Relative Reachability Analysis as a Tool for Urban Mobility Planning
Bibliographic record
Abstract
There is a plethora of user-oriented route planning applications and systems that enable the computation of the fastest journey between two locations using different transportation modes, e.g., car, public transport, walking, bicycle. While useful for individuals, they are of limited interest to a class of users that may be interested in a more global and comparative view of transportation systems in general. In this context, we adopt the view of an urban planner. Urban planners may be interested in queries such as "if a new transit stop was to be introduced in a given location, would that bring the travel time to a given point-of-interest (POI) or area-of-interest (AOI) by bus closer to the travel time by car, therefore improving air quality and/or overall traffic congestion?" To answer queries such as this one, as well as many other interesting ones, we propose the concept of relative reachability which aims at measuring how efficient a given transportation mode is (or may be) in comparison to other competing modes. We discuss the challenges associated with the computation of relative reachability of POIs (or AOIs) within a city, which reveal directions for interesting research in spatial data management towards better informed urban mobility planning processes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".