Week 7 - Oct 16 - 22


Having set up everything to kickstart the project, this blog will cover up my further progress in moving the device (Nucleo-L476RG kit plus Case) "Into the traffic". This will be the third blog covering module 1. A glance over the previous week's blog Traffic Predictor #6 - Into the traffic [Part 1 of 2] will help you in understanding trafficpredictor project progress better.

Module 1 - Traffic prediction with machine learning on an added advantage of mass storage capability of the STM32 Nucleo-64 development board

This module 1 title has been revised as above to "Traffic prediction with machine learning". Please note in future I will be referring module 1 with the revised title.


To be covered later/ on this blog,


App (Android)

Code Snippets


Explored Kit's Storage Capacity


New Website for my trafficpredictor project - Home

Official Website

App (Android)

The difficulties I had faced and the workarounds used while developing the android app for my project is explained in my previous blog Traffic Predictor #6 - Into the traffic [Part 1 of 2] The app is in its initial stage and the code snippets of the app will be posted in the next blog once completed. The screenshots of the app are given in the gallery below. The app has the following menu,

  1. Home - The homepage will include the login page for each user. This will help in aggregating data and personalizing output for each user. Now it includes a simple text "Welcome!".
  2. Maps - This is the main page of module 1 which will provide the user with the functionality of navigating to his destination. It will involve recording data of latitude, longitude and time of travel. Then these data will be processed to provide the user with a suggestion of the best route.
  3. Autopilot - This will be an option for module 2 & 3 which will be discussed in later blogs.
  4. About -This is a simple page describing the app.
  5. Connect Device - An option which provides user connection with the device (Nucleo-L476RG kit) via either of the following
    • Bluetooth
    • Wi-Fi


{gallery} Android App Screenshots


Image 1: Home


Image 2: Menu

Zoomed View

Image 3: Zoomed map

Map with marked location

Image 4: Map with marked location

App with icon

Image 5: App Icon



Sneak peak of the MATLAB GUI was given in the blog - Traffic Predictor #5 - Machine Learning and Building a case for the kit. Since MATLAB program has to be deployed in a server, a GUI will not be needed. But for a developer, an optional GUI to study the patterns of traffic will be essential. To facilitate this, changes are being made to the existing machine learning GUI already developed and make it suitable for learning traffic-related data.


[The completed version of the MATLAB GUI will be posted soon]


Explored kit's mass Storage Capacity

I have been misled by the term "mass storage". Actually, it has referred to a set of computing communications protocols that would make a USB device accessible to the host computing device and enable file transfers between the host and the USB device. Little less I was aware that it referred to the below and I conceptualized it to be a storage with more space.

Reference of the above: Nucleo-L476RG from ST and Wiki


Before this reality was understood, am happy that I had prepared the method of data storage and retrieval which would be over the web/ remote through Google spreadsheets This will not involve storing data either in the Nucleo-L476RG board or user's mobile device




Progress made so far,

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{tabbedtable} Tab LabelTab Content
Entry & Introduction

IoT on Wheels Design Challenge - Traffic predictor and auto pilot mode

Traffic Predictor #1 - The Official Announcement

PlanTraffic Predictor #3 - The Plan
Initial Setup

Traffic Predictor #2 - Quest for the Code Editor

Traffic Predictor #4 - Finally, the kit arrives

Module 1

Traffic Predictor #5 - Machine Learning and Building a case for the kit

Traffic Predictor #6 - Into the traffic [Part 1 of 2]

Module 2

Yet to Begin

Module 3Yet to Begin
IntegrationYet to Begin