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LibCity: An Open Library for Traffic Prediction

Published: 04 November 2021 Publication History

Abstract

With the increase of traffic prediction models, there has become an urgent need to develop a standardized framework to implement and evaluate these methods. This paper presents LibCity, a unified, comprehensive, and extensible library for traffic prediction, which provides researchers with a credible experimental tool and a convenient development framework. In this library, we reproduce 42 traffic prediction models and collect 29 spatial-temporal datasets, which allows researchers to conduct comprehensive experiments in a convenient way. To accelerate the development of new models, we design unified model interfaces based on unified data formats, which effectively encapsulate the details of the implementation. To verify the effectiveness of our implementations, we also report the reproducibility comparison results of LibCity, and set up a performance leaderboard for the four kinds of traffic prediction tasks. Our library will contribute to the standardization and reproducibility in the field of traffic prediction. The open source link of LibCity is https://github.com/LibCity/Bigscity-LibCity.

References

[1]
Hao Chen, Ke Yang, Stefano Giovanni Rizzo, Giovanna Vantini, Phillip Taylor, Xiaosong Ma, and Sanjay Chawla. 2020. QarSUMO: A Parallel, Congestion-optimized Traffic Simulator. In SIGSPATIAL/GIS. ACM, 578--588.
[2]
Shengyi (Costa) Huang and Chris Healy. 2018. StreetTraffic: a library for traffic flow data collection and analysis. In ACM Southeast Regional Conference. ACM, 40:1--40:3.
[3]
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. 2018. Tune: A Research Platform for Distributed Model Selection and Training. arXiv preprint arXiv:1807.05118 (2018).
[4]
David Alexander Tedjopurnomo, Zhifeng Bao, Baihua Zheng, Farhana Choudhury, and AK Qin. 2020. A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge and Data Engineering (2020).
[5]
Cathy Wu, Aboudy Kreidieh, Kanaad Parvate, Eugene Vinitsky, and Alexandre M. Bayen. 2017. Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control. CoRR abs/1710.05465 (2017).
[6]
Xueyan Yin, Genze Wu, Jinze Wei, Yanming Shen, Heng Qi, and Baocai Yin. 2021. Deep Learning on Traffic Prediction: Methods, Analysis and Future Directions. IEEE Trans. Intell. Transp. Syst. (2021).

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  • (2025)UniTE: A Survey and Unified Pipeline for Pre-Training Spatiotemporal Trajectory EmbeddingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352399637:3(1475-1494)Online publication date: Mar-2025
  • (2025)Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348445437:1(291-305)Online publication date: Jan-2025
  • (2025)Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecastingNeural Networks10.1016/j.neunet.2024.106805181(106805)Online publication date: Jan-2025
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  1. LibCity: An Open Library for Traffic Prediction

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    cover image ACM Conferences
    SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information Systems
    November 2021
    700 pages
    ISBN:9781450386647
    DOI:10.1145/3474717
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 04 November 2021

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    Author Tags

    1. Reproducibility
    2. Spatial-temporal System
    3. Traffic Prediction

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    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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    View all
    • (2025)UniTE: A Survey and Unified Pipeline for Pre-Training Spatiotemporal Trajectory EmbeddingsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352399637:3(1475-1494)Online publication date: Mar-2025
    • (2025)Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity AnalysisIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.348445437:1(291-305)Online publication date: Jan-2025
    • (2025)Dynamic meta-graph convolutional recurrent network for heterogeneous spatiotemporal graph forecastingNeural Networks10.1016/j.neunet.2024.106805181(106805)Online publication date: Jan-2025
    • (2024)Network level spatial temporal traffic forecasting with Hierarchical-Attention-LSTMDigital Transportation and Safety10.48130/dts-0024-00213:4(233-245)Online publication date: 2024
    • (2024)FaKnow: A Unified Library for Fake News DetectionData Intelligence10.3724/2096-7004.di.2024.0026Online publication date: 26-Aug-2024
    • (2024)DPSTCN: Dynamic Pattern-Aware Spatio-Temporal Convolutional Networks for Traffic Flow ForecastingISPRS International Journal of Geo-Information10.3390/ijgi1401001014:1(10)Online publication date: 31-Dec-2024
    • (2024)Spatial–Temporal Fusion Gated Transformer Network (STFGTN) for Traffic Flow PredictionElectronics10.3390/electronics1308159413:8(1594)Online publication date: 22-Apr-2024
    • (2024)STFEformer: Spatial–Temporal Fusion Embedding Transformer for Traffic Flow PredictionApplied Sciences10.3390/app1410432514:10(4325)Online publication date: 20-May-2024
    • (2024)GMTP: Enhanced Travel Time Prediction with Graph Attention Network and BERT IntegrationAI10.3390/ai50401415:4(2926-2944)Online publication date: 13-Dec-2024
    • (2024)Adversarial Reconstruction of Trajectories: Privacy Risks and Attack Models in Trajectory EmbeddingProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691274(259-269)Online publication date: 29-Oct-2024
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