A cost-effective methodology to compare travel time and speed: a tale of 11 cities
Bibliographic record
Abstract
Urban commuters and road authorities are interested in knowing how well their city can manage traffic conditions. It is challenging to make a fair comparison among global cities due to the absence of a consistent, publicly accessible and inexpensive framework. This study proposes a methodology to compare travel time and average speed in different cities. Large cities have a central core, in which many of the major activities take place. This core is called downtown or the central business district (CBD). In this study, a comparison is made by simulating the morning commute trips from origins outside to destinations inside the CBD. The proposed framework outlines how the city and the CBD borders are defined. The data are collected by an accessible location service (Google Maps Distance Matrix Application Programming Interface). The framework is conducted on 11 major cities from the five continents, namely Cairo, London, Los Angeles, Melbourne, Moscow, New York, Paris, Singapore, Sydney, Tehran and Toronto. As such Singapore and Cairo had the shortest, while London and Paris had the longest travel times. According to the distribution graphs of average speed and travel time, Asian and African cities experienced a relatively higher average speed and a lower travel time.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".