This session contains presentations includes safety, maintenance, management and preservation projects that complement and supplemental to the AASHTO Sweet Sixteen - high-value research projects.
Introduction to AASHTO RAC High Value Research Projects
Susan Sillick, Montana Department of Transportation
Active Transportation Accounting: Developing Metrics for Project Prioritization
Amy Villamagna, Plymouth State UniversityShow Abstract
Despite growing interest and momentum in enhancing active transportation, little weight is currently given to active transportation projects. This is largely due to the lack of sufficient data. In order to identify key areas for active transportation enhancement, to justify investment, and to measure success, it is necessary to understand where and when people are participating in active transportation (e.g. bicycling). This project leveraged a) existing datasets (NHDOT roadways, Strava bicycling data, and crash reports), b) statewide on-the-ground bike counter, c) efforts to develop and apply a Level of Traffic Stress (LTS) model for bicycling and incorporate novel public participatory GIS approaches to assess patterns of current bicycle activity and identify potential barriers to access and participation throughout New Hampshire. More specifically, the project assessed the reliability of Strava data to reflect biking activity in New Hampshire, evaluated the ability of Level of Traffic Stress to predict biking patterns and barriers to active transportation, evaluated perceived barriers to active transportation (e.g. safety concerns) against objective physical barriers as reflected in LTS model, and evaluated the accuracy of current LTS model using public participatory GIS. In addition, the project produced a suite of tools to be used in ArcGIS (10.3 and greater). The report offered recommendations to NHDOT for future data collection and management in order to improve and standardize statewide efforts to monitoring bicycling patterns and to map the level of bicycling traffic stress on all roadways.
Snow and Ice Control Performance Measurement: Comparing “Grip,” Traffic Speed Distributions and Safety Outcomes During Winter Storms
James Sullivan, University of VermontShow Abstract
Jonathan Dowds, University of Vermont
Road surface conditions and vehicle speeds capture important factors that influence mobility and traveler safety during and after a winter storm event. Vaisala’s proprietary “Grip” measure provides an imputed measure of the condition of the road surface. The Average Distribution Deviation (ADD) measures changes in the distribution of vehicle speeds during and after winter weather events, capturing the traveling public’s response to their perception of road surface conditions. The objective of this project was to gain a better understanding of the relationship among Grip, speed and adverse safety outcomes. To identify high risk periods, the research team extracted events where the ADD was within the normal range and Grip was compromised. These cases indicate that speed distribution of the traffic stream did not differ from the typical speed distribution for clear days but that road conditions were degraded. High-risk days, corresponding to a day and a location when the ADD and the Grip readings were inconsistent with one another, were compared to crash and Vermont State Policy incident records. High-risk days identified in this research, showed a strong co-occurrence with crashes and other snow and ice-related incidents, increasing the risk of one of these adverse outcomes by 3-4 times. However, this conclusion is based on a very limited set of data for the winters of 2016-2017 and 2017-2018, so more research is needed to support this conclusion as well as to determine whether a lower ADD threshold should be used to identify high-risk period. If the ADD-Grip discrepancies can be used to predict crashes, then this finding could be extremely useful for winter traffic safety in Vermont. For example, a programmable message board, linked to the real-time calculation of the ADD-Grip discrepancy, could communicate poor Grip situations with greater urgency added when the ADD indicates that current speeds are not safe.
Adaptation of 3D Scanning Technology for High Precision Bridge Inspection—Connecticut DOT
Andrew Mroczkowski, Connecticut Department of TransportationShow Abstract
Alexandra Hain, University of Connecticut
Arash Esmaili Zaghi, University of Connecticut
Current bridge evaluation methods are labor intensive and dependent on subjective assessments by inspectors. Inspection outcomes have a substantial impact on the safety assessment of bridges and prioritization of repairs. The goal of this research was to use commercially available 3Dscanning technology to provide high‐accuracy, objective data for key inspection applications such as section loss assessment for steel beam ends. 1. Identified scanner suited to application in bridge inspection and conducted lab trialsto develop workflow. 2. Conducted five field trials on brides in CT. 3. Currently finalizing training materials (videos& manuals) for use by CTDOT. 3Dscanning can be used to produce accurate3Dmodelsthatrepresent current conditions. CTDOT has immediate plans to use the technology and methodology from the research to:
In the future, CTDOT plans to evaluate additional use cases including:
Potential Impacts and Benefits: Improving the accuracy of the underlying measurements from bridge inspections will help inform decisions regarding load postings, prioritization of rehabilitation projects, and the allocation of retrofit funds.
