Innovative Mobility Services & Technologies: A Pathway Towards Transit Flexibility, Convenience, and Choice.
Bibliographic record
Abstract
The number of senior citizens is expected to double by the year 2020, representing 18% of the nation’s population. After age 75, driving performance begins to decline due to changes in health and medication effects. Indeed, one quarter of seniors over 75 are expected to require alternative transportation services in the future. This chapter examines transit and innovative mobility options to better meet the needs of the growing older population in the near (2011) and more distant (2021) future.Barriers to transit use among older adults include anxiety and confusion about using transit; inconvenience; cost and payment; safety; and physical discomfort. Emerging intelligent transportation systems (ITS) technologies can help to overcome these barriers and provide alternative mobility options, such as real-time information, simpler payment, demand-responsive door-to-door services, carsharing, and smart parking linked to transit. Other approaches include user training, smaller and more comfortable vehicles, and low-floor buses. While the scaling and cost reduction benefits of ITS are exciting, there are several obstacles to wide-scale deployment. One of the most significant is coordination among health and human service and transportation providers, particularly in suburban and rural locations. Some operators already struggle to provide services, and many staffers have limited experience with ITS technology. Thus, a concerted effort is needed across many different types of transit agencies to share information and compatible technologies. In the future, coordination strategies and ITS technologies will play a critical role in providing more flexibility, convenience, and choice for older travelers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.008 |
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".