MATS UTC researchers at the University of Virginia and Old Dominion University are studying electric vehicle adoption patterns and subsequent air quality impacts in the Commonwealth of Virginia. We are looking for your input! Complete our survey (estimated to take 15-20 mins) and tell us whether you are interested in a “green vehicle” for your next vehicle purchase.
When you purchase your next vehicle, will you consider buying an electric vehicle (EV)? If you already own an EV, are you wondering about the future of EV ownership and how increased usage might impact the number and placement of charging stations? Is increased EV use actually having a positive environmental impact?
Researchers at the University of Virginia (UVA) and Old Dominion University (ODU) are taking these issues seriously. Funding from MATS UTC is enabling them to model anticipated EV adoption patterns, giving them the ability to predict which households in which neighborhoods are most likely to own such vehicles. Combined with influence factors such as technology familiarity, vehicle availability, charging infrastructure provision and demographics, this predictive model could play a vital role in informing public policy related to power-grid planning, transportation investments and air quality strategies.
Taking a two-pronged research approach, the researchers are assessing both revealed preferences (RP) and stated preferences (SP) to understand how various factors influence vehicle choice. T. Donna Chen, PE, PhD, department of civil and environmental engineering at UVA, along with PhD student, Wenjian Jia, and Rajesh Paleti, PhD, department of civil and environmental engineering at ODU, are analyzing vehicle registration data (2008 to 2016) from the Virginia Department of Motor Vehicles (DMV) to develop a snapshot of RP related to actual adoption and use among regional EV owners. In the spring of 2018, survey data will be collected to determine SP toward vehicle traits, land use and EV infrastructure. A predictive spatial model is being developed based on this individual and geographic level data to better understand the impact of various regional factors and household demographics on EV adoption.
The research team is working with Virginia Clean Cities, a non-profit organization focused on reducing reliance on gas-powered vehicles, to reach out to EV owners and stakeholders. “We’re directly engaging with consumers and industry, developing an applications-based approach to help transportation planners, utility providers and public policy professionals make informed planning decisions based on when and where new EV households will emerge,” explained Chen. “These results may also lead to better insights about air quality impacts and the effect of EV adoption trends on the carbon footprint in Virginia.”
These findings will help to create a more accurate profile of EV adopters in Virginia. Ultimately, the modeling capabilities could provide a decision framework to determine the most effective placement of charging stations as well as the effects of various policies, such as rebates or usage fees, on actual EV adoption and use.
For more information, contact Dr. Chen at email@example.com or 434-924-6224.
With increasing frequency, the public is exposed to news coverage announcing the autonomous vehicle (AV) revolution and the ways in which mobile devices, sensors and other connected vehicle (CV) technologies are redefining the automotive world. As engineers and other specialists contend with the technical aspects of AVs and CVs, society grapples with questions about safety, reliability and other potential effects on personal mobility.
Connected vehicles leverage a number of communication technologies, often sensors and mobile devices, to communicate with the driver, other vehicles and roadside infrastructure. Autonomous vehicles, often referred to as driverless or self-driving cars, are capable of sensing their surrounding environment and reacting without human input. Together, CVs and AVs represent the leading edge of innovative solutions to address congestion, environmental concerns and traffic safety. However, they also represent complex public policy issues related to social, economic and consumer impacts.
Researchers within the MATS UTC consortium are seeking to understand how ever-evolving CV and AV technical advances can be leveraged for more efficient and safe use of the built environment. CVs and AVs represent the opportunity to gather real-time mobile data, revealing important traffic information and driving behaviors not available through existing monitoring devices such as stationary cameras. Many of these research efforts are being applied to existing traffic issues, such as safety and movement through intersections, and environmental concerns, such as fuel economies and emissions.
Examples of these research efforts include:
Researchers at the University of Delaware and Morgan State University are using connected vehicle technology to optimize a vehicle’s control system in real-time to reduce congestion, improve fuel economy and reduce emissions. Using hybrid buses operating at the University of Delaware, the team is studying how intelligently integrated components can respond to both routine and atypical traffic situations, resulting in optimized traffic control and vehicle fuel economy.
