TCPModel: A Short-Term Traffic Congestion Prediction Model Based on Deep Learning

Xiujuan Xu, Xiaobo Gao, Zhenzhen Xu, Xiaowei Zhao, Wei Pang, Hongmei Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)


With the progress of the urbanization, a series of traffic problems have occurred because of the growing urban population and the far lower growth rate of roads than that of cars. One of the most prominent problems is traffic congestion problem. The prediction of traffic congestion is the key to alleviate traffic congestion. To ensure the real-time performance and accuracy of the traffic congestion prediction, we propose a short-term traffic congestion prediction model called TCPModel based on deep learning. By processing a massive amount of urban taxi transportation data, we extract the traffic volume and average speed of taxis which are the most important parameters for assessing of traffic flow prediction. After analyzing the temporal and spatial distribution characteristics of the traffic flow and average speed, we present a short-term traffic volume prediction model called TVPModel, and a short-term traffic speed prediction model called TSPModel. Both models are based on a deep learning method Stacked Auto Encoder (SAE). By comparing the other traffic flow forecasting methods and average speed forecasting methods, the methods proposed by this paper have improved the accuracy rate. For traffic congestion recognition, we use a novel model called TCPModel based on three traffic parameters (average speed, traffic flow and density), which uses standard function method to standardize the parameters and calculate the congestion comprehensive threshold to determine the congestion level by thresholds. According to the experiments, TVPModel and TSPModel in this paper got satisfied accuracy compared with other prediction models.
Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publicationICAI 2019
Place of PublicationSingapore
Number of pages14
ISBN (Electronic)9789813292987
ISBN (Print)9789813292970
Publication statusPublished - 2019

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


  • Short-term traffic congestion prediction
  • Traffic data
  • Deep learning
  • Stacked autoencoder


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