Cited By
View all- Tu ZJeffries SMorse JHemmerling T(2024)Comparison of time-series models for predicting physiological metrics under sedationJournal of Clinical Monitoring and Computing10.1007/s10877-024-01237-zOnline publication date: 29-Oct-2024
More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning
Authors: 
Zhipeng Ma, 
Zheyan Tu, Xinhai Chen, Yan Zhang, Deguo Xia, Guyue Zhou, Yilun Chen, 
Yu Zheng, Jiangtao GongAuthors Info & Claims
Zhipeng Ma, Zheyan Tu, Xinhai Chen, Yan Zhang, Deguo Xia, Guyue Zhou, Yilun Chen, Yu Zheng, Jiangtao GongAuthors Info & ClaimsPages 3064 - 3075
Abstract
Trajectory representation learning plays a pivotal role in supporting various downstream tasks, such as travel time estimation, trajectory classification and Top-k similar trajectory search. Traditional methods in order to filter the noise in GPS trajectories tend to focus on routing-based methods to simplify the trajectories. However, these approaches ignore the motion details contained in the GPS data, limiting the representation capability of trajectory representation learning. To fill this gap, we propose a novel representation learning framework that is Jointly G PS and Route Modeling based on self-supervised technology, namely JGRM. We consider GPS trajectory and route trajectory as the two modals of a single movement observation and fuse information through inter-modal information interaction. Specifically, we develop two encoders, each tailored to capture representations of GPS trajectories and route trajectories respectively. The representations from these two modalities are fed into a shared transformer for inter-modal information interaction. Eventually, we design three self-supervised tasks to train the model. We validate the effectiveness of the proposed method on two real-world datasets through extensive experiments. The experimental results show that JGRM significantly outperforms existing methods in both road segment representation and trajectory representation tasks. Our source code is available at Github https://github.com/mamazi0131/JGRM.
Supplemental Material
MP4 File
Supplemental video
- Download
- 11.89 MB
References
[1]
Laura Alessandretti. 2022. What human mobility data tell us about COVID-19 spread. Nature Reviews Physics 4, 1 (2022), 12--13.
[2]
Jie Bao, Tianfu He, Sijie Ruan, Yanhua Li, and Yu Zheng. 2017. Planning bike lanes based on sharing-bikes' trajectories. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining. 1377--1386.
[3]
Geoff Boeing. 2017. OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems 65 (2017), 126--139.
[4]
Yanchuan Chang, Jianzhong Qi, Yuxuan Liang, and Egemen Tanin. 2023. Contrastive Trajectory Similarity Learning with Dual-Feature Attention. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 2933--2945.
[5]
Fei-Long Chen, Du-Zhen Zhang, Ming-Lun Han, Xiu-Yi Chen, Jing Shi, Shuang Xu, and Bo Xu. 2023. Vlp: A survey on vision-language pre-training. Machine Intelligence Research 20, 1 (2023), 38--56.
[6]
Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long, Yiding Liu, Arun Kumar Chandran, and Richard Ellison. 2021. Robust road network representation learning: When traffic patterns meet traveling semantics. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 211--220.
[7]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).
[8]
Ziquan Fang, Yuntao Du, Lu Chen, Yujia Hu, Yunjun Gao, and Gang Chen. 2021. E 2 dtc: An end to end deep trajectory clustering framework via self-training. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 696--707.
[9]
Ziquan Fang, Yuntao Du, Xinjun Zhu, Danlei Hu, Lu Chen, Yunjun Gao, and Christian S Jensen. 2022. Spatio-temporal trajectory similarity learning in road networks. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining. 347--356.
[10]
Jie Feng, Zeyu Yang, Fengli Xu, Haisu Yu, Mudan Wang, and Yong Li. 2020. Learning to simulate human mobility. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. 3426--3433.
[11]
Tao Feng, Huan Yan, Huandong Wang, Wenzhen Huang, Yuyang Han, Hongsen Liao, Jinghua Hao, and Yong Li. 2023. ILRoute: A Graph-based Imitation Learning Method to Unveil Riders' Routing Strategies in Food Delivery Service. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 4024--4034.
