Category: 2020

POSTER 20-12B: MULTI-HAZARDS EXPOSURE, VULNERABILITY AND RISK ASSESSMENT: BUILDING CLIMATE RESILIENCE WITH THE MHEVRA TOOL

MULTI-HAZARDS EXPOSURE, VULNERABILITY AND RISK ASSESSMENT: BUILDING CLIMATE RESILIENCE WITH THE MHEVRA TOOL

PI: Adjo Amekudzi-Kennedy

Co-PI(s): 

Institution(s): Georgia Institute of Technology

Abstract

Resilience building capabilities are becoming increasingly essential components of performance management systems for transportation and other infrastructure agencies. The United States? Infrastructure Investment and Jobs Act incentivizes transportation and other infrastructure agencies to prioritize investments to strengthen resilience to climate-related disruptions. Developing these capabilities will enable agencies to understand better how their systems are exposed to different hazards and provide the information necessary for prioritizing their assets and systems for resilience improvement. This paper discusses an approach to resilience building to known and unknown climate-related threats and extreme events in a transportation agency. It leverages long-term climate hazard databases, spatial and statistical analyses, and non-probabilistic approaches for specific and general climate resilience improvement. The approach was developed as part of a research project to create climate resilience building capabilities for the Georgia Department of Transportation. The study highlights the importance of good multiscalar data for addressing both specific and general resilience. It also highlights the importance of infrastructure and organizational resilience in creating robust approaches to building resilience in transportation systems. The paper is of potential value to practitioners and researchers interested in developing resilience building capabilities to manage the effects of climate-related hazards and extreme events as well as unknown threats on infrastructure and organizational performance.

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POSTER 20-12C: HARMONIZING CLIMATE ADAPTATION PLANNING FOR TRANSPORTATION SYSTEM RESILIENCE ? DEVELOPMENT OF AN ADAPTATION GUIDEBOOK FOR TRANSPORTATION

HARMONIZING CLIMATE ADAPTATION PLANNING FOR TRANSPORTATION SYSTEM RESILIENCE ? DEVELOPMENT OF AN ADAPTATION GUIDEBOOK FOR TRANSPORTATION

PI: Adjo Amekudzi-Kennedy

Co-PI(s): 

Institution(s): Georgia Institute of Technology

Abstract

Developing long-term transportation plans is vital for the allocation of funds, project prioritization, maintenance scheduling, and preparation for potential future challenges, such as urban development and population changes. Nevertheless, decision-makers are finding it progressively more complex and demanding to devise long-term transportation plans due to the profound uncertainty of what lies ahead. Furthermore, as crucial systems like transportation become more interconnected, there is a higher possibility of catastrophic and unexpected hazards threatening systems with more frequency and intensity. Climate change-induced disasters pose a significant threat to vulnerable transportation systems worldwide. This thesis proposes an approach to integrate adaptation planning in disaster risk reduction through the development of an adaptation guidebook. The guidebook aims to support local communities and policymakers in identifying and addressing the climate risks and impacts. The objective of this writing is to discuss emerging adaptation frameworks, review adaptation strategies for transportation assets and propose a Climate Adaptation Guidebook for Transportation including context specific adaption measures for different districts in a state Department of Transportation using Georgia Department of Transportation as the case study ? to address adaptation consequences of climate change related events to transportation systems.

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POSTER 20-12D: UNCERTAINTY PLANNING: RESILIENCE WITH IMPERFECT KNOWLEDGE

UNCERTAINTY PLANNING: RESILIENCE WITH IMPERFECT KNOWLEDGE

PI: Adjo Amekudzi-Kennedy

Co-PI(s): Baabak Ashuri, Russell Clark, and Brain Woodall

Institution(s): Georgia Institute of Technology

Abstract

Decision Making Under Deep Uncertainty (DMDU) enables decision makers to better incorporate risk into planning and decision making to exploit opportunities and mitigate risks. DMDU, however, is an emerging field with little maturity in its application to resilience building and resilient operations. As such, this project aimed to formulate a framework of three methods for planning and operating resilient transportation infrastructure systems on multiple levels of governance. A case study on the Talmadge Memorial Bridge describes a situation of deep uncertainty that benefits from the decision-making methods.

