Traffic Image Realtime prediction!

Let’s start by looking into the data,

Data Selection

This week, I chose Singapore LTA realtime data from CCTV cameras available online,


I started by exploring the data, and looking for potential applications. Then I found that We can measure the congestion of each location using object detection since the data are associated with spatial location of each camera.

Problem definition

  • Build an interface that shows the road congestion in Singpaore using the available open dataset from CCTV roads.
  • Visualize the data on a live platform.

Model selection

I have done a quick research on the best models that can be used to detect objects with high accuracy, and eventually, I chose YoloV5.


  • Visualization:
  • Python 3.11
  • Pytorch
  • VsCode
  • Firebase.


First, I used Pytorch Yolov8 pretrained model to build my model on cars using Udacity self driving cars dataset availble here.


  • How to replicate:
    • Create an account on roboflow,
    • Goto roboflow Universe
    • Search for the car detecting and how many model,
    • Click download the model
    • Choose the model YoloV8.
    • Copy/paste the code snippet.
    • Now, you are ready to download the trained model.

Building the pipeline

Deployment on GCP

Live preview

Mahmoud A.
Mahmoud A.
Postdoc Research Fellow at the Department of Architecture (NUS)

My research interests include data sceince in the built environemnt, human-building interaction, and graph neural networks.