Expanding cities and vehicle use in India: Differing impacts of built environment factors on scooter and car use in Mumbai
Bibliographic record
Abstract
Expanding urban populations are inducing development at the edges of Indian cities, given the constraints on land use intensification within municipal boundaries. Existing peripheral towns are becoming anchors for this new growth, creating urban agglomerations. Such areas have become preferred home locations for the working poor and emergent middle class, groups that are often priced out of the urban housing market. However, many such exurban locations lack infrastructure such as durable paved roads and transit, because investments are largely clustered within municipal boundaries. This paper focuses on the Greater Mumbai Region and relies on a cross-sectional household travel survey data set. The objective is to understand how vehicle use is linked to the built environment and socio-economics. Spatial analysis shows that cars are used in urban centres while scooters and motorcycles are used in the exurbs. Estimated censored regression models show that greater household distance from the main employment centre Nariman Point, better job accessibility and improved socio-economic factors increase vehicle use, while land use diversity and density bring down vehicle use. A key econometric result is that after controlling for location, land use, infrastructure supply and socio-economics, the expectation of a motorised two-wheeler or car in a household does not translate to its use. Overall, the findings suggest that policies encouraging higher land use diversity, density and transit supply have the potential to marginally decrease vehicle use in the Indian metropolis. However, future research needs to focus on residential location to better understand how the choices of where to live and how to travel are interconnected.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".