Bibliographic record
Abstract
Cities around the globe struggle to create better and more equitable access to important destinations and services, all the while reducing the energy consumption and environmental impacts of mobility. An Introduction to Sustainable Transportation illustrates a new planning paradigm for sustainable transportation through case studies from around the world with hundreds of valuable resources and references, color photos, graphics and tables. The second edition builds and expands upon the highly acclaimed first edition, with new chapters on urban design and urban, regional and intercity public transportation, as well as expanded chapters on automobile dependence and equity issues; automobile cities and the car culture; the history of sustainable and unsustainable transportation; the interrelatedness of technologies, infrastructure energy and functionalities; and public policy and public participation and exemplary places, people and programs around the globe. Among the many valuable additions are discussions of autonomous vehicles (AVs), electric vehicles (EVs), airport cities, urban fabrics, urban heat island effects and mobility as a service (MaaS). New case studies show global exemplars of sustainable transportation, including several from Asia, a case study of participative and deliberative public involvement, as well as one describing life in the Vauban ecologically planned community of Freiburg, Germany. Students in affiliated sustainability disciplines, planners, policymakers and concerned citizens will find many provides practical techniques to innovate and transform transportation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.051 |
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".