Development of a Safety Prediction Method for Arterial Roads Based on Big-Data Technology and Stacked AutoEncoder-Gated Recurrent Unit

Hao, Wei; Rong, Donglei; Zhang, Zhaolei; Wu, Qiyu; Byon, Young-Ji; Yi, Kaijun; Tang, Jinjun; Lyu, Nengchao

doi:10.1109/tits.2022.3172480

Cited by 6 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model consisted of a spatial-temporal geographical module and a spatial-temporal semantic module, which captured the relevant correlations, along with the utilization of a weighted loss function. Hao et al [84] presented an enhanced active safety prediction approach that utilized big data and a stacked autoencoder-gated recurrent unit (SAE-GRU) to predict the safety levels based on the recognition results.…”

Section: Crash Risk Predictionmentioning

confidence: 99%

“…In recent times, artificial intelligence (AI) models have shown the capability to address the problem of data imbalance by generating synthetic data. One popular example is the utilization of variational autoencoders (VAEs) and convolutional autoencoders, as evidenced in studies by Zhao et al [12], Chen et al [75], Hao et al [84], and Islam et al [87]. However, VAEs may not produce samples that are as realistic as those generated by generative models such as Generative Adversarial Networks (GANs), primarily due to the use of the L2 loss function.…”

Section: Sparseness Of Datamentioning

confidence: 99%

See 1 more Smart Citation

Safety in Traffic Management Systems: A Comprehensive Survey

Dash

et al. 2023

Designs

View full text Add to dashboard Cite

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

show abstract

Section: Crash Risk Predictionmentioning

confidence: 99%