Real-Time Signal Performance Measurement (RT-SPM)
Peter Jin, Rutgers UniversityShow Abstract
Thomas Brennan, College of New Jersey
Traffic signal performance measurement and visualization provide insights as operational tools to help traffic management centers get more benefits from infrastructure investment. Automated Traffic Signal Performance Measures (ATSPM) system uses high-resolution (0.1 sec) data to support the data-driven decision-making process and allows consistent and dynamic monitoring of signal-controlled intersection. This project developed the ATSPM system considering existing implementation options according to agency capabilities and resources. The research team specifically designed the system based on Adaptive Signal Control Technology (ASCT) and ATSPM open-source software to develop an economically justifiable ATSPM for arterial traffic management in New Jersey. In the standard ATSPM deployment, high-resolution signal controllers are needed to generate event data for signal performance measures and identify deficiencies requiring maintenance. Upgrading the existing controllers and reconfigure the controller interfaces for signal event data transferring will require significant funding and labor hours. This project provides an efficient and scalable approach to deploy ATSPM at Traffic Management Centers with existing Adaptive Traffic Signal Control (ATSC) systems without the need for intersection-level hardware upgrade and reconfiguration. This research was conducted to close the gap between ATSC system operations and traffic signal performance monitoring. The team is working with the New Jersey Department of Transportation on a Phase 2 project that will incorporate detection events, conduct state-wide deployment, and potentially support Connected Vehicle Applications.
Validating Change of Sign and Pavement Conditions and Evaluating Sign Retro-reflectivity Condition Assessment on Georgia’s Interstate Highways Using 3D Sensing Technology
Yichang Tsai, Georgia Institute of Technology (Georgia Tech)Show Abstract
Erany Robinson-Perry, Georgia Department of Transportation
Brennan Roney, Georgia Department of Transportation
This research focused on assessing and validating the change of traffic retro-reflectivity sign conditions and the change of pavement surface distress conditions by using the data collected on Georgia Interstates in 2015 and 2018. Light detection and ranging (LiDAR) and 3D Line Laser imaging (3D laser) technologies were integrated in the Georgia Tech Sensing Vehicle to collect the traffic sign and pavement data simultaneously. A new categorical traffic retro-reflectivity sign condition assessment method was developed using mobile LiDAR and video images. GDOT’s inspectors’ traffic sign retro-reflectivity condition field assessments validated the new method. Among 338 selected signs assessed on the I-285, 67% of the signs were classified in a good condition, and 33% of the signs were classified in a “poor” or “uncertain” condition. This new method could potentially reduce inspection time by 60% and can be implemented practically. Based on the 4 years retro-intensity deterioration analysis, promising trends for LiDAR retro-intensity change can be observed for the traffic signs. This analysis can be used to further establish a forecasting model for sign replacement planning.
To analyze the change/deterioration of pavement surface distress conditions, the 2018 pavement condition evaluation data (collected using 3D laser) was compared with the data collected in 2015. The 3D laser data has demonstrated to be accurate, reliable, and consistent. This pavement condition evaluation covers asphalt pavement distresses, jointed plain concrete pavements’ slab-base severity level classification, and International Roughness Index measurements.
Sign Life Expectancy
Nathan Huynh, University of South CarolinaShow Abstract
Terry Swygert, South Carolina Department of Transportation
In 2007, the Federal Highway Administration adopted new retroreflectivity standards for traffic signs to be implemented by agencies in their jurisdictions. To ensure traffic signs meet the required retroreflectivity standards, South Carolina Department of Transportation (SCDOT) adopted a sign replacement strategy to replace signs before the 10 year warranty end date. This project investigated the expected life of traffic signs managed by SCDOT to determine the appropriate sign replacement interval. Based on the modeling results, it was recommended that SCDOT extend the sign replacement intervalto 12 years. This conclusion was supported by both SCDOT historical sign replacement data and measurements taken from older signs. In addition, it was recommended that SCDOT incorporate sign washing in future sign maintenance activities to extend the life of signs. A simple wash with a glass cleaner and paper towel showed an average improvement of 22.5%; this equates to a lengthened life span of about two years for yellow, white, and green signs and about five years for red signs.
