Evaluating Pavement Surface Condition by Leveraging Emerging Crowdsourced Data

Monitoring pavement surface condition (PSC) is critical to pavement management and maintenance. Owing to both seasonal inclement weather and variations in traffic loads across the routes, PSC may vary significantly over time and space. Laser trucks, which are commonly used by transportation agencies, are cost-prohibitive and labor-intensive when frequently employed for large areas of PSC assessment. Besides, pavement distress may deteriorate and even pose a great threat to vehicle property and safety if they are not identified or addressed in a timely manner. Emerging crowdsourced data, driven by massive public engagement and reflecting participants’ perception of ride comfort, have the potential to improve the monitoring of pavement conditions at a finer granularity. Taking the interstates of Nashville City, Tennessee as a case, this study exploited crowdsourced Waze pothole reports to introduce various surrogate measures for PSC evaluation. The accuracy and effectiveness of these surrogate measures are assessed by correlating them to annual pavement performance measures (e.g., pavement quality index and international roughness index) derived from laser trucks data by the Tennessee Department of Transportation (TDOT). The preliminary regression results show that the proposed indicator, namely, pothole reports per mile (PRM), is highly correlated with the international roughness index. The correlation also varies across the route. Finally, this paper will demonstrate that surrogate PSC measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study has the potential to enhance the granularity of pavement condition evaluation, further leading to delicate management and maintenance of pavement.