Case Studies in Construction Materials (Jun 2022)
Urban road pavements monitoring and assessment using bike and e-scooter as probe vehicles
Abstract
Comfort and safe-mobility are two aspects that should be provided to non-motorized users that are an increasing component of the urban micro-mobility (e.g. bikes and e-scooters). For these road users, road pavement unevenness, cracks, potholes and other surface defects can make riding uncomfortable and potentially hazardous. Traditionally, road managers use standardized surveys and Key Performance Indicators (KPIs) of pavement to make decision for maintenance programs and pavement management of road urban network poses challenges in the survey of pavement surface for International Roughness Index (IRI) and distress assessment both for operational and cost constrains. Moreover, there are theoretically limitations of IRI model for low speed urban roads and low-damped vehicles like bikes and e-scooters. In such framework aims of the paper is to investigate the use of smartphone sensors to collect data for the assessment of pavement conditions and definition of KPIs for bike and e-scooter users’ ride comfort and safety. A controlled experiment was performed with repeated runs of a bicycle and e-scooter equipped with a smartphone and an android application was used to collect acceleration and position data. Detailed pavement conditions have been identified with an advanced survey equipment. After data treatments for removing signal noise, adjustment for speed variability and outlier detection, root mean square (RMS) of vertical acceleration signal and weighted frequency content of the vibrations according to ISO 2631–1, have been confirmed suitable KPIs of pavement conditions and comfort rating that can be collected by bikes and e-scooter. Results confirmed the lack of correlation of vibrations in bikes and e-scooter with analogous parameters collected with car as probe vehicle and with IRI standard values, as well. Instead, pavement monitoring by bikes and e-scooter can provide effective detection of typologies and severities of distresses not detectable by similar approaches in damped vehicles. Good correlations have been identified between RMS and medium severity alligator, longitudinal and transversal cracks. High severity pavement distress and potholes have been identified by outliers in RMS.
