Smart Pavement Monitoring and Management: Leveraging Low-Cost Mobile Sensing, Crowdsourcing, and Machine Learning

Dear Colleagues,

Roadway pavements are a fundamental component of transportation infrastructure and play a critical role in economic activity, mobility, and public safety. Across both developed and developing regions, agencies are increasingly challenged by aging pavement networks, rising traffic demands, climate-related stressors, and limited resources for inspection and maintenance. Traditionally, pavement condition assessment and management have relied on periodic, labor-intensive surveys that are costly, time-consuming, and often limited in spatial and temporal coverage. As a result, there is a growing need for more efficient, scalable, and data-driven approaches to pavement monitoring and decision-making.

Recent advances in low-cost mobile sensing, crowdsourcing, and machine learning have created new opportunities to transform pavement monitoring and management practices. Sensors embedded in smartphones, vehicles, and connected devices enable continuous and large-scale data collection on pavement condition, while crowdsourcing frameworks allow agencies to leverage data from road users and fleets at unprecedented scale. Machine learning techniques provide powerful tools to extract meaningful information from these heterogeneous data sources, supporting automated distress detection, condition evaluation, performance prediction, and optimized maintenance planning. Together, these technologies form the foundation of smart, intelligent pavement systems aligned with broader digital infrastructure and smart city initiatives.

In this Special Issue, we solicit high-quality original research and review articles that address state-of-the-art methods, technologies, and applications related to smart pavement monitoring and management. We welcome both theoretical developments and practical implementations of high technical standards, spanning multiple disciplines, with the aim of fostering cross-fertilization between pavement engineering, sensing technologies, and data analytics. Contributions that demonstrate real-world applicability, scalability, and integration into pavement management systems are particularly encouraged.

Topics of interest include, but are not limited to, the following:

• Low-cost mobile sensing technologies for pavement condition monitoring;
• Crowdsourcing frameworks and participatory sensing for roadway infrastructure;
• Smartphone-, vehicle-, custom sensor- and IoT-based pavement data acquisition;
• Machine learning and artificial intelligence for distress detection and classification;
• Data fusion and uncertainty management in heterogeneous sensing systems;
• Automated condition assessment and performance prediction models;
• Integration of sensing and analytics into pavement management systems;
• Field validation, pilot studies, and large-scale deployments;
• Impacts of climate, traffic, and usage patterns on sensed pavement response;
• Applications of smart pavement technologies in sustainable and resilient transportation systems.

We believe that this Special Issue will provide a timely and valuable platform for disseminating innovative research and practical insights that advance the state of practice in pavement engineering. We look forward to your consideration of this proposal and would be pleased to assist further in defining the scope and objectives of the Special Issue.

Prof. Dr. Symeon Christodoulou
Guest Editor

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• smart roadway pavements
• low-cost sensing
• pavement anomalies detection
• machine learning
• machine vision
• crowdsourcing
• pavement management system