A New Approach in the GIS Bikeshed Analysis with Consideration of Topography, Street Connectivity, and Energy Consumption
Bibliographic record
Abstract
In recent years, bike planning has garnered attention from planners and the public as a sustainable mode of transportation and as a means to exercise and reduce health risks. In addition, following the success of bike-share programs in cities in Paris and Lyon, France, and Montreal, Canada, several US cities initiated similar programs. With this background, GISs have been applied to conduct a spatial analysis and produce heat maps of bike-travel demand and suitable areas for a bike-sharing program. These studies include a variety of factors, such as demographics of residents, land use, street types, and available bike facilities and transit services. However, there have been few studies that take topography and street connectivity into account. The study proposes a method to combine topography and presence of intersections with estimates of energy used to bike, and incorporate the resulting travel-impedance factor, as well as street connectivity, into a GIS analysis. Using the case in Montgomery County, Maryland, USA, where elevation and street connectivity varies substantially among neighborhoods, this study shows how the size and shape of bikesheds originating from proposed light rail stations vary in the GIS analysis with or without taking into account these critical factors. The analysis results have significant implications for various bike planning programs using a GIS analysis.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".