Intelligent Transportation System: Managing Pandemic Induced Threats to the People and Economy
Bibliographic record
Abstract
Since the world wars, the world has not seen such a heavy and sustained negative impact on the people and economy, as is being caused by the pandemic COVID-19. Pandemic, like any other big change, is strongly disruptive, challenging authorities all over the world to reimagine services, logistics, people movement, and economic activities. For the national and local governments looking for resources to support traditional and novel solutions to mitigate, contain and control the pandemic's deleterious impact on the people and economy, Smart City's Intelligent Transportation System (SC-ITS) can be an important asset. Integration of the transportation advancements with artificial intelligence and information and communication technologies is interactively and dynamically empowering the ITS of Smart Cities. This integration has ushered in an era of smart city intelligent transportation services that are dynamic, adaptive and can be reconfigured to meet the smart city citizenry's changing needs in an inclusive, safe, greener, and more efficient way. We believe that by adapting and, in some cases repurposing the SC-ITS, the authorities can simultaneously increase the reach, impact, and efficacy of the solutions aimed at restarting economies while balancing both lives and livelihood concerns. This paper builds an understanding of the pandemic and then examines the SC-ITS through its components and related applications before diving deeper into the hood to examine how these components, both now and in the future, can be adapted, reconfigured and repurposed, to address the pandemic induced challenges individually and collectively. Finally, it attempts to put a perspective by evaluating the challenges and opportunities inherent in leveraging the ITS for the deployment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".