POSTER 19-26:  CURVE SAFETY IMPROVEMENTS USING MOBILE DEVICES AND AUTOMATIC CURVE SIGN DETECTION – PHASE II

PI: James (Yichang) Tsai

Co-PI(s):  

Institution(s): Georgia Institute of Technology


Abstract

A disproportionally high number of fatal crashes (25%) occur on horizontal curves, even though curves represent only a fraction of the roadway network (5% of highway miles). MUTCD requires various horizontal alignment warning signs to ensure roadway safety on curves. However, current transportation agencies? practices for inventorying existing curve signs are typically a costly manual procedure. This report presents automatic curve sign detection (ACSD) using low-cost mobile devices such as smart phones, machine learning, and crowdsourcing to develop a live curve sign inventory. This inventory can be used to analyze MUTCD curve sign compliance for safety improvements. Phase II of this research project focuses on 1) validating the curve sign computation accuracy, 2) recommending data storage and management alternatives, and 3) recommending a methodology for MUTCD curve sign compliance analysis (MCSCA), and 4) presenting a roadmap for implementing the proposed methodology. Based on tests performed in Phase II, the proposed triangulation method, using the images and GPS data collected using smartphones, can achieve accuracy within 10 meters for most cases. However, some cases can have an error greater than 15 meters. Error cases typically occur when there is a significant change in vertical alignment or a rough roadway surface. Future refinement on the triangulation method that accounts for the vehicle?s pitch angle is recommended. Additionally, data storage and management alternatives and a methodology for MUTCD curve sign compliance analysis are recommended. Finally, a roadmap for implementing the proposed ACSD, live curve sign inventory, and MCSCA is presented.


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POSTER 19-19: STATE AND DOT LEVEL ADMINISTRATION OF LAND OWNERSHIP ISSUES

PI: Karen Marie Johnson

Co-PI(s): John Marshall, jmarshall32@gsu.edu

Institution(s): Georgia State University


Abstract

The right-of-way acquisition process is time-consuming and complex, particularly when located over navigable waters. State constitutional provisions, state statutes, and case law establish public and private rights and ownership in waters and the submerged lands. Opportunities exist to improve the right-of-way acquisition process for highways crossing navigable waterways, reducing the possibility of oversight or confusion that could result in multiples agencies and private property owners appearing to have a legal right of ownership in potentially overlapping portions of the right-of-way area with no clear way to sort it out beyond a court proceeding. This project provides six recommendations to improve timely identification of projects crossing state navigable waters and prevent project delays.


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POSTER 19-15:  SAFETY PERFORMANCE OF RURAL FOUR-LANE UNDIVIDED ROADWAYS AND RURAL FOUR-LANE ROADWAYS WITH A TWO-WAY LEFT-TURN LANE

PI: Jun Liu

Co-PI(s): Praveena Penmetsa, Ph.D. Timothy E. Barnett, P.E., PTOE Xiaobing Li, Ph.D. 

Institution(s): University of Alabama


Abstract

Non-traversable medians consistently yielded improved safety performance compared to other median types such as undivided, 4-feet flush medians, and two-way left-turn lane cross-sections. However, constructing non-traversable medians can be costly. The goal of this study is to 1) examine the safety performance of existing rural four-lane roadways with above-mentioned four median types in Georgia by using Safety Performance Functions (SPFs) and Crash Modification Factors (CMFs), and 2) develop criteria to determine under what conditions these four median types yield maximum safety benefits while considering construction costs. Data were refined and integrated from multiple sources such as from the Georgia Department of Transportation (GDOT), Federal Highway Administration (FHWA) and Google Maps. The Annual Average Daily Traffic (AADT), truck percentage, and access point density were considered as key independent variables in SPFs. The CMFs were estimated to show the effectiveness of a cross-section compared to the base- four-lane undivided roadway. Note, the SPFs and CMFs developed in this study did not consider the speed limit. The key results show that the estimated CMFs vary across different values of variables, indicating that the safety effectiveness of a treatment is likely to vary across different roadway and traffic conditions. Specifically, the segments with non-traversable medians outperformed the other three segment types across all AADTs, truck percentages, and access point densities, except at very low AADT under 5,000 where the 4-feet flush medians appear to have improved safety. The research team estimated average annual crash reductions (compared with undivided roadways), which were converted into monetary values using the average crash costs by severities. The safety benefits and project construction costs were used to estimate benefit-cost ratios (BCRs). Simulations were suggested in situations where safety solely did not help in the decision-making process of identifying cost-effective median type for rural four-lane roadways. It is highly recommended that decision-makers or practitioners use the estimated safety benefits from this study and the construction costs of a specific highway project to estimate BCRs for recommendations of the cross-section type.


