Data fusion for ITS: A systematic literature review
C Ounoughi, SB Yahia - Information Fusion, 2023 - Elsevier
In recent years, the development of intelligent transportation systems (ITS) has involved the
input of various kinds of heterogeneous data in real time and from multiple sources, which …
input of various kinds of heterogeneous data in real time and from multiple sources, which …
Hybrid structures in time series modeling and forecasting: A review
Z Hajirahimi, M Khashei - Engineering Applications of Artificial Intelligence, 2019 - Elsevier
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …
of single models in processing various patterns and relationships latent in data, hybrid …
LSTM-based traffic flow prediction with missing data
Traffic flow prediction plays a key role in intelligent transportation systems. However, since
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …
traffic sensors are typically manually controlled, traffic flow data with varying length, irregular …
Short-term prediction of lane-level traffic speeds: A fusion deep learning model
Accurate and robust short-term traffic prediction is an important part of advanced traveler
information systems. With the development of intelligent navigation and autonomous driving …
information systems. With the development of intelligent navigation and autonomous driving …
An improved Bayesian combination model for short-term traffic prediction with deep learning
Short-term traffic volume prediction, which can assist road users in choosing appropriate
routes and reducing travel time cost, is a significant topic of intelligent transportation system …
routes and reducing travel time cost, is a significant topic of intelligent transportation system …
Transferability improvement in short-term traffic prediction using stacked LSTM network
Short-term traffic flow forecasting is a key element in Intelligent Transport Systems (ITS) to
provide proactive traffic state information to road network operators. A variety of methods to …
provide proactive traffic state information to road network operators. A variety of methods to …
Hybrid machine learning algorithm and statistical time series model for network-wide traffic forecast
T Ma, C Antoniou, T Toledo - Transportation Research Part C: Emerging …, 2020 - Elsevier
We propose a novel approach for network-wide traffic state prediction where the statistical
time series model ARIMA is used to postprocess the residuals out of the fundamental …
time series model ARIMA is used to postprocess the residuals out of the fundamental …
Short-term abnormal passenger flow prediction based on the fusion of SVR and LSTM
J Guo, Z Xie, Y Qin, L Jia, Y Wang - Ieee Access, 2019 - ieeexplore.ieee.org
Passenger flow prediction is important for the operation of urban rail transit. The prediction of
abnormal passenger flow is difficult due to rare similar history data. A model based on the …
abnormal passenger flow is difficult due to rare similar history data. A model based on the …
Data fusion for estimating Macroscopic Fundamental Diagram in large-scale urban networks
Since the concept of the Macroscopic Fundamental Diagram (MFD) has been introduced,
many studies have investigated the existence and characteristics of the MFD using empirical …
many studies have investigated the existence and characteristics of the MFD using empirical …
Predicting cycle-level traffic movements at signalized intersections using machine learning models
Predicting accurate traffic parameters is fundamental and cost-effective in providing traffic
applications with required information. Many studies adopted various parametric and …
applications with required information. Many studies adopted various parametric and …