Traffic flow prediction models–A review of deep learning techniques

AA Kashyap, S Raviraj, A Devarakonda… - Cogent …, 2022 - Taylor & Francis
Traffic flow prediction is an essential part of the intelligent transport system. This is the
accurate estimation of traffic flow in a given region at a particular interval of time in the future …

Spatiotemporal traffic flow prediction with KNN and LSTM

X Luo, D Li, Y Yang, S Zhang - Journal of Advanced …, 2019 - Wiley Online Library
The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation
Systems. Accurate prediction result is the precondition of traffic guidance, management, and …

Effective long short-term memory with differential evolution algorithm for electricity price prediction

L Peng, S Liu, R Liu, L Wang - Energy, 2018 - Elsevier
Electric power, as an efficient and clean energy, has considerable importance in industries
and human lives. Electricity price is becoming increasingly crucial for balancing electricity …

Attention meets long short-term memory: A deep learning network for traffic flow forecasting

W Fang, W Zhuo, J Yan, Y Song, D Jiang… - Physica A: Statistical …, 2022 - Elsevier
Accurate forecasting of future traffic flow has a wide range of applications, which is a
fundamental component of intelligent transportation systems. However, timely and accurate …

A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting

S Zhang, Y Chen, W Zhang, R Feng - Information Sciences, 2021 - Elsevier
In the past decade, deep learning models have shown to be promising tools for time series
forecasting. However, owing to significant differences in the volatility characteristics among …

A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting

H Lu, Z Ge, Y Song, D Jiang, T Zhou, J Qin - Neurocomputing, 2021 - Elsevier
Short-term traffic flow forecasting at isolated points is a fundamental yet challenging task in
many intelligent transportation systems. We present a novel long short-term memory (LSTM) …

Δfree-LSTM: An error distribution free deep learning for short-term traffic flow forecasting

W Fang, W Zhuo, Y Song, J Yan, T Zhou, J Qin - Neurocomputing, 2023 - Elsevier
Timely and accurate traffic flow forecasting is open challenging. Canonical long short-term
memory (LSTM) network is considered qualified to capture the long-term temporal …

A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge

Y Bai, J **e, D Wang, W Zhang, C Li - Computers & Industrial Engineering, 2021 - Elsevier
Manufacturing quality prediction is one of the significant concerns in modern enterprise
production management, which provides data support for reliability assessment and …

Deep Learning Algorithms for Traffic Forecasting: A Comprehensive Review and Comparison with Classical Ones

S Afandizadeh, S Abdolahi… - Journal of Advanced …, 2024 - Wiley Online Library
Accurate and timely forecasting of critical components is pivotal in intelligent transportation
systems and traffic management, crucially mitigating congestion and enhancing safety. This …

Forecasting transportation network speed using deep capsule networks with nested LSTM models

X Ma, H Zhong, Y Li, J Ma, Z Cui… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Accurate and reliable traffic forecasting for complicated transportation networks is of vital
importance to modern transportation management. The complicated spatial dependencies …