Pavement Quality Evaluation Using Connected Vehicle Data

Mahlberg, Justin; Li, Howell; Zachrisson, Björn; Leslie, Dustin K.; Bullock, Darcy M.

doi:10.3390/s22239109

Cited by 8 publications

(4 citation statements)

References 23 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be especially important for high-traffic areas or areas that are prone to damage as it allows for quick and targeted interventions to prevent further deterioration. The use and validation of this data source are proposed in Mahlberg et al, 2022(Mahlberg et al, 2022b. Production vehicle data for pavement quality leverages individual wheel speed via rotational sensors in combination with drivetrain data to provide accurate roughness information (Magnusson and Svantesson, 2021).…”

Section: Data Opportunities and Motivationmentioning

confidence: 99%

Applications of using connected vehicle data for pavement quality analysis

Mahlberg,

Li,

Zachrisson

et al. 2024

Front. Future Transp.

Self Cite

View full text Add to dashboard Cite

Current quantitative methods to evaluate pavement conditions in the United States are most commonly focused on construction acceptance using the International Roughness Index (IRI). However, from an asset management perspective, qualitative visual inspection techniques are the most prevalent. Modern vehicles with factory-equipped sensors drive these roadways daily and can passively assess the condition of infrastructure at an accuracy level somewhere between qualitative assessment and rigorous construction acceptance techniques. This paper compares crowdsourced ride quality data with an industry standard inertial profiler on a 7-mile bi-directional construction zone. A linear correlation was performed on 14 miles of I-65 that resulted in an R2 of 0.7 and a p-value of <0.001, but with a modest fixed offset bias. The scalability of these techniques is illustrated with graphics characterizing IRI values obtained from 730,000 crowdsourced data segments over 5,800 miles of I-80 in April of 2022 and October 2022. This paper looks at the use of standard original equipment manufacturer (OEM) on-board sensor data from production vehicles to assess approximately 100 miles of roadway pavements before, during, and after construction. The completed construction projects observed IRI improvements of 10 in/mi to 100 in/mi. These results suggest that it is now possible to monitor pavement ride quality at a system level, even with a small proportion of connected vehicles (CV) providing roughness data.

show abstract

Section: Data Opportunities and Motivationmentioning

confidence: 99%

Applications of using connected vehicle data for pavement quality analysis

Mahlberg,

Li,

Zachrisson

et al. 2024

Front. Future Transp.

Self Cite

View full text Add to dashboard Cite

show abstract

“…This data has already shown wide-ranging applicability to prioritize traffic signal retiming, monitor mobility in varying weather conditions, and assess work zone mobility [20,23,24]. Including additional probe, CV data sources provide unique opportunities to evaluate transportation infrastructure and prioritize investments, including pavement markings, pavement quality, and the deterioration of roadway conditions during winter storms using friction data [21,25]. Combining all of these emerging CV data attributes can provide stakeholders and planning commissions with a holistic view of the transportation network and present opportunities to invest in infrastructure improvements, including electric vehicle charging.…”

Section: Emerging Connected Vehicle Data Opportunitiesmentioning

confidence: 99%

Using Connected Vehicle Data to Evaluate National Trip Trends

Desai,

Mathew,

Mahlberg

et al. 2023

Applied Sciences

Self Cite

View full text Add to dashboard Cite

The National Household Travel Survey (NHTS), conducted by the Federal Highway Administration, has historically been used for documenting personal mobility trends. Current techniques using surveys to collect this data are labor-intensive and difficult to scale. Emerging connected vehicle (CV) data can provide an alternative data source to potentially provide a more scalable method to measure the temporal and spatial usage of passenger vehicles in near real-time. With an impending shift in the automobile industry towards alternative fuel vehicles (AFV), agile monitoring of trip trends is important to help guide state and national investments in AFV infrastructure. This study presents methodologies and visualizations summarizing observed trip characteristics using a sample of more than 500 billion CV records and nearly 1 billion CV trips for December 2022 in the United States. The analysis found very close agreement between trip lengths for internal combustion engine vehicles (ICEV) for CVs and those reported by the 2017 NHTS. Mean trip lengths and trip durations from CVs and NHTS for ICEVs are within 7.8% and 6.6% of each other. The 85th percentile comparison was similarly close, within 0.7% and 8.3%. A comparison of trip trends among states for ICEVs and AFVs as well as US census places and temporal trends for a selection of states, including Indiana, Texas, Wyoming, and California, is provided. The paper concludes that CV data is an important source to monitor trip characteristics across ICEVs and AFVs in near real-time, which will be particularly important to track during the anticipated change to AFVs.

show abstract

“…Mahlberg et al ( 2022) argued that despite the lack of standards for using connected vehicles to monitor pavement quality, agencies can begin to benefit from its potential for network level monitoring [22]. The authors suggested that even non-connected modern vehicles have extensive instrumentation that agencies can leverage to monitor infrastructure condition including pavement markings, signs, and pavement smoothness.…”

Section: Connected Vehiclesmentioning

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

Pavement Quality Evaluation Using Connected Vehicle Data

Cited by 8 publications

References 23 publications

Applications of using connected vehicle data for pavement quality analysis

Applications of using connected vehicle data for pavement quality analysis

Using Connected Vehicle Data to Evaluate National Trip Trends

Driving Standardization in Infrastructure Monitoring: A Role for Connected Vehicles

Contact Info

Product

Resources

About