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A Real-Time Machine Learning-Based Road Safety Monitoring and Assessment System

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International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

Abstract

In this paper a system to monitor road conditions, detect unsafe driving behaviours and determine the influence of rainfall on traffic safety in real time using different machine learning algorithms, has been proposed. The system developed consists of a mobile application that captures car movement using its in-built accelerometer and gyroscope sensors and a server that monitors weather conditions at 16 key locations in Mauritius using the OpenWeather API. Road conditions, pothole, speed bumps as well as driving events were analysed using the K-Nearest Neighbour (KNN) and Multi-Layer Perceptron (MLP) algorithms. Moreover, a mathematical model, which incorporates the predicted rainfall in the estimation of braking distance and recommended speed, has been proposed. An average accuracy of 80.9% was obtained for pothole detection, 70% for speed bumps and 85.5% for unsafe driving behaviours detection. The proposed model with rainfall data predicted the braking distance and recommended speed with a Mean Absolute Percentage Error (MAPE) of 14.7% and 0.735% respectively.

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Abbreviations

API:

Application Programming Interface

CART:

Classification And Regression Tree

CNN:

Convolutional Neural Network

CSV:

Comma Separated Values

GBDT:

Gradient-Boosted Decision Tree

GPS:

Global Positioning System

GUI:

Graphical User Interface

HTML:

Hyper Text Markup Language

HTTP:

Hyper Text Transfer Protocol

IDE:

Integrated Development Environment

IP:

Internet protocol

JSON:

JavaScript Object Notation

KNN:

K Nearest Neighbours

LSTM:

Long Short-Term Memory Networks

MAPE:

Mean Absolute Percentage Error

MLP:

Multi-Layer Perceptron

MLR:

Multiple Linear Regression

MPR:

Multiple Polynomial Regression

SDK:

Software Development Kit

SQL:

Structured Query Language

SVM:

Support Vector Machine

UI:

User Interface

URL:

Uniform Resource Locator

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Acknowledgements

The authors are thankful to the University of Mauritius for providing the necessary facilities for conducting this research.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Mauritius, Réduit, Mauritius

    Tulsi Pawan Fowdur & Mohammed Fayez Hawseea

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  1. Tulsi Pawan Fowdur
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Correspondence to Tulsi Pawan Fowdur.

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Fowdur, T.P., Hawseea, M.F. A Real-Time Machine Learning-Based Road Safety Monitoring and Assessment System. Int. J. ITS Res. (2024). https://doi.org/10.1007/s13177-024-00395-3

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