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POSTER 19-13: COORDINATED ANTI-CONGESTION CONTROL ALGORITHMS FOR DIVERGING DIAMOND INTERCHANGES

PI: Samuel Coogan

Co-PI(s): Makhin Thitsa

Institution(s): Georgia Institute of Technology, Mercer University


Abstract

Diverging Diamond Interchanges (DDI) are a relatively new interchange design that moves traffic to the opposite side of the road at a freeway overpass. This allows vehicles to enter and exit the freeway via unimpeded left-hand turns. This project aims to develop traffic prediction and control algorithms to optimize traffic flows at Georgia?s DDIs and their adjacent intersections. Through robust data-driven methods, our team was able to develop traffic prediction algorithms for the coordinated control of the traffic flow networks around the DDIs. The algorithm?s predictions are especially valuable for signal timing optimization purposes on the days of unusual traffic trends where nominal parameters will fail to predict the future traffic flows. Our study shows that the use of the algorithm can save approximately 70 to 350 hours of delay, amounting to approximately $1,000 to $5,000 per day in travel time savings at the four intersections of the DDI in the network when flow is atypical.

Project Video


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POSTER 19-12: COGNITIVE ATTENTION AND ITS APPLICATION IN COUNTERMEASURES ON A CURVE SECTION

PI: Junan Shen

Co-PI(s): Yunfeng Chen, Purdue University Weinan Gao, Florida Institute of Technology 

Institution(s): Georgia Southern University


Abstract

This poster presented the research project of RP 19-12 which was supported by GDOT: Cognitive attention and its application in countermeasures on a curve section of rural road. The effectiveness of the common countermeasures on reduction of crashes was examined based on the information of the number of crashes, the eye movement indexes collected during simulation. Results indicated that the eye movement indexes from eye tracker can help understanding the effectiveness of those countermeasures.


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POSTER 19-09: ENTRUSTED ENGINEER-IN-CHARGE: A NEW CRITICAL POSITION IN THE DESIGN-BUILD TEAM

PI: Baabak Ashuri

Co-PI(s): 

Institution(s): Georgia Institute of Technology


Abstract

One of the main challenges that state departments of transportations (state DOTs) face in their design-build (DB) projects is to ensure that the design-build team upholds the highest standard of care in making complex engineering decisions involving multidisciplinary works. It is crucial to understand the underpinnings of engineering-related problems during both the design and construction phases and identify an effective approach to address these issues in the alternative delivery environment. This research aims to help the Georgia DOT (GDOT) Office of Innovative Delivery clearly define its expectations for the new position of project chief engineer (PCE). The overarching goal of this research project is to identify best practice guidance for defining GDOT?s expectations from the design-build team in proactive management of design-related issues. This study identifies gaps between GDOT?s expectations and the industry understanding of the PCE’s roles and responsibilities. Several emerging challenges related to the successful implementation of the new PCE position are discussed in the context of the dynamic design-build transportation market. The research team conducted content analysis and interviewed various professional groups, including state DOT officers, highway contractors, design consultants, owner’s representatives, legal experts, and insurance experts. To further discuss any issues related to insurability, the research team conducted a separate set of interviews with the insurance experts. The results show that, overall, interviewees agree that the PCE will add value for large and complex projects requiring multidisciplinary parties. The PCE requires a unique set of skills both in design and construction, and, therefore, finding an appropriate pool of candidates for the PCE position may be challenging for design-build teams. Several recommendations are made to enhance the description of the PCE role and responsibilities, in order to minimize any gaps in the understanding of the design-build industry professionals to fulfill this position. Not limited to the PCE position introduced by GDOT, the findings contribute to the DB and public-private partnership (P3) market to better understand the engineering decision-making process for the large and complex DB and P3 projects.

Project Video


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POSTER 19-05: BIRD-LONG ISLAND MANAGEMENT STUDY

PI: Jon Calabria

Co-PI(s): Clark Alexander Kevin Haas 

Institution(s): The University of Georgia


Abstract

The shoreline of an anthropogenically altered island is changing and threatens cultural and natural resources on the property that GDOT owns and intends to use for saltwater marsh mitigation. The first phase of the Bird-Long Island investigated shoreline movement over time, deployed spat sticks, documented vegetative alliances and quantified channel velocities (Phase 1A: Alexander, C. and Calabria, J. and Phase 1B: Haas, K.)[1]. Results from the first phase informed the second phase of design interventions that balance cultural and natural resources in light of environmental changes. Design interventions are proposed with unranked strengths and weaknesses that explain their efficacy. Several conceptual plans with passive and active strategies to enhance natural and cultural resources on this property are proposed.