[12]
Tao-Yang Fu and Wang-Chien Lee. 2020. Trembr: Exploring Road Networks for Trajectory Representation Learning. ACM Trans. Intell. Syst. Technol. 11, 1 (feb 2020). https://doi.org/10.1145/3361741
[13]
Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. arXiv:1607.00653 [cs.SI]
[14]
Tianfu He, Jie Bao, Ruiyuan Li, Sijie Ruan, Yanhua Li, Li Song, Hui He, and Yu Zheng. 2020. What is the human mobility in a new city: Transfer mobility knowledge across cities. In Proceedings of The Web Conference 2020. 1355--1365.
[15]
Zhenyu Huang, Guocheng Niu, Xiao Liu, Wenbiao Ding, Xinyan Xiao, Hua Wu, and Xi Peng. 2021. Learning with noisy correspondence for cross-modal matching. Advances in Neural Information Processing Systems 34 (2021), 29406--29419.
[16]
Jiahao Ji, Jingyuan Wang, Junjie Wu, Boyang Han, Junbo Zhang, and Yu Zheng. 2022. 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. 3072--3080.
[17]
Jiawei Jiang, Dayan Pan, Houxing Ren, Xiaohan Jiang, Chao Li, and Jingyuan Wang. 2023. Self-supervised trajectory representation learning with temporal regularities and travel semantics. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 843--855.
[18]
Wonjae Kim, Bokyung Son, and Ildoo Kim. 2021. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning. PMLR, 5583--5594.
[19]
Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. arXiv:1611.07308 [stat.ML]
[20]
Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. 2021. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems 34 (2021), 9694--9705.
[21]
Xiucheng Li, Kaiqi Zhao, Gao Cong, Christian S Jensen, andWeiWei. 2018. Deep representation learning for trajectory similarity computation. In 2018 IEEE 34th international conference on data engineering (ICDE). IEEE, 617--628.
[22]
Yuxuan Liang, Kun Ouyang, Yiwei Wang, Xu Liu, Hongyang Chen, Junbo Zhang, Yu Zheng, and Roger Zimmermann. 2022. TrajFormer: Efficient Trajectory Classification with Transformers. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1229--1237.
[23]
Hongbin Liu, Hao Wu, Weiwei Sun, and Ickjai Lee. 2019. Spatio-temporal GRU for trajectory classification. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 1228--1233.
[24]
Wenjun Lyu, Haotian Wang, Yiwei Song, Yunhuai Liu, Tian He, and Desheng Zhang. 2023. A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics. In 32nd International Joint Conference on Artificial Intelligence, IJCAI 2023. International Joint Conferences on Artificial Intelligence, 6130--6137.
[25]
Zhenyu Mao, Ziyue Li, Dedong Li, Lei Bai, and Rui Zhao. 2022. Jointly contrastive representation learning on road network and trajectory. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 1501--1510.
[26]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781 [cs.CL]
[27]
Usue Mori, Alexander Mendiburu, Maite Álvarez, and Jose A Lozano. 2015. A review of travel time estimation and forecasting for advanced traveller information systems. Transportmetrica A: Transport Science 11, 2 (2015), 119--157.
[28]
Nikos Pelekis, Ioannis Kopanakis, Gerasimos Marketos, Irene Ntoutsi, Gennady Andrienko, and Yannis Theodoridis. 2007. Similarity search in trajectory databases. In 14th International Symposium on Temporal Representation and Reasoning (TIME'07). IEEE, 129--140.
[29]
Sijie Ruan, Cheng Long, Zhipeng Ma, Jie Bao, Tianfu He, Ruiyuan Li, Yiheng Chen, Shengnan Wu, and Yu Zheng. 2022. Service Time Prediction for Delivery Tasks via Spatial Meta-Learning. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 3829--3837.
[30]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017).
[31]
Dong Wang, Junbo Zhang, Wei Cao, Jian Li, and Yu Zheng. 2018. When will you arrive? estimating travel time based on deep neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.
[32]
Zheng Wang, Kun Fu, and Jieping Ye. 2018. Learning to estimate the travel time. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 858--866.
