NDLI: Multi-step traffic speed prediction model with auxiliary features on urban road networks and its understanding

Content Provider	IET Digital Library
Author	Guo, Jinlong Song, Chunyue Zhang, Hao Wang, Hui
Abstract	Multi-step prediction of long-term traffic speed is an important part of the intelligent transportation system. Traffic speed is affected by temporal features, spatial features, and various environmental features. The prediction of traffic speed considering the above features is a big challenge. This study proposed a multi-step prediction model named embedding graph convolutional long short-term memory network (EGC-LSTM) for urban road network traffic speed prediction which can deal with spatial–temporal correlation and auxiliary features at the same time. Firstly, a graph convolutional network (GCN) for capturing directed graph properties is proposed. Based on the GCN, the LSTM and sequence to sequence model are further applied to realise multi-step prediction considering the spatial–temporal correlation of the traffic network. To improve the performance of the model and obtain the importance of each step in the historical data, the attention mechanism is introduced. Then, one-hot encoding is applied to the category-type auxiliary features. Considering that the dimension becomes larger after the features are one-hot encoded, the dimensions are reduced using embedding. The experiment results prove that the proposed model's performance is better than other models, and the model is interpreted in detail.
Starting Page	1997
Ending Page	2009
Page Count	13
ISSN	1751956X
Volume Number	14
e-ISSN	17519578
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2020.0284
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/14/14
Journal	IET Intelligent Transport Systems
Publisher Date	2021-01-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Attention Mechanism Category-type Auxiliary Features Combinatorial Mathematics Convolutional Neural Nets Directed Graph Directed Graph Property EGC-LSTM Embedding Graph Convolutional Long Short-term Memory Network Environmental Feature Graph Convolutional Network Intelligent Transport System Long-term Traffic Speed Multistep Traffic Speed Prediction Model One-hot Encoding Recurrent Neural Nets Road Traffic Sequence to Sequence Model Spatial Feature Spatial–temporal Correlation Temporal Feature Traffic Engineering Computing Traffic Network Urban Road Network Urban Road Network Traffic Speed Prediction
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Short-term traffic congestion prediction with Conv–BiLSTM considering spatio-temporal features

Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

Short-term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation

Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model

Investigating long-term vehicle speed prediction based on BP-LSTM algorithms

Prediction of vehicle energy consumption on a planned route based on speed features forecasting

Urban traffic flow online prediction based on multi-component attention mechanism

An Efficient Short-Term Traffic Speed Prediction Model Based on Improved TCN and GCN

Multi-step traffic speed prediction model with auxiliary features on urban road networks and its understanding

Similar Documents

Short-term traffic congestion prediction with Conv–BiLSTM considering spatio-temporal features

Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features

Spatial-Temporal 3D Residual Correlation Network for Urban Traffic Status Prediction

Short-term prediction of traffic flow under incident conditions using graph convolutional recurrent neural network and traffic simulation

Spatiotemporal Features—Extracted Travel Time Prediction Leveraging Deep-Learning-Enabled Graph Convolutional Neural Network Model

Investigating long-term vehicle speed prediction based on BP-LSTM algorithms

Prediction of vehicle energy consumption on a planned route based on speed features forecasting

Urban traffic flow online prediction based on multi-component attention mechanism

An Efficient Short-Term Traffic Speed Prediction Model Based on Improved TCN and GCN

Multi-step traffic speed prediction model with auxiliary features on urban road networks and its understanding