Python tensorflow is used to predict the congestion types based on image object recognition
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 with directory venv
virtualenv venv
3. Activate venv using:
source venv/bin/activate
4. Install tensorflow 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?
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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.