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POSTER 19-01: IMPROVED DISASTER MANAGEMENT THROUGH AUTOMATED DAMAGE ASSESSMENT USING UNMANNED AERIAL VEHICLES (UAVS)

IMPROVED DISASTER MANAGEMENT THROUGH AUTOMATED DAMAGE ASSESSMENT USING UNMANNED AERIAL VEHICLES (UAVS)


PI: Rami J. Haddad

Co-PI(s): 

Institution(s): Georgia Southern University


Abstract

Natural disasters cause devastating effects on transportation networks by causing significant damage and obstruction on frequently traveled roads. This report describes the design and implementation of an automated Unmanned Aerial Vehicle (UAV) based damage management using convolutional neural networks (CNNs). This system utilizes image processing and deep learning techniques to assess damages to the state’s transportation system. The assessed damages are automatically geo-tagged to an ArcGIS map compatible with the Georgia Department of Transportation (GDOT) GIS standards. This UAV-based intelligent disaster management system enables the GDOT to optimize its disaster management and recovery efforts. Additionally, this system provided live streaming of the UAV’s video feed to an RTMP server, enabling the first responders to assess damages. The system is composed of hardware and software components. In addition to the UAV platform, a customized application was developed using Python and MATLAB software to automate and centralize the operation of this system. The application included managing, sampling, classifying, and ArcGIS map tagging of the UAV-generated video streams. The simulation results of this system, using a library of images, have shown that the system could classify clear vs. damaged roads with an accuracy of over 99%. However, when the classification categories increased to six, damaged roads, clear roads, blocked roads, boat in roads, fallen power lines, and flooded roads, the average classification accuracy dropped to 74.1%. This was mainly due to the relatively small size of the library of disaster-related images.


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POSTER 19-11: SAFETY AND ILLUMINATION OF RURAL AND SUBURBAN ROUNDABOUTS PHASE II

PI: Michael Rodgers

Co-PI(s): 

Institution(s): Georgia Institute of Technology


Abstract

This project focused on establishing the relationship between the presence/absence or levels of illumination and other geometric and traffic characteristics on nighttime safety at rural and suburban roundabouts. Eighty roundabouts from 37 counties across Georgia were selected to provide a wide range of conditions in terms of illumination layout, illumination levels, number of legs, number of circulating lanes, daily entering volumes, approach speeds, etc. for field measurements of illumination levels. Urban roundabouts with significant pedestrian activity were specifically excluded. Field data collection at each site included both direct measurements of illumination levels as well as a civil site survey to verify the geometric characteristics of the roundabout and were conducted by measurement teams from Georgia Institute of Technology and Georgia Southern University. The resulting data were processed, joined, and aggregated to the individual site level and used to establish statistical relationships between observed nighttime crash rates, severity, and crash types (e.g. single vs. multiple vehicles, impaired drivers, etc.) and underlying geometric factors and measured illuminance levels. The variation in observed crash rates were modeled against known parameters of the roundabouts to develop a predictive model as to how single vehicle nighttime crash rates were impacted by illumination and other factors. As expected, multiple vehicle crashes showed no statistically significant dependence on illumination levels as the vehicles themselves, through their head- and taillights, are important contributors to nighttime visibility at the roundabout. This was not the case for single vehicle crashes. Single vehicle crashes were shown to increase for 3-leg roundabouts for illumination values less than 5 lux. No such trend was observed in either 4 or 5-leg roundabouts and these sites showed no statistically significant variation in nighttime single vehicle crash rates at any level of illumination. An overarching conclusion of the study is that there was no observed evidence of illumination values in excess of 5 lux resulting in a statistically significant reduction in nighttime crash rates for rural and suburban roundabouts. These results suggest that for rural and suburban roundabouts with no significant pedestrian volumes, illumination values significantly lower than current standards may still prove effective as a safety treatment and that, in the absence of a need to protect pedestrians or cyclists at nighttime at a particular location, a reduction in lighting levels or the use of passive retroreflective safety treatments may be a cost-effective treatment.


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POSTER 19-14: EXTRACT HORIZONTAL AND VERTICAL ALIGNMENT INFORMATION FROM PUBLIC DATA AND OPEN API

EXTRACT HORIZONTAL AND VERTICAL ALIGNMENT INFORMATION FROM PUBLIC DATA AND OPEN API


PI: Xiaoming Yang

Co-PI(s): Russell Krenek, and David Scott

Institution(s): Georgia Southern University


Abstract

An open-source program was developed for extracting horizontal and vertical alignment information of roadways using public data and open APIs (application programming interface). The program employs unsupervised analysis to detect, segment, classify, and measure curves and tangents along a given route, based on the coordinates of the beginning and ending points. Erratic vertical grade data points at bridges were detected. A validation case is provided based on a 23.4-km Interstate highway route in Georgia. A visual inspection of the result showed that the program was able to detect and measure all horizontal circular curves along the sample route. Moreover, the program correctly identified the vertical curves and vertical tangents for more than 90% of the length of the route. Occasional inaccuracies in the vertical alignment analysis were observed, mainly at short segments near bridges. The program was developed in the R language. The complete source code of the program is publicly accessible via a dedicated GitHub repository.


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