Traffic Prediction using Time-Space Diagram: A Convolutional Neural Network Approach

Mohammadreza Khajeh Hosseini, Alireza Talebpour

Research output: Contribution to journalArticlepeer-review


Traffic prediction is a major component of any traffic management system. With the increase in data sources and advancement in connectivity, data analysis and machine learning approaches for traffic prediction have gained a lot of attention. Most of the existing data analysis approaches in traffic prediction rely on aggregated inputs such as flow and density, with limited studies using the individual vehicle-level data. The time-space diagram of the vehicles can be constructed from the connected vehicles’ data. This plot is comprehensive and contains all the information about traffic flow dynamics at both microscopic and macroscopic levels. Accordingly, this study introduces a deep learning-based methodology to directly predict the traffic state based on the time-space diagram with the use of convolutional neural networks (CNN). The time-space diagram is directly used as the input to the traffic prediction model using a CNN. The prediction capability of the proposed model is compared with multilayer perceptron, support vector regression, and autoregressive integrated moving average, and the results indicate a superior capability of CNN in predicting flow and density across all possible values of these parameters.

Original languageEnglish (US)
Pages (from-to)425-435
Number of pages11
JournalTransportation Research Record
Issue number7
StatePublished - Jul 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering


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