Communication between a traffic signal controller and a vehicle equipped with a global positioning system (GPS) and communication hardware provides a research team from Virginia Tech and Morgan State University with sufficient information (vehicle position, vehicle speed and signal phasing and timing data) to compute fuel-efficient speeds. The team is leveraging this communication, developing Eco-Speed Control algorithms for buses using predictive energy estimation models. These models identify optimum speed profiles using information from surrounding vehicles and upcoming signalized intersections. The goal is to predict the most efficient speed to move a bus through an intersection and reduce ‘stop/go’ behavior, a key reason for inefficient fuel economy.
Collecting high-resolution speed and acceleration data is now feasible with mobile consumer devices such as smartphones. Smartphones are equipped with sensors capable of recording vehicle performance data at a very fine temporal resolution in a cost-effective way. Researchers at Old Dominion University used this mobile sensor data to identify unsafe driving patterns and quantified the relationship between these driving patterns and traffic crash incidences. The models with microscopic traffic measures were shown to be statistically better than traditional models that only control for roadway geometry and traffic exposure variables.
Research conducted at Virginia Tech seeks to develop a dynamic speed harmonization application (SPD-HARM) that makes use of the frequently collected and rapidly disseminated multi-source data drawn from connected travelers, roadside sensors, and infrastructure. Using the connected vehicle environment, the research team is developing systems and algorithms to generate traffic condition predictions, alternative scenarios and solution evaluations in real-time. The goal is to reduce crashes, whether due to speeding, poor visibility, inclement weather or construction activities.
With growing use of connected vehicles equipped with communication technologies such as GPS to communicate with the driver, other cars and roadside infrastructure, researchers at Old Dominion University, Virginia Tech and Marshall University are exploring optimization of current adaptive signal control technology to estimate queue length and develop enhanced signal coordination through communication with CV sensors. The research focuses on Traffic Responsive Plan Selection (TRPS), an underutilized adaptive control product enabling the selection of pre-programmed traffic signal timing plans based on vehicle demand observed from selected vehicle detectors along a signalized corridor.
Autonomous vehicles are typically equipped with LIDAR (light detection and ranging remote sensing technology) or other similar sensors to detect obstacles in the surrounding environment and can be a means to track other vehicles in adjacent lanes. At Old Dominion University, LIDAR is being used to estimate traffic flow parameters along the path of the autonomous car from point-cloud data. New algorithms and models are in development to extract traffic flow information from raw LIDAR data, enabling real-world data collection for safety studies and estimations of traffic flow and driving behavior.
In an automated environment, it is possible bikers and pedestrians will be safer due to improved braking technologies. However, safety may be negatively impacted if drivers, cyclists and pedestrians over-rely on automated technology. If, for example, pedestrians and cyclists assume AVs will ‘automatically’ stop for them, then there may be increases in unsafe walking and cycling behaviors such as jay-walking or failing to use designated bike lanes. Researchers at Virginia Tech and the University of Virginia are using semi-structured interviews with various stakeholders to develop planning guidelines for walking and cycling as society transitions to an automated fleet.
These research efforts are contributing to our understanding of the challenges associated with dynamic traffic conditions in an automated and connected environment. Most importantly, these projects are using real-time traffic data to develop new approaches, such as optimized traffic signals and other traffic responsive systems, to reduce fuel consumption, improve safety, and minimize congestion.
Prior to pursuing a PhD in geotechnical engineering at the University of Delaware, Will Baker logged significant hours on construction sites across the northeast as an intern and co-op student with Duffield Associates. He gained valuable experience performing soil sampling, and field and laboratory material testing. This hands-on exposure to geotechnical engineering, a branch of civil engineering that investigates the engineering behavior of soil and rock, helped to determine Baker’s academic future.
Baker is now pursuing his PhD under the guidance of advisor, Christopher Meehan, PhD, associate professor in civil and environmental engineering at the University of Delaware. Together, they see the promise of intelligent compaction (IC) and continuous compaction control (CCC), using sensors and spatial mapping, to radically improve the foundations upon which earthwork construction projects are built. The underlying principle is that a denser soil is stronger soil. Using current compaction equipment, such as drum rollers, inspectors rely on simple calculations, density gauges and spot tests to determine if the compaction passes/fails. Meehan and Baker are exploring how IC and CCC can provide real-time monitoring of the compaction process to provide 100% analysis of the prepared site and reduce reliance on random sampling.
