Probe Vehicles Used to Measure Road Ride Quality: Pilot Demonstration

Flintsch, Gerardo W.; Valeri, S.; Katicha, Samer W.; Izeppi, Edgar David de León; Medina-Flintsch, Alejandra

doi:10.3141/2304-18

Cited by 17 publications

(11 citation statements)

References 6 publications

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“…In 2011, Flintsch et al conducted a pilot study using probe vehicles to measure road ride quality, or roughness (6). Again, vertical acceleration data were used as an index of vehicle vibration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The use of simple sensors such as accelerometers, already installed either in vehicles or mobile devices, is able to measure the vibration responses of vehicles directly and is believed to correlate highly with pavement roughness. Several past efforts investigated the feasibility of collecting acceleration data to assess pavement roughness at a significantly lower cost and higher level of temporal resolution (3)(4)(5)(6). One critical issue about the acceleration-only index is that its value depends on a combined effect of vehicle operating speeds, vehicle dynamic features, and pavement characteristics (i.e., wavelength).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Zeng

Park

Fontaine

et al. 2015

Transportation Research Record: Journal of the Transportation R

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Transportation agencies devote significant resources toward the collection of highly detailed and accurate pavement roughness data by using profiler vans to support pavement maintenance decisions. However, these agencies often cannot afford to measure roughness annually for the whole pavement network. This study introduced an improved acceleration-based metric, an index normalized by vehicle operating speed, to be used on a regular basis to screen pavement segments that are likely to be deficient; then a profile van can be sent to accurately measure the roughness condition. A profile van collected pavement profile data on 50 mi (80 km) of roadway; these data were then used to calculate the international roughness index (IRI). Meanwhile, two tablet computers were placed on the vehicle floor to collect data, including three-way accelerations, GPS coordinates, and vehicle speeds. A normalized acceleration-based index was created by incorporating a speed factor. Furthermore, logistic regression models were created to evaluate the effectiveness of the proposed index in identifying deficient pavement sections [IRI ≥ 140 in./mi (2.21 m/km)]. The proposed acceleration-based metric was able to identify between 80% and 93% of all deficient pavement sections. This research points to the feasibility of using a cost-effective acceleration-based application for network screening, a process that will reduce the total mileage of pavement sections needing to be measured and meanwhile still identify locations for which maintenance work is necessary.

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Section: Literature Reviewmentioning

confidence: 99%

mentioning

confidence: 99%

Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Zeng

Park

Fontaine

et al. 2015

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Back-office systems would be responsible for further data processing, analysis, and storage. The technology required to implement crowdsourced pavement condition monitoring with smartphones is already established, and the concept has been shown to be technically feasible (3,5,15,16).…”

Section: Crowdsourcingmentioning

confidence: 99%

Pavement Condition Monitoring with Crowdsourced Connected Vehicle Data

Dennis¹,

Hong²,

Wallace³

et al. 2014

Transportation Research Record: Journal of the Transportation R

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The Michigan Department of Transportation (DOT) is interested in new technologies to improve transportation asset management (TAM) practices. A central component of TAM is pavement condition and performance assessment. To investigate methods to improve TAM data collection, the Michigan DOT and the Center for Automotive Research researched the potential for connected vehicle data to contribute to pavement condition and performance assessment. This effort included the review, critical analysis, and synthesis of several research reports and pilot projects to determine feasible methods of using connected vehicle data for pavement condition and performance assessment. The scope of connected vehicle data was defined to include data collected by sensors installed on consumer available vehicles and smartphones. The technology to allow for data capture from embedded vehicle systems and smartphones was established, but implementing such practices into TAM had not yet proved to be practical. One possibility for effective pavement data collection was to employ crowdsourcing, with the use of numerous probe vehicles. Crowdsourcing was not a common practice in pavement condition assessment. Using crowdsourced data in existing TAM programs may require innovative methods of data collection, processing, and management. This report concluded that by focusing on consumer available equipment and crowdsourced data, new connected vehicle data–based pavement condition metrics could be used for TAM in 3 to 5 years.

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“…In addition, it has been reported that roughness characterizes energy consumption during the use phase from a life-cycle assessment view [ 11 ]. These facts justify why roughness measurements attract the interest of a Pavement Management System (PMS) [ 12 , 13 ]. Roughness is most often quantified in terms of the International Roughness Index (IRI, m/km), developed by the Word Bank in the mid-1980s as a standardized measurement method [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating Pavement Sensing Data for Pavement Condition Evaluation

Gkyrtis

Loizos

Plati

2021

Sensors

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Highway pavements are usually monitored in terms of their surface performance assessment, since the major cause that triggers maintenance is reduced pavement serviceability due to surface distresses, excessive pavement unevenness and/or texture loss. A common way to detect pavement surface condition is by the use of vehicle-mounted laser sensors that can rapidly scan huge roadway networks at traffic speeds without the need for traffic interventions. However, excessive roughness might sometimes indicate structural issues within one or more pavement layers or even issues within the pavement foundation support. The stand-alone use of laser profilers cannot provide the related agencies with information on what leads to roughness issues. Contrariwise, the integration of multiple non-destructive data leads to a more representative assessment of pavement condition and enables a more rational pavement management and decision-making. This research deals with an integration approach that primarily combines pavement sensing profile and deflectometric data and further evaluates indications of increased pavement roughness. In particular, data including Falling Weight Deflectometer (FWD) and Road Surface Profiler (RSP) measurements are used in conjunction with additional geophysical inspection data from Ground Penetrating Radar (GPR). Based on pavement response modelling, a promising potential is shown that could proactively assist the related agencies in the framework of transport infrastructure health monitoring.

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Probe Vehicles Used to Measure Road Ride Quality: Pilot Demonstration

Cited by 17 publications

References 6 publications

Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Identifying Deficient Pavement Sections by Means of an Improved Acceleration-Based Metric

Pavement Condition Monitoring with Crowdsourced Connected Vehicle Data

Integrating Pavement Sensing Data for Pavement Condition Evaluation

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