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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 …
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 …
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
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 …
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 …
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 …
forecasting. However, owing to significant differences in the volatility characteristics among …
A temporal-aware LSTM enhanced by loss-switch mechanism for traffic flow forecasting
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) …
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
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 …
memory (LSTM) network is considered qualified to capture the long-term temporal …
A manufacturing quality prediction model based on AdaBoost-LSTM with rough knowledge
Manufacturing quality prediction is one of the significant concerns in modern enterprise
production management, which provides data support for reliability assessment and …
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 …
systems and traffic management, crucially mitigating congestion and enhancing safety. This …
Forecasting transportation network speed using deep capsule networks with nested LSTM models
Accurate and reliable traffic forecasting for complicated transportation networks is of vital
importance to modern transportation management. The complicated spatial dependencies …
importance to modern transportation management. The complicated spatial dependencies …