Smartphone-enabled road condition monitoring: from accelerations to road roughness and excess energy dissipation

Abstract

We develop a framework to address the shortcomings of current smartphone-based approaches for road roughness sensing and monitoring through combining vehicle dynamics, random vibration theory and a two-layer inverse analysis. The proposed approach uses in-cabin recordings of the vehicle’s vertical acceleration measured by a smartphone positioned inside the car for the estimation of road roughness. The mechanistic road roughness–vehicle interaction model at the core of the proposed framework links the frequency spectrum of the vehicle’s vertical acceleration to the road roughness power spectral density and lends itself to the quantitative characterization of roughness-induced energy dissipation. We demonstrate that the measure of roughness provided by the stochastic model of car dynamics interacting with a rough road is fully compatible, in a statistical sense, with the spatial but deterministic definition of road roughness, and validate the identification strategy that originates from it against laser measurements of road roughness. The critical crowdsourcing features of the proposed framework, such as the marginal impact of phone position and transferability, are examined and its utility to meld with big data analytics to identify the class of vehicles travelling on a roadway network is demonstrated.

Industry- and Academic-Based Trends in Pavement Roughness Inspection Technologies over the Past Five Decades: A Critical Review

“…However, Hanson et al [94] reported a limited impact of speed change from 50 to 80 km/h in their study. Additionally, Botshekan et al [97] reported a lower impact of speed variation for larger-scale surveys.…”

“…For example, Hanson et al [94] reported that the difference between the measured IRI and the reference value varied from 5.4% to −11.9% when measured using a compact car (2001 Pontiac Sunfire) and a sport utility vehicle (2011 Nissan Rogue), respectively. However, Botshekan et al [97] indicated that well-trained machine learning models could be used for different types of vehicles. Additionally, Jeong et al [102] suggested that the use of gyration (i.e., angular velocity) can minimize the effect of vehicle model variation.…”

“…However, Bisconsini et al [95] concluded a limited impact of the mounting arrangement. In general, a rigid mounting arrangement is preferable to acquire the chassis vibration as accurately as possible [95,97].…”

“…Table 16 presents details of some studies that used acceleration data collected using smartphones and connected vehicles as a crowdsourcing tool to evaluate road roughness. Table 16 shows that most studies used smartphones as a crowdsourcing tool [57,85,90,97,99,104]. This is predictable as road users widely use smartphones, and customized software can be easily installed to collect and analyze the data.…”

“…However, two studies explored using connected vehicles as crowdsourcing tools to evaluate roughness data [45,54]. Additionally, the use of crowdsourcing is usually coupled with ML [45,97,104]. The majority of the studies presented colored maps to indicate the roughness level of road sections [57].…”