonal factors method from Ihlers et al. (2006) is more effective as an offline approach and the IntGARCH method from Matteson et al. (2011) is more effective as an online approach. The third component investigated is in the prepositioning of idle vehicles. Vehicles that are positioned at locations that take into account future demand can lead to reduced wait times for passengers and improved level of service. A dynamic relocation model is proposed that includes queueing delay to approximate the congestion effect of future demand. A linear problem is formulated based on Marianov and Serra’s (2002) work. By varying customer arrivals, the approach provides a new managerial tool to find the optimal service level.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".