Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning
Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningManagementTraffic PredictionSimilar Papers 제목 키워드 기반
Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge
For traffic prediction in transportation services such as car-sharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction h…
PredictionTraffic PredictionUrban Regional Function Guided Traffic Flow Prediction
The prediction of traffic flow is a challenging yet crucial problem in spatial-temporal analysis, which has recently gained increasing interest. In addition to spatial-temporal correlations, the functionality of urban ar…
PredictionGraph Convolutional Network With Pattern-Spatial Interactive and Regional Awareness for Traffic Forecasting
Traffic forecasting is significant for urban traffic management, intelligent route planning, and real-time flow monitoring. Recent advances in spatial-temporal models have markedly improved the modeling of intricate spat…
Urban Traffic Accident Risk Prediction Revisited: Regionality, Proximity, Similarity and Sparsity
Traffic accidents pose a significant risk to human health and property safety. Therefore, to prevent traffic accidents, predicting their risks has garnered growing interest. We argue that a desired prediction solution sh…
Semantic SimilaritySemantic Textual SimilarityCommunication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control
Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique…
Deep Reinforcement LearningTraffic Signal Control