Redundancy-Reducing and Holiday Speed Prediction Based on Highway Traffic Speed Data

Gao, Zhigang; Yang, Xiaowei; Zhang, Jianhui; Lu, Huijuan; Xu, Ruifeng; Diao, Wenjie

doi:10.1109/access.2019.2902813

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…While promising for improving transportation systems, DL models present a significant challenge due to their high demand for labeled data to achieve optimal prediction accuracy [229]. Unfortunately, collecting data for traffic-related scenarios often encounters limitations imposed by geographic constraints, specific traffic conditions, or infrequent exceptional events.…”

Section: A Data Availability and Qualitymentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

Preprint

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Section: A Data Availability and Qualitymentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

Preprint

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show abstract

“…accuracy [229]. Unfortunately, collecting data for trafficrelated scenarios often encounters limitations imposed by geographic constraints, specific traffic conditions, or infrequent exceptional events.…”

Section: Oj Logomentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

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“…Compared to traditional traffic flow [ 1 , 3 , 4 , 5 ] prediction and traffic speed prediction [ 6 , 7 , 8 , 9 ], urban traffic congestion prediction mainly focuses on congestion levels of road networks in cities. However, forecasting congestion levels of road networks is very challenging due to the following two complex factors: Spatio-temporal correlation.…”

Section: Introductionmentioning

confidence: 99%

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

Li,

Dai,

Huang

et al. 2024

Sensors

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Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively.

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Redundancy-Reducing and Holiday Speed Prediction Based on Highway Traffic Speed Data

Cited by 9 publications

References 22 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

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