3,491
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Incorporating multimodal context information into traffic speed forecasting through graph deep learning

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 1909-1935 | Received 12 Jan 2023, Accepted 05 Jul 2023, Published online: 18 Jul 2023

References

  • Azad, A. and Wang, X., 2021. Land use change ontology and traffic prediction through recurrent neural networks: a case study in Calgary, Canada. ISPRS International Journal of Geo-Information, 10 (6), 358.
  • Belhadi, A., et al., 2020. A recurrent neural network for urban long-term traffic flow forecasting. Applied Intelligence, 50 (10), 3252–3265.
  • Buchin, M., Dodge, S., and Speckmann, B., 2012. Context-aware similarity of trajectories. In: International conference on geographic information science, Columbus, OH, USA. Berlin, Heidelberg: Springer, 43–56.
  • Cao, D., et al., 2020. Spectral temporal graph neural network for multivariate time-series forecasting. Advances in Neural Information Processing Systems, 33, 17766–17778.
  • Chen, J., et al., 2020. Gst-gcn: a geographic-semantic-temporal graph convolutional network for context-aware traffic flow prediction on graph sequences. In: 2020 IEEE international conference on systems, man, and cybernetics (SMC), Toronto, ON, Canada. IEEE, 1604–1609.
  • Cheng, A., et al., 2017. Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method. Physica A: Statistical Mechanics and Its Applications, 466, 422–434.
  • Demšar, U., et al., 2021. Establishing the integrated science of movement: bringing together concepts and methods from animal and human movement analysis. International Journal of Geographical Information Science, 35 (7), 1273–1308.
  • Diao, Z., et al., 2019. Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, 33 (1), 890–897.
  • Ermagun, A. and Levinson, D., 2018. Spatiotemporal traffic forecasting: review and proposed directions. Transport Reviews, 38 (6), 786–814.
  • Fu, R., Zhang, Z., and Li, L., 2016. Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st youth academic annual conference of Chinese Association of Automation (YAC), Wuhan, China. IEEE, 324–328.
  • Gao, J., et al., 2020. A survey on deep learning for multimodal data fusion. Neural Computation, 32 (5), 829–864.
  • Ge, L., et al., 2019a. Temporal graph convolutional networks for traffic speed prediction considering external factors. In: 2019 20th IEEE international conference on mobile data management (MDM), Hong Kong, China. IEEE, 234–242.
  • Ge, Y., et al., 2019b. Principles and methods of scaling geospatial earth science data. Earth-Science Reviews, 197, 102897.
  • Guo, D., et al., 2022. Deepssn: a deep convolutional neural network to assess spatial scene similarity. Transactions in GIS, 26 (4), 1914–1938.
  • Han, C. and Song, S., 2003. A review of some main models for traffic flow forecasting. In: Proceedings of the 2003 IEEE international conference on intelligent transportation systems, Shanghai, China. IEEE, 216–219.
  • Haraguchi, M., et al., 2022. Human mobility data and analysis for urban resilience: a systematic review. Environment and Planning B: Urban Analytics and City Science, 49 (5), 1507–1535.
  • He, K., et al., 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (9), 1904–1916.
  • Huang, Q. and Wong, D.W., 2015. Modeling and visualizing regular human mobility patterns with uncertainty: an example using twitter data. Annals of the Association of American Geographers, 105 (6), 1179–1197.
  • Janowicz, K., et al., 2020. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34 (4), 625–636.
  • Jiang, W. and Luo, J., 2022. Graph neural network for traffic forecasting: a survey. Expert Systems with Applications, 207, 117921.
  • Karatzoglou, A., Jablonski, A., and Beigl, M., 2018. A seq2seq learning approach for modeling semantic trajectories and predicting the next location. In: Proceedings of the 26th ACM SIGSPATIAL international conference on advances in geographic information systems, Seattle, WA, USA. Association for Computing Machinery, 528–531,
  • Kashyap, A.A., et al., 2022. Traffic flow prediction models–a review of deep learning techniques. Cogent Engineering, 9 (1), 2010510.
  • Koesdwiady, A., Soua, R., and Karray, F., 2016. Improving traffic flow prediction with weather information in connected cars: a deep learning approach. IEEE Transactions on Vehicular Technology, 65 (12), 9508–9517.
  • Kumar, N. and Raubal, M., 2021. Applications of deep learning in congestion detection, prediction and alleviation: a survey. Transportation Research Part C: Emerging Technologies, 133, 103432.
  • Kurth, M., et al., 2020. Lack of resilience in transportation networks: economic implications. Transportation Research Part D: Transport and Environment, 86, 102419.
  • Lahat, D., Adali, T., and Jutten, C., 2015. Multimodal data fusion: an overview of methods, challenges, and prospects. Proceedings of the IEEE, 103(9), 1449–1477.
  • Lana, I., et al., 2018. Road traffic forecasting: recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine, 10 (2), 93–109.
  • Lee, M. and Holme, P., 2015. Relating land use and human intra-city mobility. PLoS One, 10 (10), e0140152.
