Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Autoři:
Tae San Kim aff001; Won Kyung Lee aff001; So Young Sohn aff001
Působiště autorů:
Department of Industrial Engineering, Yonsei University, Shinchon-dong, Seoul, Republic of Korea
aff001
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0220782
Souhrn
Solving the supply–demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made to account for both features simultaneously. Therefore, we propose a prediction framework based on graph convolutional networks. Our framework reflects not only spatial dependencies among stations, but also various temporal patterns over different periods. Additionally, we consider the influence of global variables, such as weather and weekday/weekend to reflect non-station-level changes. We compare our framework to other baseline models using the data from Seoul’s bike-sharing system. Results show that our approach has better performance than existing prediction models.
Klíčová slova:
Biology and life sciences – Physical sciences – Engineering and technology – Research and analysis methods – Neuroscience – Computer and information sciences – Mathematics – Statistics – Mathematical and statistical techniques – Statistical methods – Transportation – Earth sciences – Atmospheric science – Structural engineering – Built structures – Neural networks – Artificial intelligence – Machine learning – Deep learning – Management engineering – Decision analysis – Decision trees – Decision tree learning – Meteorology – Rain
Zdroje
1. Lin L., He Z., & Peeta S. (2018). Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach. Transportation Research Part C: Emerging Technologies, 97, 258–276.
2. Singhvi, D., Singhvi, S., Frazier, P. I., Henderson, S. G., O'Mahony, E., Shmoys, D. B., & Woodard, D. B. (2015, April). Predicting Bike Usage for New York City's Bike Sharing System. In AAAI Workshop: Computational Sustainability.
3. Hsu Y. T., Kang L., & Wu Y. H. (2016). User Behavior of Bikesharing Systems Under Demand–Supply Imbalance. Transportation Research Record: Journal of the Transportation Research Board, (2587), 117–124.
4. Beecham R., & Wood J. (2014). Characterising group-cycling journeys using interactive graphics. Transportation Research Part C: Emerging Technologies, 47, 194–206.
5. Kaltenbrunner A., Meza R., Grivolla J., Codina J., & Banchs R. (2010). Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing, 6(4), 455–466.
6. Zhang, J., Zheng, Y., Qi, D., Li, R., & Yi, X. (2016). DNN-based prediction model for spatio-temporal data. In Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM 2016), page 92.
7. Reiss S., & Bogenberger K. (2016). Validation of a Relocation Strategy for Munich's Bike Sharing System. Transportation Research Procedia, 19, 341–349.
8. Zhou X. (2015). Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PloS one, 10(10), e0137922. doi: 10.1371/journal.pone.0137922 26445357
9. Zhang, J., Zheng, Y., & Qi, D. (2017). Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI 2017), pages 1655–1661.
10. Defferrard M., Bresson X., & Vandergheynst P. (2016). Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems (pp. 3844–3852).
11. Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
12. Frade I., & Ribeiro A. (2014). Bicycle sharing systems demand. Procedia-Social and Behavioral Sciences, 111, 518–527.
13. Thomas, T., Jaarsma, C. F., & Tutert, S. I. A. (2009). Temporal variations of bicycle demand in the Netherlands: The influence of weather on cycling. In 88th Transportation Research Board annual meeting (TRB 2009).
14. Regue R., & Recker W. (2014). Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem. Transportation Research Part E: Logistics and Transportation Review, 72, 192–209.
15. Froehlich, J., Neumann, J., & Oliver, N. (2009). Sensing and predicting the pulse of the city through shared bicycling. In International Joint Conference on Artificial Intelligence, (IJCAI 2009, July) (Vol. 9, pp. 1420–1426).
16. Dabiri S., & Heaslip K. (2018). Inferring transportation modes from GPS trajectories using a convolutional neural network. Transportation Research Part C: Emerging Technologies, 86, 360–371.
