Main Reference PaperDeep learning for short-term traffic flow prediction, Transportation Research Part C, 2017.[Python]
  • The proposed method intended to forecast the traffic based on the deep learning method. It associates the linear model based on the L1 regularization and the tanh layers. The first layer in the deep learning architecture discovers the spatiotemporal relation and the remaining layer capture the non-linear functions.

Description
  • The proposed method intended to forecast the traffic based on the deep learning method. It associates the linear model based on the L1 regularization and the tanh layers. The first layer in the deep learning architecture discovers the spatiotemporal relation and the remaining layer capture the non-linear functions.

  • To develop the traffic flow prediction model based on the deep learning architecture

  • To improve the linear model

Aim & Objectives
  • To develop the traffic flow prediction model based on the deep learning architecture

  • To improve the linear model

  • The traffic flow prediction based on the multiple sources rather than a single source improves the prediction accuracy.

Contribution
  • The traffic flow prediction based on the multiple sources rather than a single source improves the prediction accuracy.

  • Operating system: Ubuntu / Windows

  • Java/Python/R

Software Tools & Technologies
  • Operating system: Ubuntu / Windows

  • Java/Python/R

  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

Project Recommended For
  • M.E / M.Tech/ MS / Ph.D.- Customized according to the client requirements.

  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

Order To Delivery
  • No Readymade Projects-project delivery Depending on the complexity of the project and requirements.

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