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

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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. 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. 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

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 and 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

[1]	PELANIS M, ŠALTENIS S, and JENSEN C S. Indexing the past, present, and anticipated future positions of moving objects[J]. ACM Transactions on Database Systems, 2006, 31(1): 255–298. doi: 10.1145/1132863.1132870.
[2]	PFOSER D, BRAKATSOULAS S, BROSCH P, et al. Dynamic travel time provision for road networks[C]. The 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Irvine, USA, 2008: 68. doi: 10.1145/1463434.1463513.
[3]	CHANG Jundong. Spatial-temporal based traffic speed imputation for GPS probe vehicles[C]. The Fifth International Conference on Network, Communication and Computing, Kyoto, Japan, 2016: 326–330. doi: 10.1145/3033288.3033339.
[4]	CHEN Rongsheng and LEVIN M W. Traffic state estimation based on Kalman filter technique using connected vehicle V2V basic safety messages[C]. 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand, 2019: 4380–4385. doi: 10.1109/ITSC.2019.8917343.
[5]	崔艳玲, 金蓓弘, 张扶桑. 基于数据融合的高速公路交通状况检测[J]. 计算机学报, 2017, 40(8): 1798–1812. doi: 10.11897/SP.J.1016.2017.01798. CUI Yanling, JIN Beihong, and ZHANG Fusang. Highway traffic condition detection with data fusion[J]. Chinese Journal of Computers, 2017, 40(8): 1798–1812. doi: 10.11897/SP.J.1016.2017.01798.
[6]	BEI Pan, DEMIRYUREK U, and SHAHABI C. Utilizing real-world transportation data for accurate traffic prediction[C]. IEEE 12th International Conference on Data Mining, Brussels, Belgium, 2012: 595–604. doi: 10.1109/ICDM.2012.52.
[7]	HE Zhixiang, CHOW C Y, and ZHANG Jiadong. STCNN: A spatio-temporal convolutional neural network for long-term traffic prediction[C]. The 20th IEEE International Conference on Mobile Data Management, Hong Kong, China, 2019: 226–233. doi: 10.1109/MDM.2019.00-53.
[8]	TANG Keshuang, CHEN Siqu, CAO Yumin, et al. Short-term travel speed prediction for urban expressways: Hybrid convolutional neural network models[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(3): 1829–1840. doi: 10.1109/TITS.2020.3027628.
[9]	TONG Guan, PENG Jiaheng, and LIANG Jun. Spatial-temporal graph multi-gate mixture-of-expert model for traffic prediction[C]. IEEE 26th International Conference on Intelligent Transportation Systems, Bilbao, Spain, 2023: 36–41. doi: 10.1109/ITSC57777.2023.10422031.
[10]	LI Maosen, CHEN Siheng, SHEN Yanning, et al. Online multi-agent forecasting with interpretable collaborative graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(4): 4768–4782. doi: 10.1109/tnnls.2022.3152251.
[11]	岳猛, 王怀远, 吴志军, 等. 云计算中DDoS攻防技术研究综述[J]. 计算机学报, 2020, 43(12): 2315–2336. doi: 10.11897/SP.J.1016.2020.02315. YUE Meng, WANG Huaiyuan, WU Zhijun, et al. A survey of DDoS attack and defense technologies in cloud computing[J]. Chinese Journal of Computers, 2020, 43(12): 2315–2336. doi: 10.11897/SP.J.1016.2020.02315.
[12]	QI Bozhao, KANG Lei, and BANERJEE S. A vehicle-based edge computing platform for transit and human mobility analytics[C]. The Second ACM/IEEE Symposium on Edge Computing, San Jose, USA, 2017: 1. doi: 10.1145/3132211.3134446.
[13]	吴洪越, 陈志伟, 石博文, 等. 一种面向边缘计算环境的去中心化服务请求分发方法[J]. 计算机学报, 2023, 46(5): 987–1002. doi: 10.11897/SP.J.1016.2023.00987. WU Hongyue, CHEN Zhiwei, SHI Bowen, et al. Decentralized service request dispatching for edge computing systems[J]. Chinese Journal of Computers, 2023, 46(5): 987–1002. doi: 10.11897/SP.J.1016.2023.00987.
[14]	张晓东, 张朝昆, 赵继军. 边缘智能研究进展[J]. 计算机研究与发展, 2023, 60(12): 2749–2769. doi: 10.7544/issn1000-1239.202220192. ZHANG Xiaodong, ZHANG Chaokun, and ZHAO Jijun. State-of-the-art survey on edge intelligence[J]. Journal of Computer Research and Development, 2023, 60(12): 2749–2769. doi: 10.7544/issn1000-1239.202220192.
[15]	WANG Tian, LUO Hao, ZHENG Xi, et al. Crowdsourcing mechanism for trust evaluation in CPCS based on intelligent mobile edge computing[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10(6): 62. doi: 10.1145/3324926.
[16]	FELDMAN M, LAI K, STOICA I, et al. May. Robust incentive techniques for peer-to-peer networks[C]. The 5th ACM Conference on Electronic Commerce, New York, USA, 2004: 102–111. doi: 10.1145/988772.988788.
[17]	陈山枝, 葛雨明, 时岩. 蜂窝车联网(C-V2X)技术发展、应用及展望[J]. 电信科学, 2022, 38(1): 1–12. doi: 10.11959/j.issn.1000-0801.2022007. CHEN Shanzhi, GE Yuming, and SHI Yan. Technology development, application and prospect of cellular vehicle-to-everything (C-V2X)[J]. Telecommunications Science, 2022, 38(1): 1–12. doi: 10.11959/j.issn.1000-0801.2022007.
[18]	HOCHREITER S and SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735–1780. doi: 10.1162/neco.1997.9.8.1735.
[19]	RAMSAK F, MARKL V, FENK R, et al. Integrating the UB-tree into a database system kernel[C]. The 26th International Conference on Very Large Data Bases, Cairo, Egypt, 2000: 263–272.
[20]	NIELSEN F. On a generalization of the Jensen–Shannon divergence and the Jensen–Shannon centroid[J]. Entropy, 2020, 22(2): 221. doi: 10.3390/e22020221.
[21]	MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-efficient learning of deep networks from decentralized data[C]. The 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, USA, 2017: 1273–1282.