Tag Archives: congestion

Leveraging New Vehicle Technologies to Address Congestion, Environmental Impacts and Traffic Safety

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:

Connected Vehicle Technologies for Energy Efficient Urban Transportation

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.

Eco-Speed Control for Hybrid Electric Buses in the Vicinity of Signalized Intersections

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.

Investigating the Relationship between Driving Patterns and Traffic Safety using Smartphones-Based Mobile Sensor Data

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.

Connected Vehicle Freeway Speed Harmonization Systems

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.

Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control

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.

Exploring the use of LIDAR Data from Autonomous Cars for Estimating Traffic Flow Parameters and Vehicle Trajectories

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.

Planning for Walking and Cycling in an Autonomous Vehicle Future

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.

Combatting Congestion: Strategies to Reduce Emissions, Address Safety and Improve Driver Morale

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.

Leveraging Connected Vehicles to Enhance Traffic Responsive Traffic Signal Control

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 mcetin@odu.edu.