Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes

Sponsor: Michigan Department of Transportation

PI: Colin Brooks

Advances in unmanned aerial vehicle (UAV) technology have enabled these tools to become easier to use and afford. In a budget-limited environment, these flexible remote sensing technologies can help address transportation agency needs in operations, maintenance, and asset management while increasing safety and decreasing cost.

This project tested and evaluated five main UAV platforms with a combination of optical, thermal, and LiDAR sensors to assess critical transportation infrastructure and issues such as bridges, confined spaces, traffic flow, and roadway assets.

A State of the Practice report was completed, and a series of lab testing were accomplished to ensure practicality and safe operations. Field demonstrations were completed at bridges, pump stations, and conferences. The project team gave a series of technical demonstrations at the Intelligent Transportation Systems World Congress in Detroit in September, 2014, enabling outreach to a wide domestic and international audience who gained understanding of the advanced research that MDOT is funding. These demonstrations showed that UAV technologies provide many advantages to helping MDOT cost-effectively assess, manage, and maintain its resources, providing benefit to staff and the traveling public.

A copy of this report can be found on the Michigan Department of Transportation website.

Colin N. Brooks
Colin N. Brooks
Tess Ahlborn
Tess Ahlborn
Timothy Havens
Timothy Havens
Thomas Oommen
Thomas Oommen

Improving Spatial Observability of Dynamic Traffic Systems through Active Mobile Sensor Networks and Crowdsourced Data

SPONSOR:  NATIONAL SCIENCE FOUNDATION (NSF)

PI:  Kuilin Zhang

To provide effective traffic congestion mitigation strategies, transportation agencies need to effectively design sensor networks to reliably estimate and predict traffic conditions across large transportation networks. The next generation traffic sensor network will offer large, diverse data streams not only from fixed traffic detectors, but also from many emerging active mobile traffic sensors such as Unmanned Aerial Vehicles, self-driving cars, and crowdsourced data sources from social sensors and transportation network companies. This new generation of agile sensors can provide a much richer but also increasingly complex traffic data environment. Moreover, crowdsourced data is generally uncontrolled, inaccurate and unreliable. This research focuses on new sensor design/control applications to transform the interconnection between travelers, sensors, data and transportation management systems.

The objective of this research is to develop rigorous mathematical foundations and innovative algorithms to accurately quantify spatial observability of dynamic traffic states, optimize active mobile sensor locations, and mine information from crowdsourced data sources. The research team will first characterize analytical space-time distributions of different traffic states at both macroscopic and microscopic scales, and further develop time-geography-oriented optimization for quantifying spatial observability for dynamic networks. A new class of ubiquitous sensor network design problems is studied for the traffic state estimation stage, and the integration of the well-fused crowdsourced data with optimized fixed and active mobile sensor data is investigated under different levels of activity/penetration rates. Utilizing the structure of underlying dynamic transportation networks, this research aims to develop computationally efficient optimization algorithms to create a distributed and scalable computing framework, which can solve joint scheduling and routing problems of active mobile sensors to increase coverage and accuracy. The research team will develop generic measures of spatial network observability that can provide additional theoretical findings for general civil engineering systems such as earthquake impact detection, ground water pollution source identification, and critical infrastructure monitoring.

Kuilin Zhang
Kuilin Zhang
Colin N. Brooks
Colin N. Brooks

Bridge Condition Assessment Using Remote Sensors

Sponsor:  US Department of Transportation Research and Innovative Technology Administration (RITA)

PI:  Tess Ahlborn     

The condition of the nation’s infrastructure has gained increased attention in recent years, primarily as a result of catastrophic events such as the I-35W collapse in Minneapolis. However, deteriorating transportation infrastructure has burdened transportation agencies for many years. Bridges continue to age, and funds for the repair and replacement of this infrastructure are insufficient at current funding levels. Remote sensing technologies, which enables non-contact data collection at great distances, offer the ability to enhance inspection and monitoring of bridges. Research Objectives The objective is to explore the use of remote sensing technologies to assess and monitor the condition of bridge infrastructure and improve the efficiency of inspection, repair, and rehabilitation efforts to develop unique signatures of bridge condition. Methodology Remote sensing technologies will be correlated with in-place sensors to obtain bridge condition assessment data without the need to place heavy instrumentation on the structure. This information will then be analyzed by a computer decision support system to develop unique signatures of bridge condition. Monitoring how these signatures change over time will provide state and local engineers with additional information used to prioritize critical maintenance and repair of our nation’s bridges. The ability to acquire this information remotely from many bridges without the expense of a dense sensor network will provide more accurate and near real-time assessments of bridge condition. Improved assessments allow for limited resources to be better allocated in repair and maintenance efforts, thereby extending the service life and safety of bridge assets, and minimizing costs of service-life extension.