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POSTER 20-13: COMMUNITY-AUGMENTED RAPID-RESPONSE TO EVENTS (CARE) INTEGRATED CRISIS COMMUNICATION SYSTEM

COMMUNITY-AUGMENTED RAPID-RESPONSE TO EVENTS (CARE) INTEGRATED CRISIS COMMUNICATION SYSTEM

PI: John E. Taylor

Co-PI(s): Neda Mohammadi

Institution(s): Georgia Institute of Technology

Abstract

The objective of this project is to establish a crisis rapid-response communication system, which is augmented with location-specific social and community data, and integrated with current GDOT crisis identification and response communication processes. This will be achieved through development of a system that leverages social and community data for detecting, validating, and more effectively communicating specific routine or emergency events. The system development and integration proposed in this project aims to supplement current GDOT crisis identification and response management systems in place in terms of speed, coverage, effectiveness, efficiency, situational awareness, and inclusiveness, and support day-to-day decision making and response operations, as well as state of emergency events and long-term planning. A central focus of this specific three year project will be to examine ongoing events occurring in Georgia with augmented social and community data.

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POSTER 20-27: SATELLITE IMAGE AND REMOTE SENSING ANALYSIS FOR HIGHWAY ASSET MANAGEMENT

SATELLITE IMAGE AND REMOTE SENSING ANALYSIS FOR HIGHWAY ASSET MANAGEMENT

PI: S. Sonny Kim

Co-PI(s): Stephan Durham, Mi Chorzepa, Jidong James Yang, and Deepak Mishra

Institution(s): University of Georgia

Abstract

Pavement condition assessment is commonly performed using information about rideability, structure, surface distress and skid resistance. In spite of the long-lasting contribution of conventional road monitoring methods, these methods require data collected from in-situ inspection. Due to the extensive road network in the US, these approaches are not capable of monitoring all the roadways. On the other hand, remote sensing methods can play a significant role to complement conventional methods. Remote sensing methods use electromagnetic radiations in a wide range of wavelengths to collect information about various objects. Satellite imagery, as one of the cutting edge methods of remote sensing, utilizes the visible and infrared range of the electromagnetic spectrum to collect images with multiple bands. It has been seen in the literature that asphalt pavements have lower mean in reflectance values than concrete pavements. Also, as asphalt pavements age, reflectance values increase. Thus, this research investigates the application of free and available satellite images for monitoring road conditions.

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POSTER 20-29: CLUSTERING ANALYSIS OF PERMANENT COUNT SITES TO ENHANCE DATA ACCURACY THROUGH IMPROVED QUALITY CONTROL RULESPOSTER 20-29:

CLUSTERING ANALYSIS OF PERMANENT COUNT SITES TO ENHANCE DATA ACCURACY THROUGH IMPROVED QUALITY CONTROL RULES

PI: Jidong J Yang

Co-PI(s): Stephan Durham, Sonny Kim, and Mi Geum Chorzepa

Institution(s): University of Georgia

Abstract

Data quality assurance is a critical component of statewide traffic monitoring programs. The current state of the practice uses quality control rules and examine data at each count station individually, which may not be robust since it disregards inherent dependency of stations in close vicinity. This research study aims to identify groups or clusters of continuous count stations (CCS) that are spatiotemporally correlated and leverage such correlations to improve the quality control of traffic data in support of various planning and engineering practices and decision-making at GDOT.

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POSTER 20-04: WEIGHT-IN-MOTION (WIM) DATA SYNTHESIS & VALIDATION – DEEP LEARNING MODELS FOR VEHICLE CLASSIFICATIONPOSTER 20-04:

WEIGHT-IN-MOTION (WIM) DATA SYNTHESIS & VALIDATION – DEEP LEARNING MODELS FOR VEHICLE CLASSIFICATION

PI: Mi Geum Chorzepa

Co-PI(s): Jidong J. Yang, Stephan Durham, and Sonny Kim

Institution(s): University of Georgia

Abstract

Sensor-based nonintrusive systems were able to successfully report up to 8 classes of vehicle types while the traditional video-based systems could only distinguish up to 4 classes of vehicle types. Recently, machine-learning methods (Naïve Bayes, K-nearest neighbor classification, random forest, and support vector machine) and deep-learning vision models (CNNS, Faster RCNN, and YOLO models) have been applied for vehicle classification with improved accuracies. However, vehicle classification with the increased number of categories, such as 13 FHWA classes, is still a challenging task. This study investigates a composite model architecture by leveraging the state-of-the-art vision transformer models coupled with a wheel-detection model for complementary feature learning.

