The Impact of Transport Infrastructure Modernisations on Acoustic Climate on the Example of the City of Szczecin (Poland) Intersections Redevelopment Effects
Bibliographic record
Abstract
The source of most noise worldwide is mainly caused by machines and transportation means, including motor vehicles such as cars, buses, trains, aircrafts and so on. The excessive noise, called noise pollution, may harm the activity or balance of human or animal life. Noise pollution can cause annoyance, aggression and sleep disturbances. Chronic exposure to noise may cause noise-induced hearing loss, tinnitus and contribute to cardiovascular problems such as hypertension as well as increased incidence of coronary artery disease. Such may bring about deterioration in the wellbeing of people and increase the number of days of incapacity for work. This paper is an attempt to analyze the impact of transport infrastructure modernisations on the noise pollution in the city of Szczecin. The main objective of this paper was to compare the level of traffic noise in the areas surrounding streets: Powstancow Wielkopolskich, Mieszka I and Aleja Piastow Streets and crossroads of the streets: Taczaka-Lukasinskiego as well as Taczaka-Derdowskiego before and after the modernizations. The comparison of obtained results suggest that in some cases the modernization hasn’t influenced on noise levels. In some, it improved the acoustic situation but hasn’t reduced the noise to keep acceptable levels. The results emphasizethe thesis that some accepted methods of streets and crosswords modernization are sometimes ineffective in the fight against noise pollution. Conclusions: Modernization of intersections in Szczecin improved traffic flow but had a little impact on the noise levels. Modernisations that improve the traffic flow can cause even increment in noise pollution. It should be taken into consideration possible benefits of used methods of city traffic modernization related not only to traffic improvements but also to noise pollution reduction. We suggest computer aided stimulations and acoustic specialist advices prior to any restructures of city traffic. To minimize the noise pollution, comprehensive solutions are needed.
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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 | 0.001 | 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 | 0.001 | 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".