# TensorflowPredictCongestionTypes

**Repository Path**: lionellee/TensorflowPredictCongestionTypes

## Basic Information

- **Project Name**: TensorflowPredictCongestionTypes
- **Description**: Python tensorflow is used to predict trafffic congestion types based on image object recognition
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-31
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Using Tensorflow to Predict Traffic Congestion Types.
Python tensorflow is used to predict the congestion types based on image object recognition
 
<p align="center"> 
 <img width="200" height="200" src="https://github.com/taiwotman/TensorflowPredictCongestionTypes/blob/master/miscellanous/smart-traffic.png"></p>
 <p align="center">
<img width="200" height="200" src="https://github.com/taiwotman/TensorflowPredictCongestionTypes/blob/master/miscellanous/high-congestion.png">
 <img width="200" height="200" src="https://github.com/taiwotman/TensorflowPredictCongestionTypes/blob/master/miscellanous/medium-congestion.png">
 <img width="200" height="200" src="https://github.com/taiwotman/TensorflowPredictCongestionTypes/blob/master/miscellanous/low-congestion.png">
</p>

**HOW TO TEST**

**1. Clone git repository and `cd` into the directory**

     git clone https://github.com/taiwotman/TensorflowPredictCongestionTypes.git
     
     cd TensorflowPredictCongestionTypes

**2. Set up [virtualenv](https://virtualenv.pypa.io/en/stable/) with directory _venv_** 

     virtualenv venv

**3. Activate _venv_ using:**

     source venv/bin/activate

**4. Install [tensorflow](https://www.tensorflow.org) using:**

     pip install tensorflow

**5. Use  traffic congestion image(supports only jpeg/jpg format). For example:**

     python run.py test_image/Aut10_010.jpg
     
**6. Example output:**

      high congestion (score = 0.70454)
      
**Docker:** 

https://hub.docker.com/r/taiwotman/smart-traffic

Mobility as a Service(MaaS) Application for real-time traffic prediction


**You want to be a contributor or implement your own real-time prediction?** 
1. Fork repository

     and/or

2. Connect and chat me on [LinkedIn](https://www.linkedin.com/in/taiwo-o-adetiloye-ph-d-505a8023/).

**FOR ACADEMIC PURPOSE; 
kindly, cite our related work:**
     
     T. Adetiloye, A. Awasthi  (2019). Multimodal Big Data Fusion for Traffic Congestion Prediction. 
         In: Seng K., Ang L., Liew   AC., Gao J. (eds) Multimodal Analytics for Next-Generation Big Data Technologies 
         and Applications(pg. 319-335). doi: https://doi.org/10.1007/978-3-319-97598-6_13. Springer, Cham.
     
     
     T. Adetiloye, A. Awasthi(2017). Predicting Short-Term Congested Traffic Flow on Urban Motorway Networks. 
          In P. Samui, S.S Roy, V.E. Balas(Eds.), Handbook of Neural Computation(pg. 145–165).
          doi: https://doi.org/10.1016/B978-0-12-811318-9.00008-9 . Academic Press.