Serving as a graduate research assistant and undertaking his PhD research, Baker is developing empirical models utilizing machine-learning techniques to better understand the relationship between CCC measurements and in situ measurements. He continues to work on active project sites to analyze the compaction process with a compaction roller instrumented with a CCC system and is working to establish geospatial and statistical characteristics of CCC measurements during active construction.
“Will’s work on developing innovative techniques for effective quality assurance and quality control of soil compaction has the potential to yield a better finished product for constructed roadways at reduced cost,” noted Dr. Meehan. “His discoveries will help to advance our understanding about using machine feedback to guide decision making in a field environment, which will encourage the use of cost saving automation in construction. In addition, Will’s work ethic as a graduate student is second to none, and he is a real pleasure to work with.”
Baker and Meehan presented some of their findings at the 2016 MATS UTC annual meeting. Their poster, Utilizing a neighboring weighted-estimate method for outlier detection with a continuous compaction control data set, described the correlation of CCC readings with traditional in situ spot test results to develop target values for compaction, working toward the compaction roller as a QC/QA tool during the construction process.
MATS UTC has taken notice of Baker’s research progress, recently naming him ‘2017 Student of the Year’. “Will’s contributions to MATS UTC-funded research on CCC and soil compaction, as well as his leadership activities with other students, warranted the recognition,” stated MATS UTC Managing Director, Lindsay Ivey Burden.
Baker is currently the chair of the GeoCongress Planning Committee, organizing a leadership workshop for students at the national GeoCongress Conference in March, 2018. He will moderate a panel discussion with industry representatives from academia, governing agencies, consulting and more; providing students with opportunities to learn about career opportunities and industry trends from leaders in the geotechnical community.
Upon graduation, Baker intends to pursue his interests in soil compaction either in an academic or field setting. “Site preparation needs to be done correctly right from the start of the project,” explained Baker. “No matter whether a construction site is large or small, it’s hard to manage soil yet it’s the foundation of a safe and sustainable construction project. I’m excited to contribute to our understanding of soil preparation and the practical use of CCC technology on-site.”
He received a bachelor’s degree in civil engineering from the University of Delaware and expects to receive his PhD in geotechnical engineering in 2020. Baker may be contacted at firstname.lastname@example.org. Meehan may be contacted at email@example.com.
Selected publications include:
Meehan, C. L., Cacciola, D. V., Tehrani, F. S., and Baker, W. J. (2017). “Assessing Soil Compaction Using Continuous Compaction Control and Location-Specific In Situ Tests.” Automation in Construction, Elsevier, 73, 31-44.
Baker, W. J. and Meehan, C. L. (2017). “Utilizing a Neighboring Weighted-Estimation Method for Anomaly Detection with a Continuous Compaction Control Data Set.” Proc., Geotechnical Frontiers 2017: Transportation Facilities, Structures, and Site Investigation, Geotechnical Special Publication No. 277, Orlando, FL, March 12-15, 2017, ASCE, Reston, VA, 55-65.
Cacciola, D. V., Meehan, C. L., Baker, W. J., and Tehrani, F. S. (2018). “A Comparison of Continuous Compaction Control Measurements with Localized In Situ Test Results.” 2018 ASCE Geo-Congress (pending).
Baker, W. J. and Meehan, C. L. (2018). “A Comparison Between of In-Place Unit Weight and Moisture Content Measurements Made Using Nuclear Based Methods and the Drive Cylinder Method.” 2018 ASCE Geo-Congress (pending).
Camera configuration a) diagram of optical setup and b) field of view, speckle pattern and subsets
It is estimated the average age of bridges in the U.S. is approaching 45 years, suggesting many of these structures may be in a state of disrepair and perhaps even reaching the end of their functional lives. In addition to age-related deterioration, these structures are exposed to weather and environmental hazards, further affecting their longevity. Take, for example, high-profile infrastructure systems in the Hampton Roads region in Virginia. The Hampton Road Bridge Tunnel and the Chesapeake Bay Bridge are vulnerable to extreme weather events such as hurricanes, sea level rise, exposure to salting during snowstorms and underside exposure to saltwater spray. These environmental hazards cause corrosion and eventually cracking which impact the long-term performance of the structures.
