Tag Archives: connected vehicles

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.

Student Spotlight: Seyedehsan (Ehsan) Dadvar, Morgan State University

Sustainability and Safety Considerations in Evolving Transportation Environments

Seyedehsan Dadvar

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.

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.