Big Data in Transportation Program Management: Findings and Interpretations from the City of Toronto
Bibliographic record
Abstract
Among North American big cities, the Toronto experiences some of the worst traffic congestion (1). Traffic congestion remains the object of policy intervention across many cities, enabling public discourse about desired future transportation services and better transportation policy. and business analytics have emerged as a potentially critical group of analyses, technologies, and means of informing program management, but what does Big Data really mean for program management in big cities facing the effects of traffic congestion. In this study, is defined as the proliferation of new information on transportation flow, speeds, and trip information from probe data, global positioning data, and Bluetooth technology in near-real time, all in such volumes that make conventional computing methods unable to manage the challenge. Although Big Data appears to be a catch phrase with a somewhat ambiguous meaning, there are reasons to believe that it may have important benefits for program management. First, this is illustrated by conceptually discussing how is different than other established analytical methods for performance monitoring. Second, empirical results from this study using archived probe speed data purchased from Inrix, Inc. for 2011, 2013, and 2014 on are shown to illustrate one initiative taken on by the City of Toronto to more tightly integrate solutions into road surface program management and performance monitoring.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".