To assess infrastructure for maintenance and repair, structural health monitoring (SHM) is an assessment strategy undertaken to determine the location, severity, and progression of damage. SHM is actually not used frequently and is primarily deployed on high-profile structural systems. Devin Harris, PhD, Associate Professor in the Department of Civil and Environmental Engineering at the University of Virginia, is tackling the problem with a fresh approach.
Harris believes image-based structural health monitoring (iSHM) can be a powerful tool for assessing condition and structural behavior, leveraging vision-based sensing techniques for describing the operational behavior of structural systems. With MATS-UTC funding, he is evaluating the laboratory performance of the iSHM concept. Using standard structural shapes under variable boundary conditions, Harris subjects a representative steel beam to a series of loading configurations, simulating real-world stresses on the structure, with measurements captured using high-resolution cameras. As the beam deforms under the various loads, the contrasting pattern painted on the beam also deforms proportionally. During testing, the cameras capture images of the behavior of the beam as it strains, rotates, deflects or deforms, which are then translated into full-field measurements of these phenomena using a technique called digital image correlation (DIC). These measurement results are then used in a structural identification scheme to update uncertainties in a finite element model of the structural system, which in turn can provide a mechanism to describe structural response and performance under different scenarios.
Preliminary results are promising. The vision-based sensing approach demonstrates real potential for deployment in the field with easy application of painted patterns on existing structures and eventual remote placement of weather-resistant cameras. Harris envisions that the technology could eventually be used for load testing for bridges all over the country. Next steps include further laboratory studies to refine the approach, extensions to field evaluation of existing structures, and finally the development of smart cameras that are easy to use by DOTs, enabling reliable data collection and analysis and providing a cost effective way to approach health monitoring in the field.
Harris may be contacted at firstname.lastname@example.org.
Sustainability and Safety Considerations in Evolving Transportation Environments
As a PhD candidate, Seyedehsan (Ehsan) Dadvar has spent his academic career studying road safety and its relationship to the ever-evolving environment related to connected, autonomous and electric vehicles, traffic simulation and freight logistics. He has become increasingly interested in the social and economic impacts of connected vehicle technologies and consumer behaviors related to these technologies.
Working with his co-advisors, Young-Jae Lee, PhD in the Department of Transportation and Urban Infrastructure Studies at MSU, and Hyeon-Shic Shin, PhD in the City and Regional Planning Program of the School of Architecture and Planning at MSU, Dadvar has contributed to a number of research projects. He is Co-Principal Investigator on an analysis of bicycle and pedestrian crash causes and interventions funded by DDOT. He has studied next generation volume reduction green infrastructure stormwater control measures (Philadelphia’s Green City Clean Waters Initiative) funded by the EPA. Recently, Dadvar and his fellow researchers completed a study funded by the CVI-UTC on applications of connected vehicle infrastructure technologies to enhance transit service efficiency and safety.
MATS UTC provided Dadvar with the opportunity to collaborate with researchers at Marshall University and Virginia Tech on the MATS UTC-funded research project, “Environmental and Safety Attributes of Electric Vehicle Ownership and Commuting Behavior.” The researchers studied attitudes toward electric vehicle (EV) use as well as the differences in commuting behavior between EV and conventional vehicle owners. The results have public policy and transportation planning implications related to EV promotion and subsidies, infrastructure related to charging stations and statewide traffic models. This work was presented at the 1st and 2nd MATS UTC Annual Meetings at the University of Delaware in 2015 and at a poster session at the University of Virginia in 2016. The final report may be viewed at http://www.matsutc.org/mode-choice-between-electric-vehicles-and-rail-transit-for-commute-trips/.
“Ehsan Dadvar has been one of the most conscientious graduate research assistants with whom we’ve had the pleasure of working,” stated Dr. Andrew Farkas, PhD, Director of the Urban Mobility and Equity Center at MSU. “His insights, analytical skills and attention to detail ensure that final technical reports, presentations, and publications we’ve co-authored over the past three years have been so highly valued.”
Dadvar is working on the final stages of his dissertation titled “Improving Crash Predictability of the Highway Safety Manual through Alternate Local Calibration Process.” The aim is to improve current procedure with a more robust approach to account for attributes of roadway segments or intersections at disaggregate level. Preliminary results were presented as a poster at the Transportation Research Board 95th Annual Meeting in 2016.