A copy of the final report and its appendices can be found at http://mtri.org/bridgecondition/

Tess Ahlborn
Tess Ahlborn
Lawrence Sutter
Lawrence Sutter
Colin N. Brooks
Colin N. Brooks
Bob Shuchman
Bob Shuchman
Joe Burns
Joe Burns

Implementation of Unmanned Aerial Vehicles (UAVs) for Assessment of Transportation Infrastructure – Phase II

Sponsor:  Michigan Department of Transportation

PI:  Colin Brooks

Through Phase I of MDOT’s “Evaluating the Use of Unmanned Aerial Vehicles for Transportation Purposes” project, the Michigan Tech combined project team was successfully able to plan, demonstrate, and document UAV capabilities in the assessment of transportation assets.

With the rapid development of UAV’s, MDOT has requested additional research concerning their use for transportation asset management.  The work plan of the MTU combined team includes:

TASK 1:

  • Collect data from the UAV platform using sensing technology in near-time (as real-time as can be achieved) demonstrating, developing, and implementing storage capabilities of large amounts of data, usage of data, and application development that complements current data usage and application at MDOT.
  • Provide data collection from UAVs to the MDOT Data, Use, Analysis, and Process (DUAP) project that meets the quality, low latency delivery and data format requirements.
  • Provide a report that describes and recommends optional methods to store and distribute potentially large imaging, point cloud, and 3D surface datasets created through UAV-based data collection.

TASK 2:

  • Demonstrate, develop, and implement high-accuracy simultaneous thermal/photo/video/Light Detection and Ranging (LIDAR) measurement using a high-fidelity sensor-fused UAV positioning approach.

TASK 3:

  • Demonstrate the capabilities to complete aerial remote sensing data collections to meet MDOT mapping and construction monitoring needs.  Coordinate with MDOT Survey Support to identify pilot projects and meet data delivery needs satisfying MDOT requirements for spatial data collection as it pertains to data density, absolute and relative 3D positional accuracy.

TASK 4:

  • Demonstrate, develop and implement uses of data collection from UAV(s) and sensors for operations, maintenance, design, and asset management.

TASK 5:

  • Demonstrate, develop and implement enhanced testing of UAV-based thermal imaging for bridge deck structural integrity.
  • Compare data collected from UAV sensors to current data collected and systems used at MDOT for highway assets/operations.

TASK 6:

  • Demonstrate, develop, and implement systems management and operations uses.

TASK 7:

  • Provide a benefit/cost analysis and performance measures that define the return on investment as a result of deploying UAVs and related sensory technologies for transportation purposes.

TASK 8:

  • Secure a Federal Aviation Administration (FAA) Certificate of Authorization (COA) to complete the tasks and deliverables.
Colin N. Brooks
Colin N. Brooks
Tess Ahlborn
Tess Ahlborn
Timothy Havens
Timothy Havens
Thomas Oommen
Thomas Oommen
Kuilin Zhang
Kuilin Zhang

Sustainable Geotechnical Asset Management along the Transportation Infrastructure Environment Using Remote Sensing

Sponsor:  USDOT Research and Innovative Technology Administration (RITA)

PI:  Thomas Oommen

The objective of this proposed study is to establish a sustainable framework for Geotechnical Asset Management (GAM) using remote sensing data that can be used by the state departments of transportation across the US in order to identify and mitigate the failure of geotechnical assets proactively along the transportation infrastructure environment. Developing a sustainable GAM to enable more proactive infrastructure risk assessment is critical for strategic investment and long term management of the United State’s transportation infrastructure. Remote sensing techniques, such as Interferometric Synthetic Aperture Radar (InSAR) and Light Detection and Ranging (LiDAR) provide great opportunities to measure ground movements precisely. Additionally, high resolution optical images complement observations from InSAR and LiDAR. Applying these techniques to the transportation environment can help to monitor and manage the stability of the geotechnical assets and would significantly reduce the level of effort currently needed to survey and inspect these assets.

Thomas Oommen
Thomas Oommen
Colin N. Brooks
Colin N. Brooks
Pasi Lautala
Pasi Lautala
Stan Vitton
Stan Vitton