Purdue University Graduate School
Evaluating Vehicle Data Analytics for Assessing Road Infrastructure Functionality.pdf (6.77 MB)

Evaluating Vehicle Data Analytics for Assessing Road Infrastructure Functionality

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posted on 2020-12-15, 01:24 authored by Justin Anthony MahlbergJustin Anthony Mahlberg
The Indiana Department of Transportation (INDOT) manages and maintains over 3,000 miles of interstates across the state. Assessing lane marking quality is an important part of agency asset tracking and typically occurs annually. The current process requires agency staff to travel the road and collect representative measurements. This is quite challenging for high volume multi-lane facilities. Furthermore, it does not scale well to the additional 5,200 centerline miles of non-interstate routes.

Modern vehicles now have technology on them called “Lane Keep Assist” or LKA, that monitor lane markings and notify the driver if they are deviating from the lane. This thesis evaluates the feasibility of monitoring when the LKA systems can and cannot detect lane markings as an alternative to traditional pavement marking asset management techniques. This information could also provide guidance on what corridors are prepared for level 3 autonomous vehicle travel and which locations need additional attention.

In this study, a 2019 Subaru Legacy with LKA technology was utilized to detect pavement markings in both directions along Interstates I-64, I-65, I-69, I-70, I-74, I90, I-94 and I-465 in Indiana during the summer of 2020. The data was collected in the right most lane for all interstates except for work zones that required temporary lane changes. The data was collected utilizing two go-pro cameras, one facing the dashboard collecting LKA information and one facing the roadway collecting photos of the user’s experience. Images were taken at 0.5 second frequency and were GPS tagged. Data collection occurred on over 2,500 miles and approximately 280,000 images were analyzed. The data provided outputs of: No Data, Excluded, Both Lanes Not Detected, Right Lane Not Detected, Left Lane Not Detected, and Both Lanes Detected.

The data was processed and analyzed to create spatial plots signifying locations where markings were detectable and locations where markings were undetected. Overall, across 2,500 miles of travel (right lane only), 77.6% of the pavement markings were classified as both detected. The study found

• 2.6% the lane miles were not detected on both the left and right side
• 5.2% the lane miles were not detected on the left side
• 2.0% the lane miles were not detected on the right side 8

Lane changes, inclement weather, and congestion caused 12.5% of the right travel lane miles to be excluded. The methodology utilized in this study provides an opportunity to complement the current methods of evaluating pavement marking quality by transportation agencies.

The thesis concludes by recommending large scale harvesting of LKA from a variety of vendors so that complete lane coverage during all weather and light conditions can be collected so agencies have an accurate assessment of how their pavement markings perform with modern LKA technology. Not only will this assist in identifying areas in need of pavement marking maintenance, but it will also provide a framework for agencies and vehicle OEM’s to initiate dialog on best practices for marking lines and exchanging information.


Degree Type

  • Master of Science in Civil Engineering


  • Civil Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Dr. Darcy M. Bullock

Additional Committee Member 2

Dr. Konstantina Gritzka

Additional Committee Member 3

Dr. Kumares Sinha

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