Upon graduating next spring, Dadvar hopes to pursue a post-doctoral fellowship to continue these research interests. “As the market for connected and autonomous vehicles grows, there is a critical need for a better understanding of the safety environment in which these vehicles will operate,” explained Dadvar. “As engineers, we must anticipate the evolution of various modes of transportation and be prepared to address related safety concerns. But this is more than an engineering problem. It’s having the foresight to understand their interconnectedness and impact on quality of life within an urban planning context.”
In addition to publishing in the ASCE Journal of Transportation Engineering, the Transportation Research Record: Journal of the TRB, and the Journal of Transportation Security, he has presented posters and other presentations across the country, including at the 92nd – 96th Annual Meetings of the TRB, the 2015 ITE Mid-Colonial District Annual Conference, the FHWA Highway Institute, and the 2nd International Conference on Sustainable Cities, Urban Sustainability and Transportation.
He is a member of the Institute of Transportation Engineers (ITE), including serving as the MSU Chapter President (2013-14), the American Society of Civil Engineers (ASCE), the American Society of Safety Engineers (ASSE), the American Statistical Association (ASA), and the Iranian Construction Engineers Organization (ICEO). He has served as a “friend” to the Transportation Research Board on its Standing Committee on Highway Safety Performance, the Standing Committee on Safety Data, Analysis and Evaluation, the Standing Committee on Pedestrians and Bicycle Transportation.
Dadvar will receive his PhD in Transportation from Morgan State University next spring. He has an MSc in Transportation Engineering from IAU – South Tehran Branch and a BSc in Civil Engineering from IAU – Gorgan, Iran.
He may be contacted at Seyedehsan.Dadvar@morgan.edu.
Environmental Implications of Fluid Flow and Contaminants
on Roadway Soils and Waterways
Earlier this year, MATS UTC announced eight collaborative research awards selected from 28 submissions, each a strong example of the consortium’s commitment to accelerating the adoption of sustainable transportation practices. Paul Imhoff, PhD, Professor in the College of Engineering at the University of Delaware (UDel), along with his colleagues, Pei Chiu, PhD at UDel, and Teresa Culver, PhD at the University of Virginia (UVA), received one of the awards to continue their work to improve stormwater treatment technologies.
Stormwater from roadways, other impervious surfaces in urban regions, and agricultural operations is a major contributor to deteriorating water quality in many watersheds such as the Chesapeake Bay. Nutrients, such as nitrogen, are the leading cause of impaired water quality in the U.S. and worldwide. Current stormwater treatment technologies, such as bioretention ponds, do not always treat nutrients sufficiently and may require sizable real estate to achieve the necessary removal.
The team’s 2017 MATS UTC-funded project, “Removing Nitrate from Stormwater with Biochar Amendment to Roadway Soils”, builds upon their earlier work using biochar, a ‘green charcoal’ produced from agricultural residues or renewable biomass such as wood chips, grass clippings or poultry waste, to remove or transform nitrate. Supported by the Chesapeake Bay Stewardship Fund (CBSF) and the Delaware Department of Transportation, this previous work found that amending the top 30 cm of a 2-m wide side slope to a well-traveled state highway with biochar resulted in a reduction of the stormwater runoff volume by 67% on average over 36 storm events. In addition, nitrate concentrations, the most difficult to remove form of nitrogen, were reduced by approximately 50% in some of the limited storms sampled.
The team is now focused on using the same field site to simultaneously sample stormwater flowing over and through biochar-amended soils to quantify its ability to reduce nitrate concentrations in both flow paths. In addition, the researchers will determine the necessary residence time for nitrate-laden stormwater in biochar-amended media for nitrate removal, and confirm that biochar provides electrons to mixed bacterial cultures in soil to convert nitrate into innocuous nitrogen gas. Results are expected to provide a path forward for full-scale evaluation, design, and implementation of this novel and sustainable technology – biochar amendment of existing roadway soils.
Imhoff has spent much of his academic career contributing to our understanding of the transport of fluids and contaminants in multiphase systems, mass transfer processes in soil and groundwater and more recently green stormwater treatment. These interests have global implications. Currently working with the Gates Foundation, Imhoff is developing above-ground toilets for urban communities in India lacking sufficient resources and space to install septic systems. “We’re working with manmade membranes to leverage the flow and reaction of fluid around solid matter,” explained Imhoff. The study is still underway but Imhoff has high hopes for the humanitarian and environmental implications of the project on the welfare of these communities.
