Adaptive Spatio-temporal Graph Learning for Bus Station Profiling
Article No.: 25, Pages 1 - 23
Abstract
Understanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal correlations, which are crucial in practical applications. To address these problems, we propose a novel bus station profiling framework to automatically infer the spatio-temporal correlations and capture the shifted spatial and long-term temporal correlations in the public transportation dataset. The proposed framework adopts and advances the graph learning structure through the following innovative ideas: (1) designing an adaptive graph learning mechanism to capture the interactions between spatio-temporal correlations rather than relying on pre-defined graphs, (2) modeling shifted correlation in shifted spatial graphs to learn fine-grained spatio-temporal features, and (3) employing self-attention mechanism to learn the long-term temporal correlations preserved in public transportation data. We conduct extensive experiments on three real-world datasets and exploit the learned profiles of stations for the station passenger flow prediction task. Experimental results demonstrate that the proposed framework outperforms all baselines under different settings and can produce meaningful bus station profiles.
References
[1]
Michael Adjeisah, Xinzhong Zhu, Huiying Xu, and Tewodros Alemu Ayall. 2023. Towards data augmentation in graph neural network: An overview and evaluation. Computer Science Review 47, (2023), 100527.
[2]
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural Inf. Process. Syst. 33 (2020), 17804–17815.
[3]
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling.
[4]
Manish Chandra, Debasis Ganguly, Pabitra Mitra, Bithika Pal, and James Thomas. 2021. NIP-GCN: An augmented graph convolutional network with node interaction patterns. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2242–2246.
[5]
Cen Chen, Kenli Li, Sin G. Teo, Xiaofeng Zou, Keqin Li, and Zeng Zeng. 2020. Citywide traffic flow prediction based on multiple gated spatio-temporal convolutional neural networks. ACM Trans. Knowl. Discov. Data 14, 4 (2020).
[6]
Xin Chen, Mingliang Hou, Tao Tang, Achhardeep Kaur, and Feng Xia. 2021. Digital twin mobility profiling: A spatio-temporal graph learning approach. In Proceedings of the IEEE 23rd Int Conf on High Performance Computing & Communications and the 7th International Conference on Data Science & Systems. 1178–1187.
[7]
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the Properties of Neural Machine Translation: Encoder-Decoder Approaches.
[8]
Jeongwhan Choi, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. 2022. Graph neural controlled differential equations for traffic forecasting. In Proceedings of the 36h AAAI Conference on Artificial Intelligence, Vol. 36. 6367–6374.
[9]
Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan. 2022. Towards spatio-temporal aware traffic time series forecasting. In Proceedings of the IEEE 38th International Conference on Data Engineering (ICDE’22). 2900–2913.
[10]
Alan Cooper. 1999. The Inmates Are Running the Asylum. Springer, Wiesbaden, 17–17.
[11]
Sina Dabiri, Chang-Tien Lu, Kevin Heaslip, and Chandan K Reddy. 2019. Semi-supervised deep learning approach for transportation mode identification using GPS trajectory data. IEEE Trans. Knowl. Data Eng. 32, 5 (2019), 1010–1023.
[12]
Alberto Del Bimbo, Andrea Ferracani, Daniele Pezzatini, Federico D’Amato, and Martina Sereni. 2014. Livecities: Revealing the pulse of cities by location-based social networks venues and users analysis. In Proceedings of the 23rd International Conference on World Wide Web. 163–166.
[13]
Shen Fang, Qi Zhang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan. 2019. GSTNet: Global spatial-temporal network for traffic flow prediction. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19). 2286–2293.
[14]
Yu Gu and Anthony Chen. 2023. Modeling mode choice of customized bus services with loyalty subscription schemes in multi-modal transportation networks. Transp. Res. Part C: Emerg. Technol. 147 (2023), 104012.
[15]
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence, Vol. 33. 922–929.
[16]
James Douglas Hamilton. 2020. Time Series Analysis.