[33]
Hao Wu, Ziyang Chen, Weiwei Sun, Baihua Zheng, and Wei Wang. 2017. Modeling trajectories with recurrent neural networks. IJCAI.
[34]
Can Yang and Gyozo Gidofalvi. 2018. Fast map matching, an algorithm integrating hidden Markov model with precomputation. International Journal of Geographical Information Science 32, 3 (2018), 547--570.
[35]
Can Yang and Gyozo Gidofalvi. 2018. Fast map matching, an algorithm integrating hidden Markov model with precomputation. International Journal of Geographical Information Science 32, 3 (2018), 547--570. https://doi.org/10.1080/13658816.2017. 1400548 arXiv:https://doi.org/10.1080/13658816.2017.1400548
[36]
Peilun Yang, HanchenWang, Ying Zhang, Lu Qin,Wenjie Zhang, and Xuemin Lin. 2021. T3s: Effective representation learning for trajectory similarity computation. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2183--2188.
[37]
Sean Bin Yang, Chenjuan Guo, Jilin Hu, Jian Tang, and Bin Yang. 2021. Unsupervised path representation learning with curriculum negative sampling. arXiv preprint arXiv:2106.09373 (2021).
[38]
Sean Bin Yang, Jilin Hu, Chenjuan Guo, Bin Yang, and Christian S Jensen. 2023. Lightpath: Lightweight and scalable path representation learning. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2999--3010.
[39]
Di Yao, Gao Cong, Chao Zhang, and Jingping Bi. 2019. Computing trajectory similarity in linear time: A generic seed-guided neural metric learning approach. In 2019 IEEE 35th international conference on data engineering (ICDE). IEEE, 1358-- 1369.
[40]
Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi. 2017. Serm: A recurrent model for next location prediction in semantic trajectories. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. 2411--2414.
[41]
Di Yao, Chao Zhang, Zhihua Zhu, Jianhui Huang, and Jingping Bi. 2017. Trajectory clustering via deep representation learning. In 2017 international joint conference on neural networks (IJCNN). IEEE, 3880--3887.
[42]
Ziqiang Zhang, Sanyuan Chen, Long Zhou, Yu Wu, Shuo Ren, Shujie Liu, Zhuoyuan Yao, Xun Gong, Lirong Dai, Jinyu Li, et al. 2022. Speechlm: Enhanced speech pre-training with unpaired textual data. arXiv preprint arXiv:2209.15329 (2022).
[43]
Zheng Zhu, Huimin Ren, Sijie Ruan, Boyang Han, Jie Bao, Ruiyuan Li, Yanhua Li, and Yu Zheng. 2021. Icfinder: A ubiquitous approach to detecting illegal hazardous chemical facilities with truck trajectories. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems. 37--40.
Index Terms
- More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning
Recommendations
Jointly Contrastive Representation Learning on Road Network and Trajectory
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementRoad network and trajectory representation learning are essential for traffic systems since the learned representation can be directly used in various downstream tasks (e.g., traffic speed inference, travel time estimation). However, most existing ...
Explainable Trajectory Representation through Dictionary Learning
SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information SystemsTrajectory representation learning on a network enhances our understanding of vehicular traffic patterns and benefits numerous downstream applications. Existing approaches using classic machine learning or deep learning embed trajectories as dense ...
L2MM: Learning to Map Matching with Deep Models for Low-Quality GPS Trajectory Data
Map matching is a fundamental research topic with the objective of aligning GPS trajectories to paths on the road network. However, existing models fail to achieve satisfactory performance for low-quality (i.e., noisy, low-frequency, and non-uniform) ...
Comments
Information & Contributors
Information
Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
Copyright © 2024 ACM.
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].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
- Beijing Natural Science Foundation
- Baidu Inc.
Conference
WWW '24
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 197Total Downloads
- Downloads (Last 12 months)197
- Downloads (Last 6 weeks)23
Reflects downloads up to 13 Jan 2025
Other Metrics
Citations
Cited By
View all- Tu ZJeffries SMorse JHemmerling T(2024)Comparison of time-series models for predicting physiological metrics under sedationJournal of Clinical Monitoring and Computing10.1007/s10877-024-01237-zOnline publication date: 29-Oct-2024
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