Additional research interests include addressing spills from fracking fluids that infiltrate surrounding soil, and developing methods to quantify and mitigate greenhouse gas emissions from landfills.
In addition to his research pursuits, Imhoff teaches courses in environmental engineering at UDel. Reflecting his commitment to sustainable landfilling and protection of our soil and water, his classes generally focus on recycling and solid waste management, groundwater flow and pollutant transport, and modeling environmental systems.
Near the start of his career, Imhoff received a National Science Foundation Faculty Early Career Development Award. He has since received a number of honors and awards, including the 2005 Distinguished Service Award from the Association of Environmental Engineering and Science Professors, the 2011 ASCE Outstanding Reviewer Award from the Journal of Environmental Engineering, and the 2016 Top Reviewer Award from Waste Management.
Imhoff received a BS from the University of Cincinnati, an MS from the University of Wisconsin at Madison, and his MA and PhD from Princeton University.
Imhoff may be contacted at email@example.com.
For those of us who drive in the Mid-Atlantic region, it will not be surprising to learn that Washington, DC ranks third, behind New York and Los Angeles, for overall traffic congestion. Worse, the stretch of southbound Interstate 95 from the Fairfax County Parkway to Fredericksburg has the dubious honor of being the single worst traffic hotspot in the country compared to 100,000 hotspots in 25 cities. The INRIX US Traffic Hotspot Study 2017 found 1,394 traffic jams on this stretch during a two-month period, resulting in average delays of 33 minutes and covering over six miles.
Massive construction projects are often undertaken to address this kind of congestion. The recently completed Elizabeth River Tunnels Project is a billion dollar public-private partnership intended to alleviate congestion in the Hampton Roads area in Virginia. The comprehensive agreement between Elizabeth River Crossing (ERC) OpCo LLC and the Virginia Department of Transportation (VDOT) encompasses the rehabilitation of two existing tunnels and the construction of a new tunnel and an expressway. By relieving choke points and improving traffic movement, the project is expected to reduce average round trip savings by 30 minutes per day, reduce gas emissions and fuel consumption, and create regional economic benefits estimated at $170 to $254 million.
The Maryland Department of Transportation is in the midst of a modern light rail project, the Purple Line, to run 16.2 miles between Bethesda in Montgomery County and New Carrollton in Prince George’s County. With conceptual and preliminary planning started in 2009 and actual construction begun in 2016, the project is scheduled for completion in 2022, including one tunnel, a number of trails and 21 stations. The light rail electrically-powered vehicles will use existing roadways and pedestrian-friendly neighborhood stations. Projections suggest daily ridership will reach 74,000 by 2040 and that 17,000 cars will be taken off roads every day, saving 1 million gallons of gas annually.
The willingness of governments and transportation agencies to undertake these complex and expensive infrastructure projects is indicative of the congestion ‘crisis’ experienced by millions and the policy dilemmas faced by public funders trying to address the issues.
The federal government acknowledges the urgency of addressing these long-term transportation challenges, passing the Fixing America’s Surface Transportation (FAST) Act in 2015. Appropriating billions of dollars for highway improvements, the Act challenges state and local governments to move forward with critical transportation projects, recognizing the ripple effect of congestion on freight movement, infrastructure degradation, environmental impacts, pedestrian and traffic safety, adoption of smart technologies and economic development.
Beyond incurring tremendous expenses to build wider highways, new tunnels and bridges, and extensive mass transit systems, are there other less-costly and environmentally sustainable approaches to alleviate traffic congestion?
Researchers in the Mid-Atlantic region are already tackling these issues, investigating congestion through multiple strategies such as infrastructure investment, public transportation, connected and automatic vehicles and land use management. With MATS UTC funding, they are pursuing collaborative, multi-disciplinary, creative approaches to study and relieve congestion. Examples include:
Quantifying the Impact of On-Street Parking Information on Congestion Mitigation
A team of researchers from Virginia Tech and Morgan State University is seeking to reduce congestion by providing drivers with real-time information about available parking spaces. Using a Morgan State University simulation of Washington, DC’s Chinatown with 1300 metered spaces and 30 loading zones as well as Virginia Tech’s smart road, the team is studying how the availability of parking information impacts driving behavior.
