Using Slightly Imbalanced Binary Classification to Predict the Efficiency of Winter Road Maintenance

Mahshid Hatamzad,Johan Casselgren,Geanette Polanco Pinerez

doi:10.1109/access.2021.3131702

Abstract

The prediction of efficiency scores for winter road maintenance (WRM) is a challenging and serious issue in countries with cold climates. While effective and efficient WRM is a key contributor to maximizing road transportation safety and minimizing costs and environmental impacts, it has not yet been included in intelligent prediction methods. Therefore, this study aims to design a WRM efficiency classification prediction model that combines data envelopment analysis and machine learning techniques to improve decision support systems for decision-making units. The proposed methodology consists of six stages and starts with road selection. Real data are obtained by observing road conditions in equal time intervals via road weather information systems, optical sensors, and road-mounted sensors. Then, data preprocessing is performed, and efficiency scores are calculated with the data envelopment analysis method to classify the decision-making units into efficient and inefficient classes. Next, the WRM efficiency classes are considered targets for machine learning classification algorithms, and the dataset is split into training and test datasets. A slightly imbalanced binary classification case is encountered since the distributions of inefficient and efficient classes in the training dataset are unequal, with a low ratio between classes. The proposed methodology includes a comparison of different machine learning classification techniques. The graphical and numerical results indicate that the combination of a support vector machine and genetic algorithm yields the best generalization performance. The results include analyzing the variables that affect the WRM and using efficiency classes to drive future insights to improve the process of decision-making.

Highlights

winter road maintenance (WRM) prediction model enables us to discover the relationship between input and output variables and leads to an improved decision-making process
Several machine learning (ML) algorithms were implemented in Python software and compared based on real data collected at a road weather station and by optical and road-mounted sensors on European road E18 in Sweden
The results obtained in this study verify that the combined support vector machine (SVM)-genetic algorithm (GA) approach yields the best performance among the considered algorithms in discriminating to between efficient and inefficient WRM classes

Summary

Introduction

A. MOTIVATION In today’s modern world, the number of vehicles on roads has rapidly increased due to a surge in the population [1], resulting in an increased number of traffic accidents, especially in winter, when conditions making driving more difficult than in normal situations. Winter road maintenance (WRM) is important for improving traffic safety. There are two main types of WRM: anti-icing and deicing. Anti-icing maintenance prevents ice from forming on the road surface (such as by using salt), and deicing maintenance contributes to removing ice from the road surface (such as by plowing). WRM needs to be well planned, including preparing a sufficient number of trucks and truck drivers and using suitable salt quantities to provide drivers with safe roads on demand.

Objectives

Methods

Results

Conclusion