LSTM network: a deep learning approach for short‐term traffic forecast

Zheng Zhao · Weihai Chen · Xingming Wu · Peter C. Y. Chen · Jingmeng Liu

1,899 citations0/0 files storedIET Intelligent Transport Systems

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Traffic Prediction and Management Techniques Air Quality Monitoring and Forecasting Hydrological Forecasting Using AI

Abstract

Short‐term traffic forecast is one of the essential issues in intelligent transportation system. Accurate forecast result enables commuters make appropriate travel modes, travel routes, and departure time, which is meaningful in traffic management. To promote the forecast accuracy, a feasible way is to develop a more effective approach for traffic data analysis. The availability of abundant traffic data and computation power emerge in recent years, which motivates us to improve the accuracy of short‐term traffic forecast via deep learning approaches. A novel traffic forecast model based on long short‐term memory (LSTM) network is proposed. Different from conventional forecast models, the proposed LSTM network considers temporal–spatial correlation in traffic system via a two‐dimensional network which is composed of many memory units. A comparison with other representative forecast models validates that the proposed LSTM network can achieve a better performance.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 1,899 citations exist upstream.

References

2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2004Travel-Time Prediction With Support Vector Regression 2014Short-term traffic forecasting: Where we are and where we’re going 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 1984Dynamic prediction of traffic volume through Kalman filtering theory 2015A gradient boosting method to improve travel time prediction 1996Combining kohonen maps with arima time series models to forecast traffic flow

Cited by

2019Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting 2020Applications of Artificial Intelligence and Machine learning in smart cities 2017Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach 2017Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks 2018

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.