Parallel Computing of Spatio-Temporal Model Based on Deep Reinforcement Learning

Lv, Zhiqiang; Li, Jianbo; Xu, Zhihao; Wang, Yue; Li, Haoran

doi:10.1007/978-3-030-85928-2_31

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…And, the movement of humans in cities is an important reference standard for the division of urban functional regions [19]. The hierarchy of city nodes is an important characteristic of relative spatial distribution [20].…”

Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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<p>This paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development.<strong></strong></p>

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Section: Resultsmentioning

confidence: 99%

A survey of sustainable development of intelligent transportation system based on urban travel demand

Yan,

2024

Sustain. Social. Dev.

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“…By calculating the correlation of the overall features, the salient features, i.e., related features, are identified. By experimenting with overall features and correlation features separately [26][27][28][29], we can see that the experimental results are greatly improved. Therefore, we can also conclude that using relevant features can help improve the model's predictive performance and reduce the data collection cost.…”

Section: Discussionmentioning

confidence: 99%

A Novel Hybrid Model Based on Rule Learning and Dilated Convolution for Vehicle Collision Prediction

Wu,

Liu,

2024

Ann Applied Sciences

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Vehicle collisions are a significant concern in road accidents, particularly with the rise of autonomous driving technology. However, existing studies often struggle to accurately predict collisions due to inconsistent correlations between collected data and collision labels. Therefore, this work quantitatively analyzes traffic accident data and constructs new features with strong correlations to the labels. In this study, a rule classification-dilated convolution network (R-DCN) model, which combines rule learning with dilated convolutional networks, is proposed. The rule learning model predicts partially collided vehicles using predefined rules, resulting in interpretability, high prediction efficiency, and quick computation. The remaining vehicle collisions are estimated using dilated convolutional layers, addressing the issue of missing important features in conventional convolution models. To distinguish between intense collisions (predicted by rule learning) and nonintense collisions (predicted by the dilated convolutional model), the data for training the network are those that remove the intense collision predicted by the rule learning model. The proposed model exhibits enhanced sensitivity to nonintense collision data. Compared to existing models, the approach presented in this work demonstrates superior evaluation metrics and training speed.

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Intelligent Fleet Management Systems in Surface Mining: Status, Threats, and Opportunities

Hazrathosseini,

Afrapoli

2023

Mining, Metallurgy & Exploration

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Parallel Computing of Spatio-Temporal Model Based on Deep Reinforcement Learning

Cited by 11 publications

References 9 publications

A survey of sustainable development of intelligent transportation system based on urban travel demand

A survey of sustainable development of intelligent transportation system based on urban travel demand

A Novel Hybrid Model Based on Rule Learning and Dilated Convolution for Vehicle Collision Prediction

Intelligent Fleet Management Systems in Surface Mining: Status, Threats, and Opportunities

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