[17]
Mireille Hildebrandt. 2008. Defining Profiling: A New Type of Knowledge?17–45.
[18]
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.
[19]
Guangyu Huo, Yong Zhang, Boyue Wang, Junbin Gao, Yongli Hu, and Baocai Yin. 2023. Hierarchical spatio–temporal graph convolutional networks and transformer network for traffic flow forecasting. IEEE Trans. Intell. Transport. Syst. 24, 4 (2023), 3855–3867.
[20]
Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, and Toyotaro Suzumura. 2023. Spatio-temporal meta-graph learning for traffic forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 8078–8086.
[21]
Songyu Ke, Zheyi Pan, Tianfu He, Yuxuan Liang, Junbo Zhang, and Yu Zheng. 2023. AutoSTG+: An automatic framework to discover the optimal network for spatio-temporal graph prediction. Artif. Intell. 318 (2023), 103899.
[22]
Patrick Kidger, James Morrill, James Foster, and Terry Lyons. 2020. Neural controlled differential equations for irregular time series. Adv. Neural Inf. Process. Syst. 33 (2020), 6696–6707.
[23]
Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3nd International Conference on Learning Representations (ICLR).
[24]
Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR’17).
[25]
András Komáromy and Paras Mehta. 2018. LocXplore: A system for profiling urban regions. In Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 568–571.
[26]
Fuxian Li, Jie Feng, Huan Yan, Guangyin Jin, Fan Yang, Funing Sun, Depeng Jin, and Yong Li. 2023. Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Trans. Knowl. Discov. Data 17, 1 (2023), 1–21.
[27]
Mengzhang Li and Zhanxing Zhu. 2021. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. 4189–4196.
[28]
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In Proceedings of the 6th International Conference on Learning Representations (ICLR’18).
[29]
Tao Liu, Aimin Jiang, Jia Zhou, Min Li, and Hon Keung Kwan. 2023. GraphSAGE-based dynamic spatial-temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transport. Syst. 24, 10 (2023), 11210–11224.
[30]
Zhi Liu, Yang Chen, Feng Xia, Jixin Bian, Bing Zhu, Guojiang Shen, and Xiangjie Kong. 2022. TAP: Traffic accident profiling via multi-task spatio-temporal graph representation learning. ACM Trans. Knowl. Discov. Data (2022).
[31]
Henrik Madsen. 2007. Time Series Analysis. CRC Press.
[32]
Ratna Mandal, Prasenjit Karmakar, Soumyajit Chatterjee, Debaleen Das Spandan, Shouvit Pradhan, Sujoy Saha, Sandip Chakraborty, and Subrata Nandi. 2023. Exploiting multi-modal contextual sensing for city-bus’s stay location characterization: Towards sub-60 seconds accurate arrival time prediction. ACM Trans. IoT 4, 1 (2023), 1–24.
[33]
Zahraa Al Sahili and Mariette Awad. 2023. Spatio-Temporal Graph Neural Networks: A Survey.
[34]
Divya Saxena and Jiannong Cao. 2022. Multimodal spatio-temporal prediction with stochastic adversarial networks. ACM Trans. Intell. Syst. Technol. 13, 2 (2022).
[35]
Chao Shang, Jie Chen, and Jinbo Bi. 2021. Discrete graph structure learning for forecasting multiple time series. In International Conference on Learning Representations.
[36]
David I. Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, and Pierre Vandergheynst. 2013. The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Sign. Process. Mag. 30, 3 (2013), 83–98.
[37]
David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, and A. Kai Qin. 2022. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering 34, 4 (2022), 1544–1561.
[38]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NeurlPS’17). 6000–6010.
[39]
Dongjie Wang, Pengyang Wang, Yanjie Fu, Kunpeng Liu, Hui Xiong, and Charles E. Hughes. 2023. Reinforced imitative graph learning for mobile user profiling. IEEE Trans. Knowl. Data Eng. (2023), 1–13.
