Street Quality Mapper (SQM)

eYantra Ideas Competition, IIT Bombay, April 2019
Read the research paper.

Technologies

Overview

Street Quality Mapper (SQM) is a system designed to identify potholes and map street quality in real-time. The system employs vehicle-mounted sensors to collect data, which is then processed to identify road abnormalities and quantify road quality. SQM offers a cost-effective alternative to manual road inspections providing a comprehensive map of a city's street surface quality and enabling authorities to monitor road conditions, make informed decisions, and improve street quality.

Features

• Real-time pothole detection: Utilizes a 6-axis accelerometer and GPS module to detect potholes based on acceleration fluctuations.
• Street quality assessment: Streets are color-coded based on road quality, with potholes plotted on the map for easy visualization.
• Ride Quality Score: Calculates a 'Ride Quality Score' based on acceleration data, helping to classify road conditions.
• Web Interface: Provides a user-friendly interface with color-coded maps and graphical representations of street quality data.

Implementation

Hardware Components

• NodeMCU with Accelerometer (MPU6050): Collects acceleration data and sends it to the cloud via a WiFi module.
• Raspberry Pi 3B+ with GPS Module (Neo6Mv2): Collects location data and transmits it to the cloud for further processing.

Software Components

• Data Processing: Data collected from the sensors is cleaned, synchronized, and processed to detect potholes using a robust peak detection algorithm based on z-scores.
• Ride Quality Calculation: The system calculates the Ride Quality Score using the formula:
  RideQualityScore = sqrt(x^2 + y^2 + z^2)
  where x, y, and z are the acceleration values along the three axes.
• Web Interface: Displays color-coded maps and provides detailed information on potholes, including timestamps and GPS coordinates.

How It Works

1. Data Collection
Accelerometer data is collected using MPU6050 sensors placed near the axles of the vehicle. GPS data is collected using the Neo6Mv2 module connected to a Raspberry Pi 3B+. Data is sent to a cloud platform (Firebase) in real time.


Data Collection framework of Street Quality Mapper system.


2. Data Processing
Collected data is cleaned and synchronized. A robust peak detection algorithm is applied to identify potholes. Ride Quality Score is calculated based on acceleration values.
3. Visualization
Streets are color-coded on maps based on their quality. Potholes are plotted on street maps with specific locations. Graphical representations show the number and intensity of potholes.

Installation and Setup

• Hardware Setup: Attach the accelerometer near the axle of the vehicle's tires and the GPS module in a secure location connected to the Raspberry Pi 3B+.
• Software Setup:
  • Install the necessary Python libraries (pip install -r requirements.txt).
  • Set up Firebase for real-time data storage.
  • Deploy the web interface on a server.
• Connectivity: Ensure a stable internet connection for real-time data transmission to the cloud.
• API Keys: Add your Google Maps API keys in place of {GOOGLE_MAPS_API_KEY} in relevant HTML files.

Usage

• Data Collection: The system automatically collects and sends data to the cloud as the vehicle moves.
• Pothole Detection: The system processes the data in real time, detects potholes, and updates the maps accordingly.
• Web Interface: Access the web interface to view the mapped potholes, color-coded street quality, and detailed reports.

Results

The Street Quality Mapper provides:

Pothole List

A list of all the potholes recorded

A list of all the potholes recorded along with their time of detection, location, ride quality score, and intensity.

Color-Coded Maps

Indicating the intensity of potholes with the following color codes:

• Red: High Priority
• Orange: Medium Priority
• Yellow: Lower-Medium Priority
• Green: Lower Priority

Map showing multiple color-coded patches of roads.

Pothole Locations

Marked on the map with details like GPS coordinates and timestamps.

Map pinpointing locations of potholes in satellite view.

Cumulative Analysis

Graphical representation of the number and intensity of potholes over time.

Accelerometer readings with timestamps. These are used to identify occurrences of potholes.

Ride Quality Score (RQS) computed according to the accelerometer readings. The higher the RQS, more is the intensity of the pothole.

Number of potholes identified grouped according to their intensity.

Future Work

• Integration with Transport Services: Expand data collection by collaborating with services like Ola and Uber.
• Mobile Sensors: Replace hardware components with mobile sensors for easier implementation.
• Machine Learning Integration: Use machine learning algorithms to improve pothole detection accuracy.
• Community Access: Make the data publicly accessible to allow community monitoring.