Short-term vessel traffic flow forecasting by using an improved Kalman model
Sign Up to like & getrecommendations! Published in 2017 at "Cluster Computing"
DOI: 10.1007/s10586-017-1491-2
Abstract: Vessel traffic flow forecasting is of significant importance for the water transport safety, especially in the multi-bridge water areas. An improved Kalman model combining regression analysis and Kalman filtering is proposed for short-term vessel traffic… read more here.
Keywords: vessel traffic; traffic; flow forecasting; traffic flow ... See more keywords
Real-Time Flow Forecasting in a Watershed Using Rainfall Forecasting Model and Updating Model
Sign Up to like & getrecommendations! Published in 2019 at "Water Resources Management"
DOI: 10.1007/s11269-019-02398-2
Abstract: Watershed is the basic unit for studying different hydrologic processes. Flow forecasting in a watershed is dependent upon the rainfall. The effect of erroneous rainfall prediction is a source of uncertainty in flow forecasting. In… read more here.
Keywords: time; model; flow forecasting; forecasting model ... See more keywords
Hourly River Flow Forecasting: Application of Emotional Neural Network Versus Multiple Machine Learning Paradigms
Sign Up to like & getrecommendations! Published in 2020 at "Water Resources Management"
DOI: 10.1007/s11269-020-02484-w
Abstract: Monitoring hourly river flows is indispensable for flood forecasting and disaster risk management. The objective of the present study is to develop a suite of hourly river flow forecasting models for the Albert river, located… read more here.
Keywords: hourly river; machine learning; river flow; flow forecasting ... See more keywords
Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model
Sign Up to like & getrecommendations! Published in 2018 at "KSCE Journal of Civil Engineering"
DOI: 10.1007/s12205-017-1016-9
Abstract: Metro passenger flow forecasting is an essential component of intelligent transportation system. To enhance the forecasting accuracy and explainable of traditional models, a hybrid model combining symbolic regression and Autoregressive Integrated Moving Average Model (ARIMA)… read more here.
Keywords: passenger flow; model; flow forecasting; hybrid model ... See more keywords
Effective passenger flow forecasting using STL and ESN based on two improvement strategies
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.04.061
Abstract: Abstract Accurate passenger flow prediction is fairly challenging because of chaotic nature of transportation system and influence mechanism originated from multiple factors. It has been found that passenger flow has a nonlinear characteristic and a… read more here.
Keywords: stl; flow forecasting; effective passenger; passenger ... See more keywords
Towards an Operational Flow Forecasting System for the Upper Niagara River
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Hydraulic Engineering"
DOI: 10.1061/(asce)hy.1943-7900.0001781
Abstract: AbstractThe authors developed a Hydrologic Engineering Center–River Analysis System (HEC–RAS) model to serve as the key component of a new, first-of-its-kind, short-term operational flow forecastin... read more here.
Keywords: towards operational; system; river; flow forecasting ... See more keywords
DGSLSTM: Deep Gated Stacked Long Short-Term Memory Neural Network for Traffic Flow Forecasting of Transportation Networks on Big Data Environment.
Sign Up to like & getrecommendations! Published in 2022 at "Big data"
DOI: 10.1089/big.2021.0013
Abstract: Deep learning and big data techniques have become increasingly popular in traffic flow forecasting. Deep neural networks have also been applied to traffic flow forecasting. Furthermore, it is difficult to determine whether neural networks can… read more here.
Keywords: big data; traffic; flow forecasting; traffic flow ... See more keywords
NCAE and ELM Based Enhanced Ensemble Optimized Model for Traffic Flow Forecasting
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Access"
DOI: 10.1109/access.2020.3034763
Abstract: Accurate and timely traffic flow forecasting is the key technology to address the issue of urban traffic congestion, which is significant for intelligent transportation system. However, it is a quite challenging task to develop an… read more here.
Keywords: ncae elm; flow forecasting; model; traffic ... See more keywords
Spatiotemporal Hashing Multigraph Convolutional Network for Service-Level Passenger Flow Forecasting in Bus Transit Systems
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3116241
Abstract: Multistep service-level passenger flow forecasting is of great value in bus transit systems. This task is faced with great challenges due to complicated and dynamic spatial–temporal dependencies, such as interstation semantic dependencies, interline spatial dependencies,… read more here.
Keywords: service level; passenger; flow forecasting; passenger flow ... See more keywords
Deep Learning Architecture for Short-Term Passenger Flow Forecasting in Urban Rail Transit
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2020.3000761
Abstract: Short-term passenger flow forecasting is an essential component in urban rail transit operation. Emerging deep learning models provide good insight into improving prediction precision. Therefore, we propose a deep learning architecture combining the residual network… read more here.
Keywords: term; term passenger; flow forecasting; short term ... See more keywords
Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Intelligent Transportation Systems"
DOI: 10.1109/tits.2023.3234512
Abstract: Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail… read more here.
Keywords: graph convolutional; traffic; flow forecasting; term ... See more keywords