A data-driven complex network approach for planning sustainable and inclusive urban mobility hubs and services
Bibliographic record
Abstract
New mobility services that facilitate multimodal options are important for strategic urban transport systems planning. Part of this strategy is municipal investment in urban mobility hubs to increase access to mobility services. We present a new evaluation framework and algorthim to locate and assess the sustainability and equity impacts of hubs in cities. Scenarios are used to evaluate hub investment strategies in different cities that prioritize (1) current mode split, (2) high transit capacity, and (3) multimodal services. From an equity perspective, high transit capacity and multimodal hub strategies include more low-income areas than current mode split, which covers middle-income areas most. Travel times to access the nearest hub in Portland by low-income households is ∼20–40 min compared to high-income households requiring ∼25–30 min. Seattle and Vancouver perform better requiring ∼15–20 min for low-income compared to ∼25–35 min for high-income households. Multimodal hubs are the most efficient requiring ∼15–20 minutes to reach compared to ∼15–30 minutes for high capacity and current mode split scenarios. From a sustainability perspective, ∼10%–50% of the population cannot reach a hub within 30 minutes by public transit compared to <10% by car, and travel time to reach the nearest hub in all three cities by car is <20 min compared to ∼20–40 min by public transit. Between all cities, low-income households representing ∼2%–15% of the total population have no access to a hub by public transit within 30 min compared to high-income households representing ∼1%–3% of the total population. Only in Portland are there low-income households not able to reach a hub by car, and in each city, all high-income households can reach at least one hub by car within 30 min. Our results show how municipalities can strategically invest in public transit and multimodal options to increase the frequency, quality, and overall mobility for low- and medium-income households and improve access to essential amenities for more vulnerable citizens. Municipalities can use our hub evaluation framework to explore alternative transport investment scenarios and spatially locate urban hubs to meet future travel demand, increase adoption of multimodal services, and improve equitable access for all citizens.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".