[40]
Pengyang Wang, Yanjie Fu, Hui Xiong, and Xiaolin Li. 2019. Adversarial substructured representation learning for mobile user profiling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 130–138.
[41]
Pengyang Wang, Kunpeng Liu, Lu Jiang, Xiaolin Li, and Yanjie Fu. 2020. Incremental mobile user profiling: Reinforcement learning with spatial knowledge graph for modeling event streams. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 853–861.
[42]
Senzhang Wang, Meiyue Zhang, Hao Miao, Zhaohui Peng, and Philip S. Yu. 2022. Multivariate correlation-aware spatio-temporal graph convolutional networks for multi-scale traffic prediction. ACM Trans. Intell. Syst. Technol. 13, 3 (2022).
[43]
Yizhuo Wang, Renhe Jiang, Hangchen Liu, Du Yin, and Xuan Song. 2023. Sequence-graph fusion neural network for user mobile app behavior prediction. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. 105–121.
[44]
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang. 2019. Graph WaveNet for deep spatial-temporal graph modeling. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI’19).
[45]
Feng Xia, Xin Chen, Shuo Yu, Mingliang Hou, Mujie Liu, and Linlin You. 2023. Coupled attention networks for multivariate time series anomaly detection. IEEE Trans. Emerg. Top. Comput. (2023), 1–14.
[46]
Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, and Huan Liu. 2021. Graph learning: A survey. IEEE Trans. Artif. Intell. 2, 2 (2021), 109–127.
[47]
Yuanbo Xu, Xiao Cai, En Wang, Wenbin Liu, Yongjian Yang, and Funing Yang. 2023. Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction. Inf. Sci. 621 (2023), 580–595.
[48]
Jiexia Ye, Juanjuan Zhao, Kejiang Ye, and Chengzhong Xu. 2022. How to build a graph-based deep learning architecture in traffic domain: A survey. IEEE Trans. Intell. Transport. Syst. 23, 5 (2022), 3904–3924.
[49]
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI’18). 3634–3640.
[50]
Zhili Zhou, Xiaohua Dong, Zhetao Li, Keping Yu, Chun Ding, and Yimin Yang. 2022. Spatio-temporal feature encoding for traffic accident detection in VANET environment. IEEE Trans. Intell. Transport. Syst. 23, 10 (2022), 19772–19781.
[51]
Nengjun Zhu, Jian Cao, Xinjiang Lu, Chuanren Liu, Hao Liu, Yanyan Li, Xiangfeng Luo, and Hui Xiong. 2022. Predicting a person’s next activity region with a dynamic region-relation-aware graph neural network. ACM Trans. Knowl. Discov. Data 16, 6 (2022).
Index Terms
- Adaptive Spatio-temporal Graph Learning for Bus Station Profiling
Recommendations
Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting
Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic ...
Adaptive Spatio-temporal Graph Neural Network for traffic forecasting
AbstractAccurate traffic forecasting is of vital importance for the management and decision in intelligent transportation systems. Indeed, it is a nontrivial endeavor to predict future traffic conditions due to the complexity of spatial relationships and ...
Spatio-temporal fusion graph convolutional network for traffic flow forecasting
AbstractIn most recent research, the traffic forecasting task is typically formulated as a spatio-temporal graph modeling problem. For spatial correlation, they typically learn the shared pattern (i.e., the most salient pattern) of traffic series and ...
Highlights- Node-specific GCN immensely enhances the capacity of the GCN.
- A spatio-temporal fusion module is proposed.
- A transformer-based global temporal correlation learning module is proposed.
- The results prove that our method is ...
Comments
Information & Contributors
Information
Published In
September 2024
280 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 04 October 2024
Online AM: 07 December 2023
Accepted: 21 November 2023
Revised: 28 October 2023
Received: 22 May 2023
Published in TSAS Volume 10, Issue 3
Check for updates
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 272Total Downloads
- Downloads (Last 12 months)186
- Downloads (Last 6 weeks)19
Reflects downloads up to 03 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in