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    A Modeling Method for Complex Traffic Flow on Highways Based on the Fusion of Heterogeneous Data from Multiple Sensors

    Source: Journal of Transportation Engineering, Part A: Systems:;2024:;Volume ( 150 ):;issue: 006::page 04024021-1
    Author:
    Shaoweihua Liu
    ,
    Yunyan Tang
    ,
    Yiliu He
    ,
    Junyi Ren
    ,
    Yujie Zhang
    ,
    Xi Luo
    ,
    Hongyun Yang
    DOI: 10.1061/JTEPBS.TEENG-8207
    Publisher: ASCE
    Abstract: Improving the efficiency and safety of highway traffic relies heavily on accurately modeling the complex dynamics of traffic flow. This study aims to develop a novel method for modeling highway traffic flows by integrating data from multiple sources, such as roadside cameras, gantry cameras, and communication devices. The method leverages heterogeneous sensor data characteristics, temporal information, and spatial structures to achieve deep fusion of latent features at the sensor level. To minimize human intervention, a multistage training approach is employed, combining large-scale self-supervised learning with supervised fine-tuning, leveraging abundant unlabeled unstructured data such as monitoring videos recorded by roadside cameras and snapshots captured by gantry cameras, alongside limited accurate structured traffic flow data aggregated by communication devices from gantries and toll stations. We demonstrate the effectiveness and stability of the proposed method on a case study, the G92 ring highway around the Hangzhou Bay in Zhejiang Province, China, achieving the mean absolute percentage error of traffic flow within 7.5% and 8.3% for fixed and variable highway sensor networks, respectively. Ablation studies further demonstrate the significant improvement in predictive accuracy achieved by the designed self-supervised pretraining task. To summarize, our approach provides a promising solution for efficient and safe management of highway traffic flow, with potential applicability to real-world scenarios.
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      A Modeling Method for Complex Traffic Flow on Highways Based on the Fusion of Heterogeneous Data from Multiple Sensors

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4296921
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    contributor authorShaoweihua Liu
    contributor authorYunyan Tang
    contributor authorYiliu He
    contributor authorJunyi Ren
    contributor authorYujie Zhang
    contributor authorXi Luo
    contributor authorHongyun Yang
    date accessioned2024-04-27T22:33:07Z
    date available2024-04-27T22:33:07Z
    date issued2024/06/01
    identifier other10.1061-JTEPBS.TEENG-8207.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296921
    description abstractImproving the efficiency and safety of highway traffic relies heavily on accurately modeling the complex dynamics of traffic flow. This study aims to develop a novel method for modeling highway traffic flows by integrating data from multiple sources, such as roadside cameras, gantry cameras, and communication devices. The method leverages heterogeneous sensor data characteristics, temporal information, and spatial structures to achieve deep fusion of latent features at the sensor level. To minimize human intervention, a multistage training approach is employed, combining large-scale self-supervised learning with supervised fine-tuning, leveraging abundant unlabeled unstructured data such as monitoring videos recorded by roadside cameras and snapshots captured by gantry cameras, alongside limited accurate structured traffic flow data aggregated by communication devices from gantries and toll stations. We demonstrate the effectiveness and stability of the proposed method on a case study, the G92 ring highway around the Hangzhou Bay in Zhejiang Province, China, achieving the mean absolute percentage error of traffic flow within 7.5% and 8.3% for fixed and variable highway sensor networks, respectively. Ablation studies further demonstrate the significant improvement in predictive accuracy achieved by the designed self-supervised pretraining task. To summarize, our approach provides a promising solution for efficient and safe management of highway traffic flow, with potential applicability to real-world scenarios.
    publisherASCE
    titleA Modeling Method for Complex Traffic Flow on Highways Based on the Fusion of Heterogeneous Data from Multiple Sensors
    typeJournal Article
    journal volume150
    journal issue6
    journal titleJournal of Transportation Engineering, Part A: Systems
    identifier doi10.1061/JTEPBS.TEENG-8207
    journal fristpage04024021-1
    journal lastpage04024021-10
    page10
    treeJournal of Transportation Engineering, Part A: Systems:;2024:;Volume ( 150 ):;issue: 006
    contenttypeFulltext
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