Urban regional function guided traffic flow prediction

Wang, Kuo; Liu, LingBo; Liu, Yang; Li, GuanBin; Zhang, Fan; Li, Lin

doi:10.1016/j.ins.2023.03.109

Cited by 10 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, it utilizes a nonlocal enhanced TSC agent to predict future trafc conditions more accurately, enabling more precise trafc control. Wang et al proposed a POI-MetaBlock network that utilizes the functionality of each region (represented by the distribution of points of interest) as metadata to further explore diferent trafc features within regions with diferent functionalities [55]. Tis model can be seamlessly integrated into traditional trafc fow prediction models, signifcantly enhancing prediction performance.…”

Section: Spatiotemporal Semantic Understandingmentioning

confidence: 99%

Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network

Shen,

Cai,

Fang

et al. 2024

Journal of Advanced Transportation

View full text Add to dashboard Cite

Accurate air traffic flow prediction assists controllers formulate control strategies in advance and alleviate air traffic congestion, which is important to flight safety. While existing works have made significant efforts in exploring the high dynamics and heterogeneous interactions of historical air traffic flow, two key challenges still remain. (1) The transfer patterns of air traffic are intricate, subject to numerous constraints and limitations such as controllers, flight regulations, and other regulatory factors. Relying solely on mining historical traffic evolution patterns makes it difficult to accurately predict the constrained air traffic flow. (2) Weather conditions exert a substantial influence on air traffic, making it exceptionally difficult to simulate the impact of external factors (such as thunderstorms) on the evolution of air traffic flow patterns. To address these two challenges, we propose a Spatiotemporal Knowledge Distillation Network (ST-KDN) for air traffic flow prediction. Firstly, recognizing the inherent future insights embedded within flight plans, we develop a “teacher-student” distillation model. This model leverages the prior knowledge of upstream-downstream migration patterns and future air traffic trends inherent in flight plans. Subsequently, to model the influence of external factors and predict air traffic flow disturbed by thunderstorm weather, we propose a student network based on the “parallel-fusion” structure. Finally, employing a feature-based knowledge distillation approach to integrate prior knowledge from flight plans and extract meteorological features, our method can accurately capture complex and constrained spatiotemporal dependencies in air traffic and explicitly model the impact of weather on air traffic flow. Experimental results on real-world flight data demonstrate that our method can achieve better prediction performance than other state-of-the-art comparison methods, and the advantages of the proposed method are particularly prominent in modeling the complicated transfer pattern of air traffic and inferring nonrecurrent flow patterns.

show abstract

Section: Spatiotemporal Semantic Understandingmentioning

confidence: 99%

Air Traffic Flow Prediction with Spatiotemporal Knowledge Distillation Network

Shen,

Cai,

Fang

et al. 2024

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…The capacity of the existing road network has gradually become unable to handle such a heavy load, which has triggered more transportation-related problems, including traffic congestion and traffic accidents. Traffic congestion [1][2][3] is an urbanization problem that co-exists worldwide. In order to promote green and sustainable development, traffic forecasting is a key enabler of Intelligent Transportation Systems (ITS) [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Weather Interaction-Aware Spatio-Temporal Attention Networks for Urban Traffic Flow Prediction

Zhong,

Wang,

Chen

et al. 2024

Buildings

View full text Add to dashboard Cite

As the cornerstone of intelligent transportation systems, accurate traffic prediction can reduce the pressure of urban traffic, reduce the cost of residents’ travel time, and provide a reference basis for urban construction planning. Existing traffic prediction methods focus on spatio-temporal dependence modeling, ignoring the influence of weather factors on spatio-temporal characteristics, and the prediction task has complexity and an uneven distribution in different spatio-temporal scenarios and weather changes. In view of this, we propose a weather interaction-aware spatio-temporal attention network (WST-ANet), in which we integrate feature models and dynamic graph modules in the encoder and decoder, and use a spatio-temporal weather interaction perception module for prediction. Firstly, the contextual semantics of the traffic flows are fused using a feature embedding module to improve the adaptability to weather drivers; then, an encoder–decoder is constructed by combining the Dynamic Graph Module and the WSTA Block, to extract spatio-temporal aggregated correlations in the roadway network; finally, the feature information of the encoder was weighted and aggregated using the cross-focusing mechanism, and attention was paid to the hidden state of the encoding. Traffic flow was predicted using the PeMS04 and PeMS08 datasets and compared with multiple typical baseline models. It was learned through extensive experiments that the accuracy evaluation result is the smallest in WST-ANet, which demonstrated the superiority of the proposed model. This can more accurately predict future changes in traffic in different weather conditions, providing decision makers with a basis for optimizing scenarios.

show abstract