Short-term traffic state prediction from latent structures: Accuracy vs. efficiency

Li, Wan; Wang, Jingxing; Fan, Rong; Zhang, Yiran; Guo, Qiangqiang; Siddique, Choudhury; Ban, Xuegang

doi:10.1016/j.trc.2019.12.007

Title: Short-term traffic state prediction from latent structures: Accuracy vs. efficiency

Full Record
Other Related Research

Abstract

Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.

Authors:

^[1]; Wang, Jingxing ^[2]; Fan, Rong ^[2]; Zhang, Yiran ^[2]; Guo, Qiangqiang ^[2]; Siddique, Choudhury ^[2]; Ban, Xuegang ^[2]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Washington, Seattle, WA (United States)

Publication Date:: Tue Dec 24 00:00:00 EST 2019

Research Org.:: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1649384

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Transportation Research Part C: Emerging Technologies

Additional Journal Information:: Journal Volume: 111; Journal Issue: -; Journal ID: ISSN 0968-090X

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS

Citation Formats


                    Li, Wan, Wang, Jingxing, Fan, Rong, Zhang, Yiran, Guo, Qiangqiang, Siddique, Choudhury, and Ban, Xuegang. Short-term traffic state prediction from latent structures: Accuracy vs. efficiency.  United States: N. p., 2019. 
Web.  doi:10.1016/j.trc.2019.12.007.

Copy to clipboard


                    Li, Wan, Wang, Jingxing, Fan, Rong, Zhang, Yiran, Guo, Qiangqiang, Siddique, Choudhury, & Ban, Xuegang. Short-term traffic state prediction from latent structures: Accuracy vs. efficiency.  United States.  https://doi.org/10.1016/j.trc.2019.12.007

Copy to clipboard


                    Li, Wan, Wang, Jingxing, Fan, Rong, Zhang, Yiran, Guo, Qiangqiang, Siddique, Choudhury, and Ban, Xuegang. Tue .  
"Short-term traffic state prediction from latent structures: Accuracy vs. efficiency".  United States.  https://doi.org/10.1016/j.trc.2019.12.007.  https://www.osti.gov/servlets/purl/1649384.

Copy to clipboard


                    
@article{osti_1649384,

  title        = {Short-term traffic state prediction from latent structures: Accuracy vs. efficiency},

  author       = {Li, Wan and Wang, Jingxing and Fan, Rong and Zhang, Yiran and Guo, Qiangqiang and Siddique, Choudhury and Ban, Xuegang},

  abstractNote = {Recently, deep learning models have shown promising performances in many research areas, including traffic states prediction, due to their ability to model complex nonlinear relationships. However, deep learning models also have drawbacks that make them less preferable for certain short-term traffic prediction applications. For example, they require a large amount of data for model training, which is also computationally expensive. Moreover, deep learning models lack interpretability of the results. This paper develops a short-term traffic states forecasting algorithm based on partial least square (PLS) to help enhance real-time decision-making and build better insights into traffic data. The proposed model is capable of predicting short-term traffic states accurately and efficiently by capturing dominant spatiotemporal features and day-to-day variations from collinear and correlated traffic data. Three case studies are developed to demonstrate the proposed model in short-term traffic prediction applications.},

  doi          = {10.1016/j.trc.2019.12.007},

  journal      = {Transportation Research Part C: Emerging Technologies},

  number       = -,

  volume       = 111,

  place        = {United States},

  year         = {Tue Dec 24 00:00:00 EST 2019},

  month        = {Tue Dec 24 00:00:00 EST 2019}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1016/j.trc.2019.12.007

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 38 works

Citation information provided by
Web of Science

Save / Share:

Export Metadata

Save to My Library

Similar Records in DOE PAGES and OSTI.GOV collections:

Forecasting Nodal Price Difference Between Day-Ahead and Real-Time Electricity Markets Using Long-Short Term Memory and Sequence-to-Sequence Networks

