Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

Li, Wan; Ban, Xuegang “Jeff”; Zheng, Jianfeng; Liu, Henry X.; Gong, Cheng; Li, Yong

doi:10.1061/jtepbs.0000384

Title: Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

Abstract

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 periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.

Authors:

^[1];

^[2]; Zheng, Jianfeng ^[3]; Liu, Henry X. ^[4]; Gong, Cheng ^[3]; Li, Yong ^[5]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Univ. of Washington, Seattle, WA (United States)
Didi Chuxing, Beijing (China)
Univ. of Michigan, Ann Arbor, MI (United States)
Jinan Public Security Bureau, Jinan (China)

Publication Date:: Sat Aug 01 00:00:00 EDT 2020

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

Sponsoring Org.:: USDOE

OSTI Identifier:: 1648915

Grant/Contract Number:: AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Transportation Engineering, Part A: Systems

Additional Journal Information:: Journal Volume: 146; Journal Issue: 8; Journal ID: ISSN 2473-2907

Publisher:: American Society of Civil Engineers (ASCE)

Country of Publication:: United States

Language:: English

Subject:: 99 GENERAL AND MISCELLANEOUS

Citation Formats


                    Li, Wan, Ban, Xuegang “Jeff”, Zheng, Jianfeng, Liu, Henry X., Gong, Cheng, and Li, Yong. Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections.  United States: N. p., 2020. 
Web.  doi:10.1061/jtepbs.0000384.

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                    Li, Wan, Ban, Xuegang “Jeff”, Zheng, Jianfeng, Liu, Henry X., Gong, Cheng, & Li, Yong. Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections.  United States.  https://doi.org/10.1061/jtepbs.0000384

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                    Li, Wan, Ban, Xuegang “Jeff”, Zheng, Jianfeng, Liu, Henry X., Gong, Cheng, and Li, Yong. Sat .  
"Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections".  United States.  https://doi.org/10.1061/jtepbs.0000384.  https://www.osti.gov/servlets/purl/1648915.

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@article{osti_1648915,

  title        = {Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections},

  author       = {Li, Wan and Ban, Xuegang “Jeff” and Zheng, Jianfeng and Liu, Henry X. and Gong, Cheng and Li, Yong},

  abstractNote = {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 periodic trends of traffic volume are also considered in the proposed model. This is the first time that a deep-learning method has been applied for movement-based traffic volume prediction at signalized intersections. In the numerical experiment, the proposed model is evaluated using real-world data and simulation data to demonstrate its effectiveness. The impacts of various structures of traffic networks on the proposed model are also discussed. Results show that the proposed model outperforms some of the state-of-the-art volume prediction methods currently in the literature.},

  doi          = {10.1061/jtepbs.0000384},

  journal      = {Journal of Transportation Engineering, Part A: Systems},

  number       = 8,

  volume       = 146,

  place        = {United States},

  year         = {Sat Aug 01 00:00:00 EDT 2020},

  month        = {Sat Aug 01 00:00:00 EDT 2020}

}

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Similar Records

Title: Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections

Abstract

Citation Formats

Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences journal, January 2012

Traffic predictive control from low-rank structure journal, March 2017

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting journal, November 2020

Nonparametric Regression and Short‐Term Freeway Traffic Forecasting journal, March 1991

Travel time prediction with LSTM neural network conference, November 2016

LSTM: A Search Space Odyssey journal, October 2017

Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification journal, June 2014

Long Short-Term Memory journal, November 1997

Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction journal, December 2013

Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method journal, January 2017

Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches journal, January 2003

Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact journal, March 2020

Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow journal, January 2019

Deep learning in fluid dynamics journal, January 2017

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Traffic signal timing optimization in connected vehicles environment conference, June 2017

Connected Vehicles Based Traffic Signal Timing Optimization journal, December 2019

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Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction journal, April 2017

Adaptive Kalman filtering for multi-step ahead traffic flow prediction conference, June 2013

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A Bayesian Network Approach to Traffic Flow Forecasting journal, March 2006

Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models journal, January 1998

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Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition journal, July 2007

Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences
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Traffic predictive control from low-rank structure
journal, March 2017

Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
journal, November 2020

Nonparametric Regression and Short‐Term Freeway Traffic Forecasting
journal, March 1991

Travel time prediction with LSTM neural network
conference, November 2016

LSTM: A Search Space Odyssey
journal, October 2017

Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification
journal, June 2014

Long Short-Term Memory
journal, November 1997

Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction
journal, December 2013

Traffic Flow Prediction with Rainfall Impact Using a Deep Learning Method
journal, January 2017

Forecasting Traffic Flow Conditions in an Urban Network: Comparison of Multivariate and Univariate Approaches
journal, January 2003

Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact
journal, March 2020

Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow
journal, January 2019

Deep learning in fluid dynamics
journal, January 2017

Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting
journal, January 1999

Traffic signal timing optimization in connected vehicles environment
conference, June 2017

Connected Vehicles Based Traffic Signal Timing Optimization
journal, December 2019

Short-term traffic state prediction from latent structures: Accuracy vs. efficiency
journal, February 2020

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
journal, April 2017

Adaptive Kalman filtering for multi-step ahead traffic flow prediction
conference, June 2013

Deep learning for short-term traffic flow prediction
journal, June 2017

A Bayesian Network Approach to Traffic Flow Forecasting
journal, March 2006

Urban Freeway Traffic Flow Prediction: Application of Seasonal Autoregressive Integrated Moving Average and Exponential Smoothing Models
journal, January 1998

A hybrid deep learning based traffic flow prediction method and its understanding
journal, May 2018

Short-Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition
journal, July 2007