  • Li, M. and Zhu, Z., 2021. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI conference on artificial intelligence. 35 (5), 4189–4196.
  • Li, S., et al., 2016. Geospatial big data handling theory and methods: a review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133.
  • Li, Y. and Shahabi, C., 2018. A brief overview of machine learning methods for short-term traffic forecasting and future directions. Sigspatial Special, 10 (1), 3–9.
  • Li, Y., et al., 2017. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926.
  • Lin, L., et al., 2018. Road traffic speed prediction: a probabilistic model fusing multi-source data. IEEE Transactions on Knowledge and Data Engineering, 30 (7), 1310–1323.
  • Liu, J., et al., 2020. Urban big data fusion based on deep learning: an overview. Information Fusion, 53, 123–133.
  • Liu, K., et al., 2018. Learn to combine modalities in multimodal deep learning. arXiv preprint arXiv:1805.11730.
  • Liu, Y. and Wu, H., 2017. Prediction of road traffic congestion based on random forest. In: 2017 10th international symposium on computational intelligence and design (ISCID), Hangzhou, China. IEEE, 361–364.
  • Liu, Y., et al., 2017. Short-term traffic flow prediction with conv-LSTM. In: 2017 9th international conference on wireless communications and signal processing (WCSP), Nanjing, China. IEEE, 1–6.
  • Ramakrishnan, N. and Soni, T., 2018. Network traffic prediction using recurrent neural networks. In: 2018 17th IEEE international conference on machine learning and applications (ICMLA), Orlando, FL, USA. IEEE, 187–193.
  • Ren, Y., et al., 2020. A hybrid integrated deep learning model for the prediction of citywide spatio-temporal flow volumes. International Journal of Geographical Information Science, 34 (4), 802–823.
  • Ryu, S., Kim, D., and Kim, J., 2020. Weather-aware long-range traffic forecast using multi-module deep neural network. Applied Sciences, 10 (6), 1938.
  • Sattar, F., et al., 2016. Recent advances on context-awareness and data/information fusion in its. International Journal of Intelligent Transportation Systems Research, 14 (1), 1–19.
  • Sharif, M. and Alesheikh, A.A., 2017. Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience & Remote Sensing, 54 (3), 426–452.
  • Tedjopurnomo, D.A., et al., 2020. A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering, 34 (4), 1544–1561.
  • Tu, W., et al., 2017. Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns. International Journal of Geographical Information Science, 31 (12), 2331–2358.
  • Tu, W., et al., 2020a. Scale effect on fusing remote sensing and human sensing to portray urban functions. IEEE Geoscience and Remote Sensing Letters, 18 (1), 38–42.
  • Tu, W., et al., 2020b. Portraying the spatial dynamics of urban vibrancy using multisource urban big data. Computers, Environment and Urban Systems, 80, 101428.
  • Wang, H.-W., et al., 2020. Evaluation and prediction of transportation resilience under extreme weather events: a diffusion graph convolutional approach. Transportation Research Part C: Emerging Technologies, 115, 102619.
  • Wang, J., et al., 2021. Libcity: an open library for traffic prediction. In: Proceedings of the 29th international conference on advances in geographic information systems, Beijing, China. Association for Computing Machinery, 145–148.
  • Wang, S., et al., 2018. A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks. International Journal of Geographical Information Science, 32 (7), 1368–1390.
  • Wei, D. and Liu, H., 2013. An adaptive-margin support vector regression for short-term traffic flow forecast. Journal of Intelligent Transportation Systems, 17 (4), 317–327.
  • Wu, Y., et al., 2018. A hybrid deep learning based traffic flow prediction method and its understanding. Transportation Research Part C: Emerging Technologies, 90, 166–180.
  • Wu, Z., et al., 2020. Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, Virtual Event, CA, USA. Association for Computing Machinery, 753–763.
  • Yin, X., et al., 2022. Deep learning on traffic prediction: methods, analysis and future directions. IEEE Transactions on Intelligent Transportation Systems, 23 (6), 4927–4943.
  • Yin, Y. and Shang, P., 2016. Forecasting traffic time series with multivariate predicting method. Applied Mathematics and Computation, 291, 266–278.
  • Ying, Z., et al., 2018. Hierarchical graph representation learning with differentiable pooling. Advances in Neural Information Processing Systems, 31.
  • Yu, B., Yin, H., and Zhu, Z., 2017a. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875.
  • Yu, R., et al., 2017b. Deep learning: a generic approach for extreme condition traffic forecasting. In: N. Chawla and W. Wang, eds. Proceedings of the 2017 SIAM international conference on data mining. Society for Industrial and Applied Mathematics, 777–785.
  • Zhang, Y. and Raubal, M., 2022. Street-level traffic flow and context sensing analysis through semantic integration of multisource geospatial data. Transactions in GIS, 26 (8), 3330–3348.
  • Zhao, P., et al., 2020. Where to go next: a spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering, 34(5), 2512–2524.
  • Zhao, T., et al., 2022. Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction. Computers, Environment and Urban Systems, 94, 101776.