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<p>For decades, agencies have collected infrastructure condition assessment data using dedicated equipment that require substantial capital investments and staff time to operate/drive. However, these techniques are challenging to scale network wide. The United States has over 8 million lane miles of roadways which generate almost 3 trillion vehicle miles annually. Connected vehicles can now provide real-time data on a wide range of parameters such as vehicle speed, location, lane markings, and 3 axis acceleration. This dissertation develops techniques to validate, utilize and leverage connected vehicle data for infrastructure assessment and monitoring. </p>
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<p>Opportunities to employ connected vehicle data were examined in the following areas: quality of lane marking edge lines, width of lanes (particularly temporary lanes in construction zones), and pavement roughness. Quality of lane markings was evaluated using embedded lane keep assist data and equipment. In 2020 and 2021 over 5000 miles of pavement markings were evaluated on Indiana interstates. Comparisons between 2020 and 2021 data showed detection increase from 80.2% to 92.3%. Although there are no industry standards for lane keep assist data, this study demonstrated both the importance and utility of partnering with the automotive industry to develop shared vision on acceptable lane quality. </p>
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<p>A follow-up quantitative study was performed using a LiDAR vehicle to compare LiDAR values with those that are obtained from traditional retroreflectivity measurements used for contract acceptance and maintenance decisions. A comparison of LiDAR intensity to retroreflectivity (the industry standard) on 70 miles of US-52 and US-41 in Indiana was assessed and a linear regression found that the intensity values are comparable to retroreflectivity readings with an R2 of 0.87 and 0.63 for right edge and center skip lines respectively. These results suggest that LiDAR is a viable tool for monitoring of retroreflectivity of pavement markings that are strongly correlated with existing standards, but scale much better than traditional retroreflectivity measurement techniques.</p>
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<p>The LiDAR data also provided the opportunity to evaluate how well modern vehicles measure lane width. This dissertation reports on over 200 miles of roadway and when compared to LiDAR and field measurements had a root mean square error of 0.24 feet. This data is valuable for agencies to quickly identify system wide where lane widths fall below acceptable design standards, typically 11-feet. </p>
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<p>The final connected vehicle data set evaluated was pavement roughness and compared with traditional dedicated vehicles collecting international roughness index (IRI) data. The study evaluated a 20-mile segment in 2022, and showed a linear regression between these data sets had an R2 of over 0.7, suggesting that connected vehicle roughness data can be utilized for network level monitoring of pavement quality. Scalability of these techniques is also illustrated with graphics characterizing IRI values obtained from almost 6 million records to evaluate improvements in Indiana construction zones and over 5,800 miles of I-80 in April of 2022 and October 2022.</p>
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<p>Although connected vehicle data for infrastructure assessment is still in its infancy, these case studies demonstrate significant opportunities for public agencies to collect selected system wide infrastructure condition in near real-time, and in many cases at a lower cost than traditional techniques. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/22688923 |
| Date | 29 April 2023 |
| Creators | Justin Anthony Mahlberg (9746357) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/LEVERAGING_CONNECTED_VEHICLE_DATA_FOR_INFRASTRUCTURE_PERFORMANCE_EVALUATION_AND_MONITORING/22688923 |
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