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

Journal Article · · Transportation Research Record
 [1];  [2];  [1];  [3]
  1. Univ. of Washington, Seattle, WA (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  3. Univ. of Washington, Seattle, WA (United States); Pacific U.S. DOT Univ. Transportation Center for Federal Region 10, Seattle, WA (United States)
+ Show Author Affiliations
Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.
Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
1648914
Journal Information:
Transportation Research Record, Journal Name: Transportation Research Record Journal Issue: 4 Vol. 2674; ISSN 0361-1981
Publisher:
National Academy of Sciences, Engineering and MedicineCopyright Statement
Country of Publication:
United States
Language:
English

References (18)

Multilane traffic flow dynamics: Some macroscopic considerations journal August 1984
Short-term inter-urban traffic forecasts using neural networks journal March 1997
A behavioral theory of multi-lane traffic flow. Part II: Merges and the onset of congestion journal February 2002
Traffic Flow Prediction using Kalman Filtering Technique journal January 2017
Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium journal October 2015
Traffic speed prediction for urban transportation network: A path based deep learning approach journal March 2019
Deep learning journal May 2015
Traffic flow data compression considering burst components journal November 2017
Innovative method for traffic data imputation based on convolutional neural network journal April 2019
Traffic Prediction Using Multivariate Nonparametric Regression journal March 2003
Macroscopic dynamics of multilane traffic journal June 1999
A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision conference July 2017
Short Term Prediction of Traffic Parameters Using Support Vector Machines Technique
  • Theja, P. V. V. K.; Vanajakshi, L.
  • Third International Conference on Emerging Trends in Engineering and Technology (ICETET 2010), 2010 3rd International Conference on Emerging Trends in Engineering and Technology https://doi.org/10.1109/ICETET.2010.37
conference November 2010
A comparison of the performance of artificial. neural networks and support vector machines for the prediction of traffic speed conference January 2004
Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos journal April 2017
A Hierarchy of Models for Multilane Vehicular Traffic I: Modeling journal January 1998
Flexible and Robust Method for Missing Loop Detector Data Imputation
  • Henrickson, Kristian; Zou, Yajie; Wang, Yinhai
  • Transportation Research Record: Journal of the Transportation Research Board, Vol. 2527, Issue 1 https://doi.org/10.3141/2527-04
journal January 2015
Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks journal June 2017

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