Evaluation of the Smart Work Zone Speed Notification System
Brian Kary, Minnesota Department of TransportationShow Abstract
John Hourdos, University of Minnesota, Twin Cities
Maintenance and construction on Minnesota’s roadways often create travel disruption for drivers through traffic slowdowns and queuing. MnDOT has previously tested systems to inform drivers of traffic backups in rural work zones, but slowdowns near complex urban work zones are less predictable. Drivers traveling at highway speed may come upon congestion suddenly, resulting in abrupt braking and the risk of rear-end collisions.
The Smart Work Zone Speed Notification (SWZSN) system was designed to collect traffic speed data throughout a work zone and run it through an algorithm, generating the appropriate message for drivers on a variable message sign, such as “35 MPH 1 Mile Ahead” or “Stopped Traffic Ahead.” MnDOT wanted to deploy the system within a project replacing 4.4 miles of Interstate 94 (I-94) east of downtown St. Paul. The construction was to be completed in stages between spring 2016 and fall 2017. This large project would include many lane closures and expected traffic congestion; the new system could mitigate some of the traffic disruption. The deployment and evaluation of the SWZSN took place in three phases: Pre-SWZSN deployment (mid-2016), Post-SWZSN Phase I deployment (2016 into 2017), Post-SWZSN Phase II deployment (the entire 2017 work season). To collect data for the system, MnDOT’s Regional Transportation Management Center mounted Wavetronix speed detection sensors on poles every half-mile in the work zone, replacing old loop detectors. Researchers also deployed nine solar-powered cameras on mobile trailers about every half-mile. This allowed researchers to capture traffic flow images in more strategic locations where traffic queues were forming since the construction zones were complex, crowded and often changing, with many visual obstructions. Data were transferred primarily via an arranged wireless radio link to the Minnesota Traffic Observatory. Researchers then applied their own innovative methodology—a Trajectory Extraction Tool (TET)—to the traffic images captured by the cameras using video alone to calculate a vehicle’s deceleration rate when approaching traffic congestion. The cameras were positioned to optimize TET performance. Researchers gathered tens of thousands of data points for analysis from traffic in the work zone over the course of the project.
Evaluation of Automated Flagger Assistance Devices
Henry Brown, University of Missouri, ColumbiaShow Abstract
Automated flagger assistance devices (AFADs) are designed to improve worker safety by replacing flaggers who are typically located near traffic approaching a work zone. The objective of this project was to evaluate the effectiveness of a new AFAD developed by the Missouri Department of Transportation (MoDOT).
The MoDOT AFAD configuration, involving STOP/SLOW paddles, Red/Yellow lights, and a changeable message sign (CMS) was incorporated onto a truck‐mounted attenuator for operator protection. The scope of this project included two phases: a field test with CMS and a simulator study (both with and without CMS). The two phases were each followed by a survey that captured driver preferences and understanding. For the first time in the United States, detailed quantitative driver behavior measures were used to compare the effectiveness of human flaggers versus AFADs.
High-Resolution Micro Traffic Data from Roadside LiDAR Sensors for Connected-Vehicles and New Traffic Applications
Hao Xu, University of Nevada, RenoShow Abstract
New traffic systems and applications, such as connected and autonomous vehicles and near‐crash analysis, require traffic flow information with more details and higher accuracy ‐ specifically, all‐traffic trajectories not provided by traditional traffic sensors. 360‐degree light detection and ranging LiDAR sensors can provide high resolution data because they detect surrounding objects with high accuracy and frequency and are not influenced by light conditions. The project team explored LiDAR sensors to solve this need and developed algorithms specifically for roadside LiDAR sensing systems. Due to sensor installation and data characteristics, methodologies for roadside LiDAR data processing are different from the methods used in autonomous vehicles technologies.
