Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
8 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Using construction waste hauling trucks' GPS data to classify earthwork-related locations: A Chengdu case study (2402.14698v3)

Published 22 Feb 2024 in cs.LG and cs.AI

Abstract: Earthwork-related locations (ERLs), such as construction sites, earth dumping ground, and concrete mixing stations, are major sources of urban dust pollution (particulate matters). The effective management of ERLs is crucial and requires timely and efficient tracking of these locations throughout the city. This work aims to identify and classify urban ERLs using GPS trajectory data of over 16,000 construction waste hauling trucks (CWHTs), as well as 58 urban features encompassing geographic, land cover, POI and transport dimensions. We compare several machine learning models and examine the impact of various spatial-temporal features on classification performance using real-world data in Chengdu, China. The results demonstrate that 77.8% classification accuracy can be achieved with a limited number of features. This classification framework was implemented in the Alpha MAPS system in Chengdu, which has successfully identified 724 construction cites/earth dumping ground, 48 concrete mixing stations, and 80 truck parking locations in the city during December 2023, which has enabled local authority to effectively manage urban dust pollution at low personnel costs.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. “Mitigating construction dust pollution: State of the art and the way forward,” Journal of Cleaner Production, vol. 112, pp. 1658–1666, 2016.
  2. Ministry of Ecology and Environment of the People’s Republic of China, “2020 China ecological status environmental bulletin,” [Online]. Available: https://www.mee.gov.cn/hjzl/sthjzk/zghjzkgb/
  3. D. Cheriyan and J.-h. Choi, “A review of research on particulate matter pollution in the construction industry,” Journal of Cleaner Production, vol. 254, p. 120077, 2020.
  4. “An lca-based environmental impact assessment model for construction processes,” Building and Environment, vol. 45, no. 3, pp. 766–775, 2010.
  5. “Research on digital intelligent supervision and carbon reduction of construction waste trucks: A case study of chengdu city,” Dec. 2023. [Online]. Available: https://www.researchgate.net/publication/376354707
  6. “An experimental study on airborne particles dispersion in a residential room heated by radiator and floor heating systems,” Journal of Building Engineering, vol. 32, p. 101677, 2020.
  7. “An empirical analysis of environmental pollutants on building construction sites for determining the real-time monitoring indices,” Building and Environment, vol. 170, p. 106636, 2020.
  8. “Systematic evaluation framework and empirical study of the impacts of building construction dust on the surrounding environment,” Journal of Cleaner Production, vol. 275, p. 122767, 2020.
  9. “Occupational health risk assessment based on dust exposure during earthwork construction,” Journal of Building Engineering, vol. 44, p. 103186, 2021.
  10. “Aerosol particle and trace gas emissions from earthworks, road construction, and asphalt paving in germany: Emission factors and influence on local air quality,” Atmospheric Environment, vol. 122, pp. 662–671, 2015.
  11. S. Gautam and A. K. Patra, “Dispersion of particulate matter generated at higher depths in opencast mines,” Environmental Technology & Innovation, vol. 3, pp. 11–27, 2015.
  12. S. Ahmed and I. Arocho, “Emission of particulate matters during construction: A comparative study on a cross laminated timber (clt) and a steel building construction project,” Journal of Building Engineering, vol. 22, pp. 281–294, 2019.
  13. “Mixed-integer and conditional trajectory planning for an autonomous mining truck in loading/dumping scenarios: A global optimization approach,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1512–1522, 2022.
  14. “Optimization of earthwork allocation path as vehicle route problem based on genetic algorithm,” in E3S Web of Conferences, vol. 165. EDP Sciences, 2020, p. 04057.
  15. K. Han and C. Chen, “Method for deducing pollution point location based on slag transport vehicle track point,” Patent CN115 964 545B, Apr. 2023. [Online]. Available: https://patents.google.com/patent/CN115964545B/en
  16. “A method for identifying earthwork points and transportation networks based on gps trajectories of slag trucks,” Patent CN117 131 149B, Jan. 2024. [Online]. Available: https://www.patentguru.com/cn/CN117131149B/en
  17. V. Mayer-Schönberger and K. Cukier, Big data: A revolution that will transform how we live, work, and think. Houghton Mifflin Harcourt, 2013.
  18. “Big data: the management revolution,” Harvard Business Review, vol. 90, no. 10, pp. 60–68, 2012.
  19. W. Lu, “Big data analytics to identify illegal construction waste dumping: A hong kong study,” Resources, Conservation and Recycling, vol. 141, pp. 264–272, 2019.
  20. “Identification of working trucks and critical path nodes for construction waste transportation based on electric waybills: A case study of Shenzhen, China,” Journal of Advanced Transportation, vol. 2022, 2022.
  21. “Precision cityshield against hazardous chemicals threats via location mining and self-supervised learning,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 3072–3080.
  22. Machine learning: algorithms and applications. CRC Press, 2016.
  23. Data mining: concepts and techniques. Morgan Kaufmann, 2022.
  24. I. H. Witten and E. Frank, “Data mining: practical machine learning tools and techniques with java implementations,” ACM Sigmod Record, vol. 31, no. 1, pp. 76–77, 2002.
  25. “A unified approach to interpreting model predictions,” Advances in neural information processing systems, vol. 30, 2017.
  26. “Consistent individualized feature attribution for tree ensembles,” arXiv preprint arXiv:1802.03888, 2018.
  27. “A value for n-person games,” 1953.
  28. “Classification of urban functional areas from remote sensing images and time-series user behavior data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 1207–1221, 2020.
  29. “Classification of urban area using multispectral indices for urban planning,” Remote Sensing, vol. 12, no. 15, p. 2503, 2020.
  30. “Sinolc-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data,” Earth System Science Data Discussions, vol. 2023, pp. 1–38, 2023.
  31. “Identification and prediction of mixed-use functional areas supported by poi data in Jinan city of China,” Scientific Reports, vol. 13, no. 1, p. 2913, 2023.
  32. “Urban functional zone classification based on poi data and machine learning,” Sustainability, vol. 15, no. 5, p. 4631, 2023.
  33. “Aircraft taxi time prediction: Feature importance and their implications,” Transportation Research Part C: Emerging Technologies, vol. 124, p. 102892, 2021.
  34. “Lightgbm: A highly efficient gradient boosting decision tree,” Advances in neural information processing systems, vol. 30, 2017.
  35. “Influence of hyperparameters on random forest accuracy,” in Multiple Classifier Systems: 8th International Workshop, MCS 2009, Reykjavik, Iceland, June 10-12, 2009. Proceedings 8. Springer, 2009, pp. 171–180.
  36. “Studies on the temporal and spatial variations of urban expansion in Chengdu, western China, from 1978 to 2010,” Sustainable Cities and Society, vol. 17, pp. 141–150, 2015.
  37. Sichuan Guolan Zhongtian Environmental Technology Group Co Ltd, “Alpha-maps intelligent atmospheric monitoring and control system,” [Online]. Available: https://marketplace.huaweicloud.com/contents/a6cc6b5a-6f62-4509-867e-b7e4395b3ca3#productid=OFFI858632267563057152
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets