A Mobile-Side-Dominant Method for Querying Present and Future Velocity on Urban Roads

HAN Jingyu; WANG Yanzhi; CHEN Jin; YAN Xinxin; ZHANG Yiting

doi:10.11999/JEIT240102

Volume 46 Issue 9

Sep. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(9): 3722-3730

HAN Jingyu, WANG Yanzhi, CHEN Jin, YAN Xinxin, ZHANG Yiting. A Mobile-Side-Dominant Method for Querying Present and Future Velocity on Urban Roads[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3722-3730. doi: 10.11999/JEIT240102

Citation:

HAN Jingyu, WANG Yanzhi, CHEN Jin, YAN Xinxin, ZHANG Yiting. A Mobile-Side-Dominant Method for Querying Present and Future Velocity on Urban Roads[J]. Journal of Electronics & Information Technology, 2024, 46(9): 3722-3730. doi: 10.11999/JEIT240102

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A Mobile-Side-Dominant Method for Querying Present and Future Velocity on Urban Roads

doi: 10.11999/JEIT240102

School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China

Funds: Jiangsu Provincial Key Research & Development Plan (BE2022065-5)

Received Date: 2024-02-26
Rev Recd Date: 2024-06-15

Available Online: 2024-06-24

Publish Date: 2024-09-26

Abstract

Abstract

Querying present and future traffic velocities of road segments is a routine task in urban intelligence transportation management, and a Vehicle-equipped-Edge Dominant (VED) method is proposed to answer the querying of present and future velocity of urban road segments. The collected data is exchanged with the other mobile sides by every vehicle-equipped mobile side when the mobile side’s speed falls below a given threshold, and the light-weighted present and history velocity indexes are constructed locally to support the querying of present velocity. To train as few models as possible to predict future velocities, a road network is proposed to be partitioned into a set of road-segment clusters based on the segments’ topological morphism and the spatio-temporal space is proposed to be partitioned into a set of model-equivalence classes according to the periodic time windows and road-segment clusters. The similar traffic patterns are exhibited by the road segments in the same model-equivalence class within the given time window. For every model-equivalence class, the federated learning is performed between the mobile sides and the data center to train the Long Short-Term Memories (LSTMs) which are stored at the mobile sides to answer the querying of future velocities of nearby areas. Data is indexed by every mobile side and queries are answered locally, thus the query response latency and possible communication congestion can be avoided. Further, data is stored at the mobile sides, rather than at one data center, so as to prevent the privacy leakage due to security attacks.
- Intelligence transportation,
- Query,
- Prediction,
- Index,
- Federated learning

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References(21)

References

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