The Citizen Engineer: Urban Infrastructure Monitoring via Crowd-Sourced Data Analytics

Abstract

In the US, the challenges of an aging infrastructure network, coupled with requirements for maintaining continuous functionality of this network, highlights the need for innovative, and multidisciplinary solutions aimed at the timely detection and remediation of defects and deterioration before serious failure situations materialize. Considering the constant and widespread interactions of citizens with urban infrastructure systems, and the increasing ubiquity of mobile and personal electronic devices equipped with onboard sensing capabilities (e.g. camera, accelerometers, GPS, etc.), the concept of leveraging crowd-sourcing provides a promising data-driven solution for urban infrastructure monitoring. In this approach, the vision of the "citizen engineer" is introduced by empowering citizens to become "active human sensors" at the source of defect detection and data collection, thus extending the role of citizens from passive infrastructure users to active infrastructure monitors. In the proposed method, volunteers are motivated and instructed to use mobile devices to capture and send geo-tagged images of defects (e.g. cracks, corrosion, trip/slip hazards, potholes, etc.) that they observe in an urban infrastructure environment, including a short description and severity rating. The collected photos and descriptions are processed using object recognition techniques. Defects are identified and extracted from the photos and quantified, whereas additional information about the defects and their perceived severity are obtained from the description field. Beyond the local condition measures, the aggregate data provides responsible authorities with a quantitative analysis of the detected defects as well as a measure of importance and severity (i.e. heat maps), as perceived by the citizen, that can be used to inform maintenance decisions. While the challenges of this framework are discussed in detail, it is expected to be a highly promising departure from the traditional top-down infrastructure monitoring approaches.

Mobile crowdsourcing-based data collection for user-centered facility maintenance management

“…of the motivation methods (Harris et al 2017). The results of the survey carried out in the present study revealed that 77.8% of facility management personnel were opposed to offering incentives to the volunteer participants.…”

A crowdsourcing-based methodology using smartphones for bridge health monitoring

“…To overcome these issues, a new path that utilizes crowdsourcing data has attracted increasing interests from researchers in recent years. [9][10][11] Owing to the development of wireless technologies and Internet of things, almost everyone has at least one smart device which is able to connect to the Internet. These smart devices are usually equipped with various sensors, such as accelerometer, camera, gyroscope, and GPS.…”

Road infrastructure design towards passenger ride comfort for autonomous public transport

“…These categories are: geometry in the distance domain, geometry in the spectral domain, the effect in distance domain; and geometry and effect in the distance domain. Among them, the International Roughness Index (IRI) proposed by Sayers in [61] is considered as a standard index for road roughness evaluation. IRI was implemented in several standards such as [62] and [63] and has been recognised worldwide [64].…”

“…Figure 2-3: IRI model by a 2-DOF QCS [61] As for the bus, the relationship between road unevenness/vibration and perceived comfort has been the subject of studies over the years [65]. There are many factors affecting passenger perceived comfort, both objective and subjective, as summarised in Figure 2-4.…”

“…The three approaches in PBPM has been classified as: connected vehicle approach (uses OEM-installed accelerometers, sensor hardware and standardised onboard vehicle) fleet vehicle approach (uses semi-permanent, non-stock accelerometers in a fleet of agency-owned vehicles, supplemented by GPS units) and mobile device approach (uses accelerometer-equipped mobile devices to gather and transmit roughness information to a central database) [9]. The latter approach using smartphone sensors as the concept of "citizen as sensors" in [60] or "citizen engineer" [61] has received much research interest in recent years, followed by the connected vehicle and the fleet vehicle (which can be grouped into one dedicated sensor onboard approach). Regardless of applications, the methodology can be classified into three groups: acceleration thresholds or threshold-based methods, signal processing and machinelearning techniques.…”