LiDAR for Air Quality Measurement
Using state-of-the-art light detection and ranging (LiDAR) technology at Old Dominion University, researchers are taking an innovative approach to addressing air quality and pollution levels in relation to traffic patterns at specific congested choke points in the Hampton Roads area. They hope to validate this new approach as a way to correlate traffic flow with emissions, giving public health and policy agencies better information upon which to make traffic management and land usage decisions.
Bicycle and Pedestrian Traffic Count Program to Estimate Performance Measures on Streets and Sidewalks in Blacksburg, VA
University of Virginia and Virginia Tech researchers developed a bicycle and pedestrian traffic count program as a tool to understand the impact of pedestrians and bikes on the entire transportation network as well as on specific trails and corridors. They hope to develop a non-motorized land use model on a national scale.
Connected Vehicle Technologies for Efficient Urban Transportation
Researchers at the University of Delaware and Morgan State University are interested in using connected vehicle technology to optimize a vehicle’s control system in real-time to reduce congestion, improve fuel economy and reduce emissions. Using hybrid buses operating at the University of Delaware, the team is studying how intelligently integrated components can respond to both routine and atypical traffic situations, resulting in optimized traffic control and vehicle fuel economy.
Multi-City Direct-Demand Models of Peak Period Bicycle and Pedestrian Traffic
Virginia Tech researchers are studying the shift to non-motorized modes, such as cycling and walking, as commuters and other travellers adopt alternative options to congested roadways. Their research seeks to provide better spatial estimates of walking and cycling traffic as an input to assess exposure to hazards, evaluate infrastructure investments, or locate facilities. Their direct-demand models are intended to provide generalizable results related to the built environment around non-motorized traffic.
Environmental and Safety Attributes of Electric Vehicle Ownership and Commuting Behavior
Researchers at Morgan State University are studying attitudes toward electric vehicle (EV) use as well as the differences in commuting behavior between EV and conventional vehicle owners. The results may dictate new approaches for making public policy and transportation planning decisions related to EV promotion and subsidies, infrastructure related to charging stations and statewide traffic models.
Performance Measures for Freight Transport and General Traffic: Investigating Similarities and Differences Using Alternative Data Sources
Researchers at Old Dominion University are using three probe data sources to investigate the correlation between freight and general traffic travel times in the Hampton roads area. Such research can help to determine if a congestion relief program for a given bottleneck could benefit both freight and general-use traffic and, ultimately, provide DOTs with tools to ensure efficient movement of freight along heavily-used highway systems.
Traffic congestion, whether it occurs in major metropolitan areas or even in smaller cities, suburban areas or rural settings, has a negative effect on quality of life, environmental impacts, economic prosperity, and regional competitiveness. Research efforts that examine forward-thinking transportation strategies represent the next wave of fighting congestion with practical, cost-effective solutions.
One of the earliest innovations promoted by the FHWA’s Every Day Counts initiative is adaptive signal control technology – adaptive because traffic flow can be regulated based on data transmitted by strategically-placed sensors to adjust the timing of red, yellow and green lights. The goal is to reduce congestion by creating smoother flow and improving travel times by progressively moving vehicles through green lights. A positive by-product is that emissions are reduced and fuel economy is improved.
With growing use of Connected Vehicles (CV) (vehicles typically equipped with communication technologies such as GPS to communicate with the driver, other cars and roadside infrastructure), researchers at Old Dominion University, Virginia Tech and Marshall University are exploring optimization of current adaptive signal control technology to estimate queue length and develop enhanced signal coordination through communication with CV sensors. The research focuses on Traffic Responsive Plan Selection (TRPS), an underutilized adaptive control product enabling the selection of pre-programmed traffic signal timing plans based on vehicle demand observed from selected vehicle detectors along a signalized corridor.
Using a signal system in Morgantown, WV as the test bed, the researchers tested algorithms for estimating queue lengths from vehicle trajectory data in real-time, estimating the state of the system in real-time, and communicating information back to the controllers to change the timing plans when appropriate. The field data collection work has been completed and the advanced TRPS plans are now being compared in a simulation environment to basic coordination timing plans and basic TRPS control option across various volume scenarios to estimate improvements in delay, emissions, and fuel consumption.
