Traffic Flow Prediction using Time Series Forecasting

[ShaikArshidBanu]

Description:

Predicting future traffic flow, which will aid in traffic management and planning. The goal is to build a model that can accurately forecast traffic flow based on historical data and other influencing factors. This will help in reducing congestion, optimizing traffic light timings, and improving overall traffic management.

Implementing and comparing different time series forecasting models:

-ARIMA (AutoRegressive Integrated Moving Average): Suitable for capturing linear temporal dependencies. -LSTM (Long Short-Term Memory) networks: Effective for capturing long-term dependencies and non-linear patterns. -GRU (Gated Recurrent Unit) networks: Similar to LSTMs but with a simplified architecture.

Expected Outcomes:

-A robust and accurate traffic flow prediction model. -Improved traffic management and planning capabilities. -Reduced traffic congestion and optimized traffic light timings.

@Niketkumardheeryan I am GSSOC'24 contributor & would love to work on this issue, can you please assign this issue to me.