Journal Article Das, Ronit ; Bo, Rui ; Chen, Haotian ; ... - IEEE Access

Price forecasting is at the center of decision making in electricity markets. Much research has been done in forecasting energy prices for a single market while little research has been reported on forecasting price difference between markets, which presents higher volatility and yet plays a critical role in applications such as virtual trading. To this end, this paper takes the first attempt at it and employs novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units and Sequence-to-Sequence (Seq2Seq) architecture to forecast nodal price difference between day-ahead and real-time markets. In addition to value prediction, these deep learning architecturesmore »« less
https://doi.org/10.1109/ACCESS.2021.3133499
MSASGCN : Multi-Head Self-Attention Spatiotemporal Graph Convolutional Network for Traffic Flow Forecasting

Journal Article Cao, Yang ; Liu, Detian ; Yin, Qizheng ; ... - Journal of Advanced Transportation

Traffic flow forecasting is an essential task of an intelligent transportation system (ITS), closely related to intelligent transportation management and resource scheduling. Dynamic spatial-temporal dependencies in traffic data make traffic flow forecasting to be a challenging task. Most existing research cannot model dynamic spatial and temporal correlations to achieve well-forecasting performance. The multi-head self-attention mechanism is a valuable method to capture dynamic spatial-temporal correlations, and combining it with graph convolutional networks is a promising solution. Therefore, we propose a multi-head self-attention spatiotemporal graph convolutional network (MSASGCN) model. It can effectively capture local correlations and potential global correlations of spatial structures,more »« less
https://doi.org/10.1155/2022/2811961
Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

Journal Article Li, Wan ; Ban, Xuegang “Jeff” ; Zheng, Jianfeng ; ... - Journal of Transportation Engineering, Part A: Systems

The traffic volume of each movement at signalized intersections can provide valuable information on real-time traffic conditions that enable traffic control systems to dynamically respond to the fluctuated traffic demands. Real-time movement-based traffic volume prediction is challenging due to various nonlinear spatial relationships at different locations/approaches and the complicated underlying temporal dependencies. In this study, a novel deep intersection spatial-temporal network (DISTN) is developed for real-time movement-based traffic volume prediction at signalized intersections, which considers both spatial and temporal features by the convolutional neural network (CNN) and long short-term memory (LSTM), respectively. In addition, the within-day, daily, and weekly periodicmore »« less
Cited by 7
https://doi.org/10.1061/jtepbs.0000384

Full Text Available
Explore Spatio‐Temporal Learning of Large Sample Hydrology Using Graph Neural Networks

Journal Article Sun, Alexander Y. ; Jiang, Peishi ; Mudunuru, Maruti K. ; ... - Water Resources Research

Abstract Streamflow forecasting over gauged and ungauged basins play a vital role in water resources planning, especially under the changing climate. Increased availability of large sample hydrology data sets, together with recent advances in deep learning techniques, has presented new opportunities to explore temporal and spatial patterns in hydrological signatures for improving streamflow forecasting. The purpose of this study is to adapt and benchmark several state‐of‐the‐art graph neural network (GNN) architectures, including ChebNet, Graph Convolutional Network (GCN), and GraphWaveNet, for end‐to‐end graph learning. We explicitly represent river basins as nodes in a graph, learn the spatiotemporal nodal dependencies, and thenmore »« less
https://doi.org/10.1029/2021WR030394
Evaluation of regression and neural network models for solar forecasting over different short-term horizons

Journal Article Inanlouganji, Alireza ; Reddy, Agami ; Katipamula, Srinivas - Science and Technology for the Built Environment

Forecasting solar irradiation has acquired immense importance in view of the exponential increase in the number of solar pho- tovoltaic (PV) system installations. In this article, analyses results involving statistical and machine-learning techniques to predict solar irradiation for di?erent forecasting horizons are reported. Yearlong typical meteorological year 3 (TMY3) datasets from three cities in the United States with di?erent climatic conditions have been used in this analysis. A simple forecast approach that assumes consecutive days to be identical serves as a baseline model to compare forecasting alternatives. To account for seasonal variability and to capture short-term ?uctuations, di?erent variants ofmore »« less
https://doi.org/10.1080/23744731.2018.1464348

Similar Records