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

Journal Article · Sat Aug 01 00:00:00 EDT 2020 · Journal of Transportation Engineering, Part A: Systems

DOI:https://doi.org/10.1061/jtepbs.0000384· OSTI ID:1648915

^[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)

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.

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

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1648915

Journal Information:: Journal of Transportation Engineering, Part A: Systems, Vol. 146, Issue 8; ISSN 2473-2907

Publisher:: American Society of Civil Engineers (ASCE)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 7 works

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