Monitoring Road Surface Conditions with Cyclist's Smartphone Sensors

Abstract

Road networks form one of the most important infrastructures in modern cities, while road conditions determine the very possibility and quality of land transportation. It is therefore important to monitor and manage road networks properly. The vast area that should be monitored and managed makes this task both expensive and timeconsuming. Recently, an approach to involve road users, such as car drivers, pedestrians, and cyclists, to participate in monitoring road conditions has emerged. Monitoring roads using bicycles has an advantage, compared to using a car, since it allows for reaching narrow roads. This paper presents results of a preliminary study of using a bicycle for detecting road surface defects including potholes, and bumps. Data collected with a cyclist’s smartphone sensors was used to train artificial neural networks in different configurations. The trained networks were then used to detect road surface defects. Results obtained in the experiments indicate that for the accelerometer data, a convolutional neural network provides for the best average accuracy in classifying road surface conditions. Also, this and a long short term memory network produce better results than a standard deep neural network.