The project team developed a procedure for roadside LiDAR data processing, including major steps of background, object clustering, identification of road user types, tracking road users in different data frames, and output of traffic trajectory data. This project developed a procedure, including multiple algorithms, for generating high accuracy multimodal traffic trajectories with roadside 360‐degree LiDAR sensors. The developed data‐processing procedure first excludes points of background objects such as road surface, trees, and poles; then, it clusters left points into objects that are multimodal travelers; it further classifies those objects into pedestrians, vehicles and other road user types. The procedure calculates each road user’s location with x‐y‐z coordinates of clustered points and estimates their speeds based on time difference and location change in continuous data frames. Finally, trajectories including road user type, location, speed, and direction information are obtained. To demonstrate applications of roadside LiDAR, this project applied trajectories from roadside LiDAR for prediction of pedestrians crossing roads, pedestrian‐vehicle near‐crash analysis, and detection of wildlife animals crossing highways. The major achievements of this project are summarized below: 1) Development of an automatic LiDAR background filtering algorithm 2) Development of an algorithm to extract trajectories of road users from roadside LiDAR data 3) Development of an algorithm to identify different road users with roadside LiDAR data 4) Development of an integrated procedure for processing high‐resolution cloud points from roadside LiDAR and extracting multimodal traffic trajectories 5) A pilot application of roadside LiDAR to detect and predict pedestrians crossing roads based on LiDAR trajectory data 6) A pilot application of roadside LiDAR to define and extract near‐crash events 7) A pilot application of roadside LiDAR to detect wildlife animals crossing a highway.
Evaluation of New Rumble Strip Designs to Reduce Roadside Noise and Promote Safety
Jon Peterson, Washington State Department of TransportationShow Abstract
Jim Laughlin, Washington State Department of Transportation
John Donahue, Washington State Department of Transportation
Rumble strips are an effective countermeasure to keep vehicles on the roadway and reduce the frequency of crashes. Drivers are alerted by the noise and vibration within the vehicle caused by the uneven rumble strip surface. Noise related to incidental contact with rumble strips outside the vehicle can be a source of disturbance and the cause of complaints from roadside residents. The objective was to identify a rumble strip design that minimizes external noise while maintaining effectiveness at alerting the driver.
Four potential rumble strip pattern designs were identified and installed at test locations around the state. Patterns tested included three modifications to longstanding milled designs, and one sinusoidal design. Measurements of sound volume and characteristic were collected in both the interior of the test vehicle, as well as at 25 ft. and 50 ft. distance from the rumble strip. Results were compared and evaluated with respect to national guidance for optimal interior volume required to alert the driver, and a minimum threshold value for external noise quality and volume associated with disturbance at adjacent properties.
Iowa DOT Snowplow Optimization
Jing Dong, Iowa State UniversityShow Abstract
The current routes for Iowa Department of Transportation (DOT) District 3 are designed based on staff knowledge and past experience. Optimizing these routes may help reduce service distance and deadhead distance (i.e., the distance a truck travels while not performing maintenance service) and allow the routes to more efficiently meet service expectations.
District 3 staff provided information on the district’s current winter maintenance operations, including the current snowplow routes, area of responsibility maps, and fleet sizes and compositions for the district’s 20 depots. Automatic vehicle location (AVL) data for the trucks servicing District 3 were used to characterize the district’s current operations. The total travel distances and turnaround points were calculated based on data from a test run conducted for this study in December 2017. The service speeds, deadhead speeds, and spreading rates were calculated based on data from three winter storm events in 2017 and 2018. The Iowa DOT’s Roadway Asset Management System (RAMS) was used to build the traffic network for this study. Information was compiled on
roadway facility types and service levels and the winter maintenance system. The traffic network was manually processed to further characterize the serviced roads and to associate service and deadhead speeds with service road segments. Four practical constraints were considered in this study: truck capacity, or the amount of material each truck can carry; the size and composition of the fleet assigned to each depot; road-truck dependency, or the requirement that specific roads be serviced by specific types of trucks; and road segment cycle time, or the frequency at which different roads must be serviced. Two sets of optimized routes for the district were designed by estimating models to solve two problems, each with the objective of minimizing total travel distance. Both optimization problems were solved as capacitated arc routing problems (CARPs) using a memetic algorithm (MA) and considering the constraints noted above. The first was a single-depot winter maintenance routing problem, where one depot at a time was considered and the routes were optimized for the district’s current responsibility maps. In addition, a parallel metaheuristic approach was developed to improve solution quality and computational efficiency. To explore how the spreading rate might change the optimized routes, a sensitivity analysis with regard to the spreading rate was also conducted. The second was a multiple-depot winter maintenance routing problem with reload/intermediate facilities, where the depot boundaries within each of the district’s six sectors could be redesigned and each truck could reload at any depot or reload station (if any) within the sector.
