SMART AND SUSTAINABLE PARKING SYSTEMS IN ADVANCED BUSINESS ECONOMIES: LESSONS FOR URBAN TRANSPORT PLANNING IN NIGERIA
Bibliographic record
Abstract
Smart parking systems, especially automated parking systems (APS), have become quite popular in developed nations as a solution to the challenges posed by urbanization, limited space, and the need for environmental sustainability. These systems bring a host of benefits, such as better space utilization, lower construction and operational costs, improved safety, and positive environmental impacts. This paper takes a closer look at notable examples of APS in advanced economies like the United States, Germany, and Canada, highlighting their advantages compared to traditional parking systems. Using a qualitative approach and multiple case studies, the research delves into how smart and sustainable parking systems have been implemented and their outcomes in these leading business economies, while also drawing lessons for urban transport in Nigeria. Case studies from places like Manhattan, Munich, and Toronto showcase how APS can enhance urban living by alleviating traffic congestion, reclaiming green spaces, and providing safer, more efficient parking options. The paper discusses the significance of these innovations for Nigeria, Africa's most populous nation, especially in light of its rapidly growing urban population and increasing vehicle usage. It advocates for a policy shift in Nigeria towards the adoption of APS, suggesting that lessons from developed countries could help create a more efficient and sustainable parking infrastructure. By embracing these smart solutions, Nigeria can tackle its urbanization challenges while boosting public safety, environmental sustainability, and economic growth.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".