Research Article Design and Development of Weigh-In-Motion Using Vehicular Telematics Sivaramalingam Kirushanth and Boniface Kabaso Department of Information Technology, Faculty of Informatics and Design, Cape Peninsula University of Technology, Cape Town 8000, South Africa Correspondence should be addressed to Sivaramalingam Kirushanth; sivaramalingamk@vau.jfn.ac.lk Received 24 July 2019; Revised 7 December 2019; Accepted 26 December 2019; Published 4 March 2020 Academic Editor: Xinyu Liu Copyright © 2020 Sivaramalingam Kirushanth and Boniface Kabaso. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Identifying overloaded vehicles on a highway is essential for the safety of vehicles on the road as well as for the performance monitoring of highway infrastructure and planning. Traffic enforcement uses various weigh-in-motion (WIM) methods. Since Vehicular Telematics (VT) is favoured in the transport industry, using it for building a new WIM system to infer the payload of a vehicle at any road segment would be beneficial for the transport industry. This paper presents the effort taken to use VT data from onboard diagnostics modules and smartphones to infer the payload of a vehicle. The experiment done to find the correlation between VT data and the payload of a vehicle is discussed. Feature engineering was done; nine different settings were tested to find the best regression model. A multiple nonlinear regression model produced significant a p value of 6.322e-08 and an R-squared value of 0.8736. Results support the notion of using the VT data for nonintrusive measurement of the weight of a vehicle in motion. 1. Introduction Road safety is one of the most significant issues in the world [1]. Driving an overloaded vehicle causes various kinds of hazards such as mechanical failures and structural deforma- tion of vehicles and roads, which lead to accidents, and it is an illegal and punishable offence in most countries. Accord- ing to the South African National Road Traffic Regulations, driving an overloaded vehicle leads to prosecution for an offence under regulations in the National Road Traffic Act, 1996 [2]. According to the U.S. Department of Transporta- tion, vehicle condition and road/environment conditions are the two factors which are collectively responsible for 5.2% of road accidents [3]. Vehicles carrying more than the manufacturer’s specified and permitted payload are consid- ered overloaded. In other words, a vehicle is overloaded if the total weight of a vehicle when fully loaded is more than the maximum allowed Gross Vehicle Weight (GVW), where the GVW is the sum of kerb weight and payload [4]. Weighing the weight of a moving vehicle on the road is known as weigh-in-motion (WIM). Fred Moses and George Globe introduced Bridge WIM (B-WIM) in the USA in the early 1970s. The successful B-WIM application took place in Australia in the mid-1980s [5]. WIM has been used in the transport industry for more than a decade and for many reasons. Earlier, it was only used to plan and build the roads and bridges. In recent years, the legislation has been changed, and the WIM data is also used by traffic enforcement depart- ments for the enforcement of overloading. Identifying an overloaded vehicle driving on any road is still a tough task for enforcement officials. In many countries, the high-speed WIM (HS-WIM) is used to detect the overloaded vehicle on the road; the selected vehicles are then screened on a static WIM to obtain more accurate weight. The present HS-WIMs have the accuracy of 5%-15% due to various internal and external disturbing factors [6]. The vehicle industry has used Vehicular Telematics (VT) for more than a decade for various reasons. Pay As You Drive (PAYD) or User-Based Driving Insurance (UBI) is the most popular insurance schemes used by vehicle insurance compa- nies all over the world. On-board devices are installed on the user vehicles to collect driving information as they drive. Hindawi Journal of Sensors Volume 2020, Article ID 7871215, 22 pages https://doi.org/10.1155/2020/7871215