Vehicle Count Prediction

As part of our course project for CSN-382(Machine Learning) at IIT Roorkee, we implemented several existing machine learning and deep learning algorithms for Vehicle Count Prediction. We comprehensively compared and analysed our results using metrics like RMSE, MAE and MSE. We proposed a new model named JV2Former (based on our names initial letters) and it demonstrated performance more than 10X better wrt MAE with the next best performing model. In this repository we make available the code of all the models we tried out with timeframes of 1 day, 2 days etc as the windows for predicting the next observation. All of them are provided as Jupyter Notebooks for better visualization and interactive code.

Contributors:

Male Jithendra

Pasala Veerasiva

Vivek Narayan

Acknowledgement:

We would like to thank Prof. Raksha Sharma for giving us the opportunity to delve deep into various deep learning paradigms and explore. If you have any queries related to our work, please reach out to us here by raising an issue or mailing me at u_vnarayan@cs.iitr.ac.in. If this code is useful, you are more than welcome to go ahead and utilize it and I would love to hear back from you how it was useful.

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
original		original
past day		past day
README.md		README.md
TransAM.ipynb		TransAM.ipynb
Transam+LSTM+GRU.ipynb		Transam+LSTM+GRU.ipynb
adaboostjunction.ipynb		adaboostjunction.ipynb
adaboosttotal.ipynb		adaboosttotal.ipynb
arima.ipynb		arima.ipynb
catb-rf-scaling-total.ipynb		catb-rf-scaling-total.ipynb
catb-xgb-total.ipynb		catb-xgb-total.ipynb
catboostjunction.ipynb		catboostjunction.ipynb
catboosttotal.ipynb		catboosttotal.ipynb
knnregressionjunction.ipynb		knnregressionjunction.ipynb
knnregressortotal.ipynb		knnregressortotal.ipynb
lastm_junction.ipynb		lastm_junction.ipynb
past 7 obs.zip		past 7 obs.zip
past day.zip		past day.zip
randomforest-ml-project.ipynb		randomforest-ml-project.ipynb
randomforest-total.ipynb		randomforest-total.ipynb
randomforestjunction.ipynb		randomforestjunction.ipynb
rf+xgb_bagging.ipynb		rf+xgb_bagging.ipynb
rf_xgb-bagging.ipynb		rf_xgb-bagging.ipynb
simple-200nnjunction.ipynb		simple-200nnjunction.ipynb
simplennjunction.ipynb		simplennjunction.ipynb
temp.py		temp.py
transam-decoder-modified (1).ipynb		transam-decoder-modified (1).ipynb
transam-decoder-modified-causal-mask.ipynb		transam-decoder-modified-causal-mask.ipynb
transam-decoder-modified-lstm-gru.ipynb		transam-decoder-modified-lstm-gru.ipynb
transam-decoder-modified.ipynb		transam-decoder-modified.ipynb
transformer-gru lstm-2-days.ipynb		transformer-gru lstm-2-days.ipynb
transformer-gru-2-days best.ipynb		transformer-gru-2-days best.ipynb
transformer-gru-2-days.ipynb		transformer-gru-2-days.ipynb
transformer-lstm-2-days best.ipynb		transformer-lstm-2-days best.ipynb
transformer-lstm-2-days.ipynb		transformer-lstm-2-days.ipynb
transformer-lstm-2-days128.ipynb		transformer-lstm-2-days128.ipynb
xgb-rf-scaling-total.ipynb		xgb-rf-scaling-total.ipynb
xgboost-15junction.ipynb		xgboost-15junction.ipynb
xgbregressortotal.ipynb		xgbregressortotal.ipynb

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