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MetaTP: Traffic Prediction with Unevenly-Distributed Road Sensing Data via Fast Adaptation

Authors:

Abhishek Gupta,

Lu SuAuthors Info & Claims

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 5, Issue 3

Article No.: 141, Pages 1 - 28

https://doi.org/10.1145/3478083

Published: 14 September 2021 Publication History

Abstract

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.

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Index Terms

MetaTP: Traffic Prediction with Unevenly-Distributed Road Sensing Data via Fast Adaptation
1. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools
2. Networks
  1. Network types
    1. Cyber-physical networks
      1. Sensor networks

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cover image Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Volume 5, Issue 3

Sept 2021

1443 pages

EISSN:2474-9567

DOI:10.1145/3486621

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Published: 14 September 2021

Published in IMWUT Volume 5, Issue 3

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Das DBhattacharjee SChakraborty SMitra BDas S(2024)Early Detection of Driving Maneuvers for Proactive Congestion Prevention2024 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom59722.2024.10494436(135-142)Online publication date: 11-Mar-2024
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Gao JPan YZhang XHan QHu Y(2024)Sharing instant delivery UAVs for crowdsensingComputers and Industrial Engineering10.1016/j.cie.2024.110100191:COnline publication date: 18-Jul-2024
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Wang QGuo BCheng LYu Z(2023)sUrbanProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108777:3(1-20)Online publication date: 27-Sep-2023
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Liu JYuan SDong YBiondi BNoh H(2023)TelecomTMProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35962627:2(1-24)Online publication date: 12-Jun-2023
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Chen JLiu TLi RFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)Region Profile Enhanced Urban Spatio-Temporal Prediction via Adaptive Meta-LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615027(224-233)Online publication date: 21-Oct-2023
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Wang QGuo B(2023)Environment-agnostic Effective Learning for Domain Generalization on IoT Time Series Data2023 International Conference on Artificial Intelligence of Things and Systems (AIoTSys)10.1109/AIoTSys58602.2023.00053(214-220)Online publication date: 19-Oct-2023
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