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POSTER 20-07: LEVERAGING MULTISOURCE DATA FOR TRAFFIC DATA QUALITY CONTROL

LEVERAGING MULTISOURCE DATA FOR TRAFFIC DATA QUALITY CONTROL

PI: Jidong J. Yang

Co-PI(s): Stephan Durham, Sonny Kim, and Mi Geum Chorzepa

Institution(s): University of Georgia

Abstract

Maintaining High-quality traffic data serves as the foundation for core transportation planning/engineering activities and decisionmaking. The current state of the practice in traffic data quality control features rule-based data checking and validation processes, where the rules are subjective and insensitive to variation inherited with traffic data. This research study aims to leverage the multisource data available at GDOT to improve the quality control process for traffic data. Cross-validating inductive loop-based traffic count data with independent sources (e.g., from the exiting video detection data as part of the 511 system) provides a practical and robust approach for quality control of traffic data in support of various planning/engineering practices and decision-making at GDOT.

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POSTER 20-20: TACIT KNOWLEDGE MODEL TO SUPPORT KNOWLEDGE CAPTURE AND TRANSFER IN GDOT

TACIT KNOWLEDGE MODEL TO SUPPORT KNOWLEDGE CAPTURE AND TRANSFER IN GDOT

PI: David A. Guerra-Zubiaga

Co-PI(s): 

Institution(s): Kennesaw State University

Abstract

Experiences from contractors, vendors, and internal GDOT staffs are often time lost because there is little mechanism for capturing experiences-tacit knowledge. This was how this project provide value to GDOT organization. The research bases on the idea that knowledge is an important intangible asset of any organization. Let’s take a step back and introduce two types of knowledge: Explicit Knowledge and Tacit Knowledge. Explicit can be described with words, instructions, and guidelines while tacit is more complex not only to identify but also to organize, capture and reuse for supporting key decision making. Tacit knowledge is based on the idea that people know more than what they can verbally express. This research concentrates on Tacit Knowledge Capture and Store using the experimental software. Moreover, GDOT and Kennesaw Research team are well aware that capturing, transferring, and maintaining skilled employees and contractors’ experiences is beneficial in terms of cost, efficiency, time, and efforts spend on special projects (i.e.: I-85 Bridge Collapse). The research team met with different GDOT offices, such as Office of Traffic Operations, Office of Bridge Maintenance, Bridge Construction, and Bridge Design to invent the instances (sketches, videos, patterns, and storytelling) of tacit knowledge and store them in the experimental software base on the Tacit Knowledge Model in which the KSU research team had defined previously. Tacit Knowledge Model is created based on Object Oriented Design and Programming using Unified Modeling Language (UML) notation. The team can reach its goal by (1) understanding previous GDOT knowledge management projects, (2) studying the I-85 bridge collapse case study (3) evaluating the findings and utilizing them at a specific GDOT department or area to consolidate specific benefits within that department or area via defined scenarios, (4) defining Tacit Knowledge Model and finally (4) developing a new tool with the potential of being implemented throughout the entire organization. •Keywords: Knowledge management, Tacit knowledge management, Explicit knowledge, Implicit knowledge, Tacit knowledge lifecycle, Tacit knowledge model.

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POSTER 20-21: A FORECAST OF STATE MOTOR FUEL REVENUES: THE EFFECT OF NEW TECHNOLOGIES AND THE STATE VEHICLE FLEET MIX ON GEORGIA MOTOR FUEL RECEIP

A FORECAST OF STATE MOTOR FUEL REVENUES: THE EFFECT OF NEW TECHNOLOGIES AND THE STATE VEHICLE FLEET MIX ON GEORGIA MOTOR FUEL RECEIPTS

PI: Laura Wheeler

Co-PI(s): 

Institution(s): Georgia State University

Abstract

This display outlines the research on the impact of alternative fuel vehicles on the state motor fuel receipts and the development of a model to forecast motor fuel receipts.

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