“Most intersections have timed signals to ensure traffic moves at a regular pace,” explained Mecit Cetin, director of the Transportation Research Institute at Old Dominion University and one of the project’s lead collaborators. “The beauty of using enhanced TRPS is the ability to develop a full range of scenarios, or traffic response plans, to modify the timing of the traffic signal. Think, for example, of a traffic signal near a movie theater. Traffic flow fluctuates from the norm when movie-goers leave the theater. Using CV data and the most appropriate plan, the traffic signal becomes responsive to queues in real-time. In other words, the traffic signal is responsive to the immediate problem.”
The goal is to develop guidelines for designing and operating TRPS to reduce fuel consumption and emissions while promoting the adoption of traffic responsive programs as a low-cost adaptive solution to reduce congestion.
For more information, contact Dr. Cetin at firstname.lastname@example.org.
Researchers at Virginia Tech (VT) and Morgan State University (MSU) are taking a serious look at reducing vehicle fuel consumption and emissions by studying some of the largest vehicles on the roads – conventional diesel and hybrid electric buses. Building on their previous research that developed an Eco-Speed Control (ESC) system to reduce fuel consumption levels for vehicles with conventional engines, the team is turning its attention to fuel savings associated with integrating buses with ESC technology.
Communication between a traffic signal controller and a vehicle equipped with a global positioning system (GPS) and communication hardware provides the VT-developed ESP system with sufficient information (vehicle position, vehicle speed and signal phasing and timing data) to compute the fuel efficient speeds. Hesham Rakha, Ph.D., P.Eng. Samuel Reynolds Pritchard Professor of Engineering in the Charles E. Via, Jr. department of civil and environmental engineering at VT and director of the Center for Sustainable Mobility at the VT Transportation Institute (VTTI), along with colleagues Hao Chen, research associate at VTTI; Mansoureh Jeihani, Ph.D., associate professor in transportation and urban infrastructure studies at MSU; and Celeste Chavis, Ph.D., assistant professor in transportation and urban infrastructure studies at MSU, are leveraging this communication, developing ESC algorithms for buses using predictive energy estimation models. These models identify optimum speed profiles using information from surrounding vehicles and upcoming signalized intersections.
The goal is to predict the most efficient speed to move a bus through an intersection and reduce ‘stop/go’ behavior, a key reason for inefficient fuel economy. The ability to move buses through intersections efficiently could result in significant fuel savings and reduced emissions.
The first phase of the study is underway, with field testing to begin this summer at the Smart Road Test Facility at VT. This full-scale, closed test bed is managed by VTTI and owned/maintained by VDOT. It features 2.2 miles of paved lanes with 14 pavement sections. Embedded sensors detect moisture, temperature, strain vibration and weigh-in motion. The facility can even create artificial snow of up to 4” per hour as well as fog. To test the ESC system tailored for buses, three test conditions will be compared and evaluated:
- Uninformed driver: no data will be communicated (no speed adjustment)
- Countdown: the bus driver will be provided with a “time to red light” countdown every 2 seconds (self-adjusted speed)
- Recommended speed profile: the driver will be given an audio alert every 2 seconds with a recommended speed (prescribed speed).
It is anticipated that buses traveling at the calculated, prescribed speed will realize the highest energy efficiencies. Adjusting speed by even a few miles/per hour, while reducing braking and accelerating, can have significant impact on fuel savings over time.
The researchers are pleased to be leveraging the resources offered by the Smart Road facility. “The test facility allows us to test our algorithms that compute optimum vehicle trajectories using real drivers in conditions as close to reality as possible without actually being on the roadways,” explained Rakha. “We can test a variety of conditions and delays without jeopardizing the safety of any surrounding vehicles.”
The MSU team will implement and test the ESC algorithms developed by VT in a driving simulator to extend the previous controlled field tests on light-duty vehicles to test under more complex conditions, such as considering different approach speeds and multiple signalized intersections.
When the project wraps up in mid-2018, the team hopes to better understand the challenges associated with dynamic traffic conditions and real-time data computation as well as the differences in responses based on vehicle powertrain type and electric battery state-of-charge. Ultimately, they hope all buses will be able to drive more efficiently near intersections, reducing travel time and fuel consumption.
For more information, contact Dr. Rakha at email@example.com.