Toward a More Meterless Parking System: User Demographic Factors Influencing Adoption and Usage of Pay by Cell (PBC) Services in Washington, DC
Bibliographic record
Abstract
The District Department of Transportation’s (DDOT) pay by cell (PBC) program for on-street parking has been very successful. Since its launch in July 2011, the program has attracted one million customers, accounting for approximately 10 million transactions and 55 percent of DC’s parking revenues. The operational and maintenance cost of the program is significantly lower than other means of paying for parking, such as coins and credit cards. The program also enjoys a high level of customer satisfaction. DC’s high adoption rates afford DDOT the opportunity to look at meterless parking. DC will be experimenting with removing meters from one side of the street as part of the parkDC: Chinatown/Penn Quarter project. However, for this concept to gain citywide acceptance, the pay by cell program needs to cater to the needs of all customers that park in the District. This paper analyzes the characteristics of customers that use the current pay by cell program; draws inferences about common traits of PBC users and their usage of the PBC system; and starts framing an understanding on the demographics of PBC non-users. Identifying the general demographics of non-users will enable DDOT to develop outreach strategies that encourage adoption of the PBC program by all parkers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".