Research Article TRR JOURNAL OF THE TRANSPORTATION RESEARCH BOARD Investigation on Identifying Road Anomalies using In-Vehicle Sensors for Cooperative Applications and Road Asset Management Moksheeth Padarthy 1 , Mohammed Sami 1 , and Emiliano Heyns 1 Abstract One of the main challenges for road authorities is to maintain the quality of the road infrastructure. Road anomalies can have a significant impact on traffic flow, the condition of vehicles, and the comfort of occupants of vehicles. Strategies such as pavement management systems use pavement evaluation vehicles that are equipped with state-of-the-art devices to assist road authorities in identifying and repairing these anomalies. The quantity of data available is limited, however, by the limited availability and, therefore, coverage of these vehicles. To address this problem, several investigations have been conducted on the use of smartphones or equipping vehicles with additional sensors to identify the presence of road anomalies. This paper aims to add to this arsenal by using sensors already available in production vehicles to identify road anomalies. If production vehicles could be used to identify road anomalies, then road authorities would be equipped with an additional fleet of mobile sensors (vehicles traveling on a particular road) to receive initial insights into the presence of anomalies. This information could then be used to assist road authorities to deploy their staff and equipment more precisely at these locations, such that appropriate equipment reaches the right place at the right time. In this paper, an algorithm that uses lateral acceleration and individual wheel speed signals, which are commonly available vehicular variables, was developed to detect potholes using machine learning techniques. The results of the algorithm were validated with real life test scenarios. Any obstacle or portion of the road that can hinder the smooth running of a vehicle, cause discomfort or harm to the passengers, or damage the vehicle is deemed a road anomaly. An anomaly can manifest in several forms, pri- marily, planned and unplanned. Unplanned anomalies can be attributed to conditions such as frequent loading and unloading of the road because of dynamic traffic conditions, poor drainage, variation in peak tempera- tures, and poor construction (1). These unplanned anom- alies manifest in the form of potholes, ruts, cracks, and so forth. The effects of these anomalies require the roads to be periodically maintained and upgraded. For this, the road and transport authorities need to be able to ascer- tain the type of anomalies and their location to manage their maintenance operations efficiently. Planned anom- alies, on the other hand, are pieces of roadside infrastruc- ture that have an intentional impact on the smooth running of a vehicle, for example, speed bumps and rumble strips. Road and transport authorities globally have now adopted pavement management systems (PMS) to iden- tify sections of road that require repair. The strategies of PMS can be broadly classified into evaluation strategies and surveillance strategies (2). Evaluation strategies use performance and deterioration models and life cycle eco- nomic methods to determine the maintenance needs of roads (2). In this method, common performance varia- bles such as cracking, stone loss, ride comfort, structural 1 HAN University of Applied Sciences, The Netherlands Corresponding Author: Moksheeth Padarthy, moksheeth.padarthy@han.nl Transportation Research Record 0(0) 1–11 ! National Academy of Sciences: Transportation Research Board 2020 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/0361198120923989 journals.sagepub.com/home/trr