Trajectory-Based Embedding for Random Coefficients of a Theory-Based Car-Following Model

Kang, Yubin; Kim, Gyeongjun; Jeong, Seunyun; Sohn, Keemin

doi:10.2139/ssrn.4066993

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Following this, models based on long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied to the car-following prediction problem [39,40] and have shown superior performance compared to the basic RNN model due to their more efcient memory cells [41]. Additionally, the Sequence-to-Sequence (Seq2Seq) and Encoder-Decoder architecture have demonstrated their advantages in multistep input and output performance [42,43].…”

Section: Introductionmentioning

confidence: 99%

Transformer‐Enhanced Traffic Load Simulation for Wear Evaluation of Bridge Expansion Joint

Dong,

Pan,

Wang

et al. 2024

Structural Control and Health Monitoring

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Timely wear evaluation is crucial in maintaining the functionality of bridge expansion joints (BEJs), ultimately ensuring the safety of bridges. Despite the significance of traffic load simulation (TLS) in simulation-based evaluation methods, existing TLS approaches face challenges in accurately modeling in situ traffic flow at a high fidelity. This paper presents a novel methodology and its application for evaluating the wear performance of BEJs, employing a Transformer-enhanced TLS approach. Initially, a tailored dataset is crafted for data-driven car-following modeling, leveraging an established spatial-temporal traffic load monitoring system. High-fidelity TLS with a mean absolute error (MAE) of 0.1738 m/s is then achieved using Transformer modules equipped with an attention mechanism. To evaluate the final wear life of BEJs, transient dynamic analysis and a calibrated finite element model of the bridge are employed to extract cumulative displacement. Additionally, a surrogate model is developed to depict the relationship between the hourly traffic weight on the entire bridge deck and the cumulative displacement of BEJs, yielding an impressive R-squared value of 0.96619. Comparative results demonstrate the superior performance of our proposed TLS approach over other data-driven approaches, with the linear model derived from our TLS approach outperforming the model generated by the conventional Monte Carlo-based TLS approach. To conclude, our proposed TLS emerges as a comprehensive and precise methodology for the wear evaluation of BEJs.

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