BananAlhethlool / Traffic-Prediction-DeepLearning

Using Deep Learning algorithms to help predict traffic for smart cities.

Smart City: Traffic prediction using Deep Learning

By Banan Alhethlool | [e-mail]

Introduction:

Traffic prediction is very important for any urban city, especially in crowded areas. Predicting traffic can be very useful for managing future traffic. Therefore, the idea proposed for this project is to use deep learning algorithms to help us predict traffic at a certain time and location. The goal of the project is to help smart cities have a better understanding of traffic patterns that leads to better management of traffic and solves many problems related to infrastructure and safety.

Dataset

The dataset for this project consists of datetime, vehicles, and junction information. The data is collected from sensors at every junction. It has over 48, 000 observations and was obtained as an open-source from Kaggle, Here.

Tools

• Technologies: Jupyter Notebook, Python, SQL and SQLlite.
• Libraries: Pandas, Numpy, Matplot, Seaborn, Sklearn, Keras, and Tensorflow.