Back to Search
Start Over
Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction
- Source :
- Future Transportation; Volume 1; Issue 1; Pages: 21-37, Future Transportation, Volume 1, Issue 1, Pages 21-37
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.
- Subjects :
- Computer science
spatial and temporal analysis
010501 environmental sciences
short-term prediction
computer.software_genre
01 natural sciences
0502 economics and business
Resilience (network)
0105 earth and related environmental sciences
speed forecasting
050210 logistics & transportation
Artificial neural network
business.industry
Deep learning
05 social sciences
Detector
Ranging
machine learning
Backpropagation
Term (time)
Recurrent neural network
Artificial intelligence
Data mining
business
computer
Subjects
Details
- Language :
- English
- ISSN :
- 26737590
- Database :
- OpenAIRE
- Journal :
- Future Transportation; Volume 1; Issue 1; Pages: 21-37
- Accession number :
- edsair.doi.dedup.....29c971b25a0c7f34b4621d5f295e0064
- Full Text :
- https://doi.org/10.3390/futuretransp1010003