Initial Analytical Investigation of Overhead Sign Trusses with Respect to Remaining Fatigue Life and Predictive Methods for Inspection
Hayder Rasheed, Kansas State UniversityShow Abstract
Most state highway agencies do not perform routine fatigue inspection on highway signs, luminaires, and traffic signals, thereby increasing the potential for unnoticed fatigue cracking. The Kansas Highway System utilizes over 450 sign trusses, most of which have been in service for 30‐45 years. In addition to aging support structures, the structural designs of these signs and signals sometimes result in significant cyclical loading due to wind gusts. This study was conducted to investigate the behavior of the structures and develop a software that is capable of estimating the fatigue life based on daily wind speed fluctuation.
This study investigated the possibility of estimating the remaining fatigue life for each aluminum element according to AASHTO LRFD wind load combinations. Fatigue Life Simulator Software (FLSS) was developed to work compatibly with STAAD.Pro software and Sign Truss Interface, a program used by KDOT to simulate wind pressure, to determine fatigue life for any model of structural support system in the state of Kansas. Fatigue evaluations were conducted using nominal axial member‐specific stress ranges corresponding to a wind speed database for a 45‐ year period, as well as hundreds of structural analysis simulations. Potential fatigue failure was assessed for each member of the support structure by evaluating the ratio of consumed fatigue cycles to ultimate fatigue cycles using Miner’s rule to estimate finite life. Fatigue life simulation software (FLSS) successfully predicted fatigue cracking in two members of a truss sign structure in Wichita KS upon its implementation by KDOT. The actual structure showed fatigue cracking at the same two locations indicated by the software upon field inspection, which were otherwise unnoticed.
Reducing Long-Term Consolidation Settlement from New Embankments
Steven Bartlett, University of UtahShow Abstract
David Stevens, Utah Department of Transportation
Primary consolidation, or the short-term soil settlement associated with addition of a bridge-approach embankment or building load, is the main soil compression concern during construction. However, secondary compression of the underlying soils can cause long-term settlement damage to bridges, their foundations and approach embankments, overlying pavements, and other nearby constructed works. Surcharging or preloading of earthen embankments and underlying compressible soils with additional, temporary embankment fill is the most commonly deployed strategy to reduce the magnitude of secondary compression that may occur after construction.
A research team from The University of Utah collected and analyzed soil consolidation data from a few past project sites and prepared a research report that discusses the design and implementation of surcharging technology in terms of the required laboratory, field, and engineering evaluations. In addition to the past projects’ data that was analyzed, additional laboratory consolidation tests and time-rate tests were performed on fine-grained, cohesive soil samples from a few past project sites located along the Wasatch Front in Utah.
Bio-Based Renewable Additives for Anti-Icing Applications
Xianming Shi, Washington State UniversityShow Abstract
Mehdi Honarvar Nazari, Washington State University
This project identified the performance and impacts of 21 anti-icer mixtures that were designed using the central composite design method based on the preliminary experiments of the authors.
Selected constituent materials pose minimal toxicity to the environment (e.g., no heavy metal content) and were derived from eco-friendly, cost-effective processes. Agro-based solutions derived from locally sourced agro-based materials mixed with salt brine, and commercial additives (with little toxicity) were tested for their ice-melting capacity, ice-penetration rate, ability to protect asphalt binder and concrete, effect on the friction coefficient of deiced and anti-iced asphalt pavement, and anti-corrosion performance. The main criterion for choosing the best-performing anti-icer was ice-melting capacity. A decision making process based on an analytical hierarchy process (AHP) was used to determine the best-performing anti-icer. The best-performer anti-icer mixture.
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