Accuracy enhancement of roadway anomaly localization using connected vehicles

Bridgelall, Raj; Tolliver, Denver

doi:10.1080/10298436.2016.1162306

Cited by 8 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be done with the help of the future development of sensor technology. Lastly, the intensive on-going research on RIF and TWIT [95][96][97][98][99][100][101] as the alternatives for IRI in connected vehicle environment will be promising for large-scale implementation.…”

Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

“…Regarding new roughness index, a speed-independent road impact factor -RIF (individual vehicle) and its corresponding time-wavelength-intensity-transform -TWIT (vehicle groups) for connected vehicles were established using advanced signal processing in [94]. Further studies were conducted intensively to investigate and validate the RIF regarding sampling rate selection [95], localisation [96,97], RIF-IRI proportionality [98], deterioration forecasts in consideration of suspension parameter variances [99], stop-andgo conditions [100], and wavelength sensitivity [101].…”

Section: Signal Processingmentioning

confidence: 99%

See 1 more Smart Citation

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Purpose: With the development of smart technologies, Internet of Things and inexpensive onboard sensors, many response-based methods to evaluate road surface conditions have emerged in the recent decade. Various techniques and systems have been developed to measure road profiles and detect road anomalies for multiple purposes such as expedient maintenance of pavements and adaptive control of vehicle dynamics to improve ride comfort and ride handling. A holistic review of studies into modern response-based techniques for road pavement applications is found to be lacking. Herein, the focus of this article is threefold: to provide an overview of the stateof-the-art response-based methods, to highlight key differences between methods and thereby to propose key focus areas for future research. Methods: Available articles regarding response-based methods to measure road surface condition were collected mainly from "Scopus" database and partially from "Google Scholar". The search period is limited to the recent 15 years. Among the 130 reviewed documents, 37% are for road profile reconstruction, 39% for pothole detection and the remaining 24% for roughness index estimation. Results: The results show that machine-learning techniques/data-driven methods have been used intensively with promising results but the disadvantages on data dependence have limited its application in some instances as compared to analytical/data processing methods. Recent algorithms to reconstruct/estimate road profiles are based mainly on passive suspension and quarter-vehicle-model, utilise fewer key parameters, being independent on speed variation and less computation for real-time/online applications. On the other hand, algorithms for pothole detection and road roughness index estimation are increasingly focusing on GPS accuracy, data aggregation and crowdsourcing platform for large-scale application. However, a novel and comprehensive system that is comparable to existing International Roughness Index and conventional Pavement Management System is still lacking.

show abstract

Section: Discussion Conclusion and Outlookmentioning

confidence: 99%

Section: Signal Processingmentioning

confidence: 99%

Response-based methods to measure road surface irregularity: a state-of-the-art review

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…The tradeoff is spatial resolution in localizing roughness along the route, for example, when mapping the location of potholes and other roadway irregularities using a linear referencing system. To focus this research on evaluating the consistency and distinguishability of the CRI, this work does not elaborate on the localization capabilities inherited from the RIF-transform but instead points to previous work that did so [40].…”

Section: Feature Extractionmentioning

confidence: 99%

Characterizing Ride Quality With a Composite Roughness Index

Bridgelall

2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

View full text Add to dashboard Cite

There are many important applications that require ride quality characterization. However, the only international standard that specifies a roughness index is not suitable for applications beyond assessing the ride quality of paved roads. Other potential applications include automated ride quality characterization of gravel roads, bike or wheelchair paths, railways, rivers, airways, hyperloops, and elevator channels. This work proposes a composite index that characterizes roughness from multidimensional movements along any path. Statistical tests demonstrate two important properties-that the index is consistent based on an ever-decreasing margin-of-error of the mean, and distinguishable among different paths. A low-cost sensor package of accelerometers, gyroscopes, and a speedometer produced the data for spatio-temporal transformation. The experiments conducted on buses revealed that both the consistency and distinguishability of the index improves with the number of measurements. The approach is best suited for applications that can use in-situ sensors or crowdsensing to automate ride quality characterization.

show abstract

“…The goal of this study is to advocate for the widespread use of connected vehicles (CVs) to automate road and rail condition monitoring. The author undertook comprehensive research and writing, including a doctoral dissertation on the topic in 2015 [2]. Despite this, industry adoption of the approach lags and there have been no standards developed to prescribe its use in CVs.…”

Section: Introductionmentioning

confidence: 99%

Driving Standardization in Infrastructure Monitoring: A Role for Connected Vehicles

Bridgelall

2023

Vehicles

View full text Add to dashboard Cite

This study tackles the urgent need for efficient condition monitoring of road and rail infrastructure, which is integral to a nation’s economic vitality. Traditional methods proved both costly and inadequate, resulting in network gaps and accelerated infrastructure decay. Employing connected vehicles with integrated sensors and cloud computing capabilities can provide a cost-effective, sustainable solution for comprehensive infrastructure monitoring. In advocating for international standardization, this study furnishes compelling evidence—encompassing trends in transportation, economics, and patent landscapes—that underscores the necessity and advantages of such standards. The analysis confirmed that trucks and rail will remain dominant in freight transport as infrastructure limitations intensify. A noteworthy finding is the absence of patented solutions in this domain, which simplifies the path toward global standardization. By integrating data from diverse sources, agencies can optimize maintenance triggers and allocate funds more strategically, thus preserving vital transportation networks. These insights not only offer an effective alternative to current practices but also have the potential to influence policymaking and industry standards for infrastructure monitoring.

show abstract

Accuracy enhancement of roadway anomaly localization using connected vehicles

Cited by 8 publications

References 16 publications

Response-based methods to measure road surface irregularity: a state-of-the-art review

Response-based methods to measure road surface irregularity: a state-of-the-art review

Characterizing Ride Quality With a Composite Roughness Index

Driving Standardization in Infrastructure Monitoring: A Role for Connected Vehicles

Contact Info

Product

Resources

About