Precision Bounds of Pavement Deterioration Forecasts from Connected Vehicles

Bridgelall, Raj

doi:10.1061/(asce)is.1943-555x.0000218

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…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%

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

Nguyen

Lechner

Wong

2019

Eur. Transp. Res. Rev.

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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.

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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.

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“…Alternatively, producing the EAR index from a selected speed band by sampling the traversal data from many vehicles will obviate the need for calibration to account for the VIFs of a specific vehicle. Previous studies of the precision bounds of the EAR index demonstrate that the margin of error, within a 95% confidence interval (MOE 0.95 ), diminishes rapidly after only several hours of data collection from the typical vehicle mix (22). The MOE 0.95 will diminish to equivalent levels of precision with fewer traversals when the same vehicle or vehicle type is used.…”

Section: Statistical Modelsmentioning

confidence: 96%

Use of Connected Vehicles to Characterize Ride Quality

Bridgelall

Rahman

Tolliver

et al. 2016

Transportation Research Record: Journal of the Transportation R

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The United States rely on the performance of more than four million miles of roadways to sustain its economic growth and to support the dynamic mobility needs of its growing population. The funding gap to build and maintain roadways is ever widening. Hence, the continuous deterioration of roads from weathering and usage poses significant challenges. Transportation agencies measure ride quality as the primary indicator of roadway performance. The international roughness index is the prevalent measure of ride quality that agencies use to assess and forecast maintenance needs. Most jurisdictions utilize a laser-based inertial profiler to produce the index. However, technical, practical, and budget constraints preclude their use for some facilities, particularly local and unpaved roads that make up more than 90% of the road network in the US. This study expands on previous work that developed a method to transform sensor data from many connected vehicles to characterize ride quality continuously, for all facility types, and at any speed. The case studies used a certified and calibrated inertial profiler to produce the international roughness index. A smartphone aboard the inertial profiler produced simultaneously the roughness index of the connected vehicle method. The results validate the direct proportionality relationship between the inertial profiler and connected vehicle methods within a margin-of-error that diminished below 5% and 2% after 30 and 80 traversal samples, respectively. Use of Connected Vehicles to Characterize Ride QualityRaj Bridgelall et al.Page 3/17 INTRODUCTIONRide quality refers to the degree that a vehicle protects its occupants from factors that decrease ride comfort. The road impact factors (RIF) are uneven surfaces and anomalies such as potholes, cracks, and utility covers. The driver impact factors (DIF) are behaviors such as abrupt braking, rapid acceleration, weaving, and speeding around curves. Hence, the RIF and the DIF can induce motions and noises that cause rider discomfort. The vehicle impact factors (VIF) affect how riders perceive the disturbances from RIF and DIF. The VIF are a strong function of the vehicle suspension and handling characteristics but they can also include other factors such as features of furniture design, interior aesthetics, and entertainment. Altogether, these factors result in the overall ride quality experienced. Highway agencies narrow the definition of ride quality to the RIF and use a model of the vehicle suspension system, called the Golden Car, to standardize the VIF as a fixed suspension response that dampens vibrations from road roughness (1). The Golden Car approximates the suspension response of vehicles typical of the 1980s, which is around the time-period that practitioners agreed on the approach. Nearly all regular passenger and commercial motor vehicles, regardless of their size and weight, provide similar suspension responses because manufacturers design them to attenuate vibrations within a common range of frequencies that cause hu...

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“…However, it is noted that the APT vehicle dimension may be smaller than existing bus and DART configurations are like a small truck or lorry, hence other related findings can also be considered for APT vehicle dynamics simulation. Another study in [99] illustrated the DART ride index -DRI as a modified BRI for a specific application on smaller vehicle dimensions.…”

Section: Discussionmentioning

confidence: 99%

“…Further study can develop an active quarter-bus model coupled with measuring road profiles of the whole city bus lane networks for BRI calculation. Especially from the refined BRI, different alternatives and scenarios can be developed by adapting the BRI quarter-bus simulation for the APT module with a larger/smaller dimension with a capacity of 60, 80, or 120 passengers given its different suspension configuration as in the investigation of [99]. relationship and BRI thresholds will rationally link the "user needs" with "pavement manager" for pavement management to evaluate road roughness as determined from passenger ride comfort.…”

Section: Discussionmentioning

confidence: 99%

Road infrastructure design towards passenger ride comfort for autonomous public transport

Van¹,

Ron²

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The rapid developments of smart technology and the vision to smart cities have led to parallel developments of various innovative transport means. Among them, road-based autonomous public transport (APT) is being planned, designed and developed worldwide such as the Dynamic Autonomous Road Transit (DART) in the city of Singapore. Innovative vehicle configuration and operation in the APT shall demand new requirements for design and construction of the infrastructure concerning the ride comfort of onboard passengers. This study aims at supporting the required update of guidelines for the design of roadway (e.g. horizontal alignment design), for vehicle speed recommendation along existing infrastructure and for pavement maintenance concerning passenger ride comfort in which DART is considered as a case study for APT application. Bottom-up and top-down approaches are used, in which the former method uses existing bus vehicles and network as a base-line reference while the latter method is based on APT's vehicle specification and operation.At first, this study starts with a comprehensive review of ride comfort thresholds, recent approaches to evaluate passenger ride comfort onboard and to assess road surface irregularity, where research gaps are pointed out for further study. The interaction between passenger-vehicle-pavement is studied through numerical analysis. Given the similarities between regular bus and APT, the former system has been investigated and detailed bus mathematical models are constructed and validated to evaluate the pavement quality that affects passenger ride comfort. Based on the comparison of different bus models regarding their simulation complexity and performance, a refined Bus Ride Index (BRI) is proposed as a key achievement of this research study. At the same time, regression relationships are established between road geometrical design (e.g. horizontal curve radius) and passenger perceptions at different postures onboard the bus. Herein, experimental study with analytical analysis is carried out to evaluate principal factors (e.g. lateral acceleration, lateral jerk and duration of turning movement) affecting passenger-vehicle-road interaction and ride discomfort based on existing bus fleet and road network.Finally, the DART concept is examined from different perspectives such as network analysis, traffic operation and road infrastructure design.The findings indicate that research on geometry design and road maintenance based on passenger ride comfort for APT is important to support the new vehicle concept. In the urban context, while road geometry concerning the horizontal curve is contributing to the upper range (uncomfortable, very uncomfortable to extremely uncomfortable), road roughness is the principal factor affecting passenger ride quality in the lower range of discomfort (not uncomfortable, a little uncomfortable, fairly uncomfortable to uncomfortable). The developed BRI offers a faster, inexpensive, convenient and more suitable method for evaluating bus lane roughness regarding p...

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Precision Bounds of Pavement Deterioration Forecasts from Connected Vehicles

Cited by 13 publications

References 10 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

Use of Connected Vehicles to Characterize Ride Quality

Road infrastructure design towards passenger ride comfort for autonomous public transport

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