An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix

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摘要 Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model’s high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications.

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	作者相关文章
	LI Wen-hao
	CHEN Yan-yan
	PAN Yu-yan
	ZHANG Yun-chao

关键词 ： traffic engineering, traffic prediction, spatio-temporal correlation, real time impedance matrix, STTN network

Abstract：Effective traffic management and congestion reduction heavily rely on accurate traffic flow prediction. Existing prediction methods, such as Markov, ARIMA, STANN, GLSTM, and DCRNN models, often face challenges because they rely on fixed spatial relationships, leading to limited long-term prediction accuracy. To address these shortcomings, this study proposes the Impedance-Spatio-Temporal Topological Network (Impedance-STTN) prediction model. The Impedance-STTN model integrates K-medoids clustering for data analysis, generating a real-time impedance matrix from impedance functions, traffic big data, and real-time flow data. This approach captures dynamic node relationships within the spatio-temporal network, enhancing prediction accuracy. Experimental results demonstrate the superior predictive performance of the Impedance-STTN model, achieving accuracies of 94.79%, 93.78%, and 93.11% in 5 min, 15 min, and 30 min predictions, respectively. These results outperform existing models, especially in long-term predictions. The findings underscore the model’s high accuracy and effectiveness across varying prediction durations, marking a significant advancement in traffic flow prediction. This suggests promising avenues for future research and practical applications.

Key words： traffic engineering traffic prediction spatio-temporal correlation real time impedance matrix STTN network

收稿日期: 2023-06-18

通讯作者: LI Wen-hao, CHEN Yan-yan

引用本文:

LI Wen-hao, CHEN Yan-yan, PAN Yu-yan, ZHANG Yun-chao. An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix[J]. Journal of Highway and Transportation Research and Development, 2024, 18(2): 67-75.
LI Wen-hao, CHEN Yan-yan, PAN Yu-yan, ZHANG Yun-chao. An Improved Spatio-Temporal Network Traffic Flow Prediction Method Based on Impedance Matrix. Journal of Highway and Transportation Research and Development, 2024, 18(2): 67-75.

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http://journal10.magtechjournal.com/Jwk_gljtkj_en/CN/ 或 http://journal10.magtechjournal.com/Jwk_gljtkj_en/CN/Y2024/V18/I2/67

[1] CHEN Y, LI S, PAN Y, et al. Urban Expressway Congestion Forewarning Based on Slope Change of Traffic Flow Fundamental Diagram[J]. Journal of Transportation Engineering, Part A:Systems, 2022, 148(6):04022030.
[2] EDES Y J S, MICHALOPOULOS P G, PLUM R A. Improved Estimation of Traffic Flow for Real-time Control[J]. Transportation Research Record, 1980, 95:28-39.
[3] AHMED M S, COOK A R. Analysis of Freeway Traffic Time-series Data by Using Box-Jenkins Techniques[J]. Transportation Research Record, 1979:https://api.semanticscholar.org/CorpusID:106553179.
[4] OKUTANI I. The Kalman Filtering Approaches in some Transportation and Traffic problems[C]//1987.[2023-09-10] . https://api.semanticscholar.org/CorpusID:126916011.
[5] CHAN K Y, DILLON T S. On-road Sensor Configuration Design for Traffic Flow Prediction Using Fuzzy Neural Networks and Taguchi Method[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 62(1):50-59.
[6] ROSSI R, GASTALDI M, GECCHELE G, et al. Fuzzy Logic-based Incident Detection System Using Loop Detectors Data[J]. Transportation Research Procedia, 2015, 10:266-275.
[7] YUAN F, CHEU R L. Incident Detection Using Support Vector Machines[J]. Transportation Research Part C:Emerging Technologies, 2003, 11(3-4):309-328.
[8] PAN Y A, GUO J, CHEN Y, et al. Incorporating Traffic Flow Model Into a Deep Learning Method for Traffic State Estimation:A Hybrid Stepwise Modeling Framework[J]. Journal of Advanced Transportation, 2022, 2022(1):5926663.
[9] MA X, TAO Z, WANG Y, et al. Long Short-term Memory Neural Network for Traffic Speed Prediction Using Remote Microwave Sensor Data[J]. Transportation Research Part C:Emerging Technologies, 2015, 54:187-197.
[10] KE J, ZHENG H, YANG H, et al. Short-term Forecasting of Passenger Demand Under On-Demand Ride Services:A Spatio-temporal Deep Learning Approach[J]. Transportation Research Part C:Emerging technologies, 2017, 85:591-608.
[11] MA X, ZHONG H, LI Y, et al. Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 22(8):4813-4824.
[12] CAI K, SHEN Z, LUO X, et al. Temporal Attention Aware Dual-Graph Convolution Network for Air Traffic Flow Prediction[J]. Journal of Air Transport Management, 2023, 106:102301.
[13] WANG S, LV Y, PENG Y, et al. Metro Traffic Flow Prediction via Knowledge Graph and Spatiotemporal Graph Neural Network[J]. Journal of Advanced Transportation, 2022, 2022(1):2348375..
[14] SHAO B, SONG D, BIAN G, et al. A Hybrid Approach by CEEMDAN-Improved PSO-LSTM Model for Network Traffic Prediction[J]. Security and Communication Networks, 2022, 2022(1):4975288.
[15] CHEN L, SHI P, LI G, et al. Traffic Flow Prediction Using Multi-view Graph Convolution and Masked Attention Mechanism[J]. Computer Communications, 2022, 194:446-457.
[16] YANG G, YU H, XI H. A Spatio-Temporal Traffic Flow Prediction Method Based on Dynamic Graph Convolution Network[C]//2022 34th Chinese Control and Decision Conference (CCDC). IEEE, 2022:5364-5369.
[17] PAN Y, GUO J, CHEN Y. Calibration of Dynamic Volume-delay Functions:A Rolling horizon-based Parsimonious Modeling Perspective[J]. Transportation research record, 2022, 2676(2):606-620.
[18] SPIESS H. Conical Volume-delay Functions[J]. Transportation Science, 1990, 24(2):153-158.
[19] DAVIDSON K B. The Theoretical Basis of A Flow-travel Time Relationship for Use in Transportation Planning[J]. Australian Road Research, 1978, 8(1).
[20] AKÇELIK R. Travel Time Functions for Transport Planning Purposes:Davidson's Function, Its Time Dependent Form and Alternative Travel Time Function[J]. Australian Road Research, 1991, 21(3).
[21] BOSTAN A, SCHOSTÉ. A Simple And Fast Algorithm for Computing Exponentials of Power Series[J]. Information processing letters, 2009, 109(13):754-756.
[22] WU X, FANG J, LIU Z, et al. Multistep Traffic Speed Prediction From Spatial-temporal Dependencies Using Graph Neural Networks[J]. Journal of transportation engineering, Part A:Systems, 2021, 147(12):04021082.
[23] XU M, DAI W, LIU C, et al. Spatial-temporal Transformer Networks for Traffic Flow Forecasting[J]. arXiv preprint arXiv:2001.02908, 2020.