A Scalable Pavement Sensing, Data Analytics, and Visualization Platform for Lean Governance in Smart Communities

Nguyêñ, Phong Q.; Rao, Raghav; Brown, V. W.; McConnell, Matthew; Barendt, Nicholas A.; Zingale, Nicholas C.; Mandal, Soumyajit; Kaffashi, Farhad; Loparo, Kenneth A.

doi:10.1109/mercon50084.2020.9185283

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…In recent research, the authors proposed a scalable platform for pavement sensing, data analytics, and visualization [24]. Their approach involved utilizing an embedded system to collect road condition data, while employing the DTW algorithm for the detection of road anomalies such as potholes, cracks, and bumps.…”

Section: B Dtw Approachmentioning

confidence: 99%

“…For instance, road roughness increases energy loss caused by pavement-vehicle interaction (PVI). Implementing roadway remediation solutions that reduce such losses by 50% would improve vehicle fuel economy by 2%, resulting in substantial reductions in energy consumption and greenhouse gas emissions [24]. Overall, the potential of crowdsensing for RPCM has gained attention due to its ability to harness various technologies and provide cost-effective solutions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Jan,

Khattak,

Khan

et al. 2023

IEEE Access

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RPCM forms a crucial element of preventive maintenance strategies, particularly in light of escalating vehicular pressures and the advent of extreme weather patterns. Consequently, there is a growing demand for cost-effective solutions that leverage emergent technologies such as the IoT, AI, and cloud computing. This research intends to articulate the evolutionary trajectory of road solutions while delineating the prevalent challenges and offering viable trajectories for future enhancements. To achieve this objective, a systematic literature review was executed using the Scopus and Web of Science databases, the aim of which was to discern the inherent challenges of existing solutions. Following a stringent elimination process of duplicates and irrelevant studies, a corpus of 74 research papers was assembled for review. Assessment criteria encompassed the sensing platforms and algorithms deployed, the variety of road deformities detected, and the overall accuracy of the proposed solutions. The analysis revealed a variety of methodologies applied to RPCM, each bearing distinct advantages and limitations. Notably, SPbased monitoring solutions utilizing ML techniques and improved data gathering methodologies exhibited superior outcomes relative to alternative approaches. To conclude, this research elucidates the wide-ranging methodologies in RPCM, critically examining their respective advantages and drawbacks. Among the methodologies surveyed, SP-based monitoring deploying ML techniques emerges as a compelling approach, demonstrating the potential for enhancing accuracy and data gathering techniques. These insights form a valuable foundation for the conception and development of future cost-effective and efficacious RPCM solutions.

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Section: B Dtw Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Jan,

Khattak,

Khan

et al. 2023

IEEE Access

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“…At the same time, pavement images were obtained by a camera mounted on the car, which were then used as a ground truth for the estimation model. Nguyen et al [35] developed a system that also collected acceleration data through an IoT device placed outside the vehicle, and which also integrated a gyroscope and GPS signal receivers. The recorded variations of vibration were then used in a visualization tool that represented the quality of the network surface.…”

Section: Smart Probe Vehiclesmentioning

confidence: 99%

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

Amândio¹,

Parente²,

Neves

et al. 2021

Buildings

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Nowadays, pavement management systems (PMS) are mainly based on monitoring processes that have been established for a long time, and strongly depend on acquired experience. However, with the emergence of smart technologies, such as internet of things and artificial intelligence, PMS could be improved by applying these new smart technologies to their decision support systems, not just by updating their data collection methodologies, but also their data analysis tools. The application of these smart technologies to the field of pavement monitoring and condition evaluation will undoubtedly contribute to more efficient, less costly, safer, and environmentally friendly methodologies. Thus, the main drive of the present work is to provide insight for the development of future decision support systems for smart pavement management by conducting a systematic literature review of the developed works that apply smart technologies to this field. The conclusions drawn from the analysis allowed for the identification of a series of future direction recommendations for researchers. In fact, future PMS should tend to be capable of collecting and analyzing data at different levels, both externally at the surface or inside the pavement, as well as to detect and predict all types of functional and structural flaws and defects.

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A Scalable Pavement Sensing, Data Analytics, and Visualization Platform for Lean Governance in Smart Communities

Cited by 2 publications

References 9 publications

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Crowdsensing for Road Pavement Condition Monitoring: Trends, Limitations, and Opportunities

Integration of Smart Pavement Data with Decision Support Systems: A Systematic Review

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