Drive‐by Systems for 24/7 Road and Rail Infrastructure Monitoring

Abstract Drive‐by monitoring technologies offer a potentially transformative solution for enhancing transportation infrastructure monitoring and improving the enforcement of vehicle regulations across road and rail networks. Current monitoring of infrastructure is occasional, single point‐in‐time, and labour‐intensive. Current overload enforcement methods are similarly limited by a labour‐intensive, sporadic nature, which results in insufficient oversight of vehicle overloading and often poor compliance with legal limits. This paper proposes the use of accelerometers mounted on vehicles to (i) self‐weigh the vehicles and (ii) detect the surface profiles over which they pass. Vehicle self‐weighing, where the weights are shared with enforcement agencies, has great potential to improve compliance with legal weight limits. A bonus is that the surface profile provides valuable information on road/rail roughness and has potential for bridge monitoring. The on‐board accelerometer data can provide continuous, tamper‐proof weight monitoring by processing the acceleration signals to extract surface profiles and vehicle loads. For road vehicles, where vehicle weight enforcement is a challenge, this has the potential to monitor compliance with legal gross weight limits without disrupting traffic flow. In rail transport, drive‐by monitoring of the track and bridge stock has the potential to accommodate heavier train loads, hence reducing operational costs and the carbon footprint of freight. By equipping trains with sensors, the system continuously assesses track stiffness and condition as trains pass, minimizing the need for dedicated monitoring vehicles. The rail monitoring model incorporates stochastic variations in factors such as train weight, speed, and suspension. Through a form of Inverse Dynamics, it determines track stiffness beneath each sleeper, supporting dynamic stability assessments to minimise risks such as track deterioration and train overturning.