17. Krizhevsky A., Sutskever I., & Hinton G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).
18. Zhang, J., Pan, X., Li, M., & Philip, S. Y. (2016). Bicycle-sharing system analysis and trip prediction. In Mobile Data Management (MDM), 2016 17th IEEE International Conference on (IEEE 2016), pages 174–179.
19. Guo Y., Zhou J., Wu Y., & Li Z. (2017). Identifying the factors affecting bike-sharing usage and degree of satisfaction in Ningbo, China. PloS one, 12(9), e0185100. doi: 10.1371/journal.pone.0185100 28934321
20. Caggiani L., Camporeale R., Ottomanelli M., & Szeto W. Y. (2018). A modeling framework for the dynamic management of free-floating bike-sharing systems. Transportation Research Part C: Emerging Technologies, 87, 159–182.
21. Nankervis M. (1999). The effect of weather and climate on bicycle commuting. Transportation Research Part A: Policy and Practice, 33(6), 417–431.
22. Zeng M., Yu T., Wang X., Su V., Nguyen L. T., & Mengshoel O. J. (2016). Improving Demand Prediction in Bike Sharing System by Learning Global Features. Machine Learning for Large Scale Transportation Systems (LSTS).
23. Zhou M., Wang D., Li Q., Yue Y., Tu W., & Cao R. (2017). Impacts of weather on public transport ridership: Results from mining data from different sources. Transportation research part C: emerging technologies, 75, 17–29.
24. Motoaki Y., & Daziano R. A. (2015). A hybrid-choice latent-class model for the analysis of the effects of weather on cycling demand. Transportation Research Part A: Policy and Practice, 75, 217–230.
25. Zhang Y., Thomas T., Brussel M. J. G., & van Maarseveen M. F. A. M. (2016). Expanding bicycle-sharing systems: lessons learnt from an analysis of usage. PLoS one, 11(12), e0168604. doi: 10.1371/journal.pone.0168604 27977794
26. Box G. E., & Pierce D. A. (1970). Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. Journal of the American statistical Association, 65(332), 1509–1526.
27. Li, Y., Zheng, Y., Zhang, H., & Chen, L. (2015, November). Traffic prediction in a bike-sharing system. In Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems (p. 33). ACM.
28. Chen, L., Zhang, D., Wang, L., Yang, D., Ma, X., Li, S., et al. (2016, September). Dynamic cluster-based over-demand prediction in bike sharing systems. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 841–852). ACM.
29. Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM Sigkdd International Conference On Knowledge Discovery and Data Mining (pp. 785–794). ACM.
30. Williams R. J., & Zipser D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1(2), 270–280.
31. Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010). Recurrent neural network based language model. In Eleventh Annual Conference of the International Speech Communication Association.
32. Hochreiter S., & Schmidhuber J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. 9377276
33. Ridgeway G. (2012). Generalized boosted models: A guide to the gbm package. R package vignette.
34. Wu Y., Tan H., Qin L., Ran B., & Jiang Z. (2018). A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166–180.
35. Taieb S. B., Bontempi G., Atiya A. F., & Sorjamaa A. (2012). A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Systems with Applications, 39(8), 7067–7083.
Článok vyšiel v časopise
PLOS One
2019 Číslo 9
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Je Fuchsova endotelová dystrofie rohovky neurodegenerativní onemocnění?
- Fixní kombinace paracetamol/kodein nabízí synergické analgetické účinky
Najčítanejšie v tomto čísle
- Graviola (Annona muricata) attenuates behavioural alterations and testicular oxidative stress induced by streptozotocin in diabetic rats
- CH(II), a cerebroprotein hydrolysate, exhibits potential neuro-protective effect on Alzheimer’s disease
- Comparison between Aptima Assays (Hologic) and the Allplex STI Essential Assay (Seegene) for the diagnosis of Sexually transmitted infections
- Assessment of glucose-6-phosphate dehydrogenase activity using CareStart G6PD rapid diagnostic test and associated genetic variants in Plasmodium vivax malaria endemic setting in Mauritania