VDOT’s Connected Vehicle Program Noah Goodall, Ph.D., P.E. Research Scientist Virginia Center for Transportation Innovation and Research ASHE Old Dominion Section Meeting June 13, 2013

Smartphones Very sophisticated computer Sensors GPS 3-axis accelerometer Camera Magnetometer Carried with you all day

Modern Vehicles – Very Sophisticated How many lines of code in a: F-22 Raptor: Average new Ford: 1.7 million 10 million

Computerized Measurement Speed Heading Acceleration (lateral, longitudinal, vertical) Position (from GPS) Other diagnostics Wipers on/off Headlights on/off Braking status Turn signals on/off Tire pressure Rain sensors Steering wheel angle Stability control

Vehicle-to-Vehicle Communication: Not Sophisticated Hi-tech vehicles Low-tech communication with other vehicles Brake lights Turn signals Horn Flash headlights

Vehicle-to-Infrastructure Communication: Not Much Better We want to know where vehicles are, what they’re doing Many sensors already in the field to do this

Field Detection

Field Sensor Shortcomings Limited data quality Point detection, not continuous coverage Difficult/expensive to repair = frequent downtime Limited types of data Aggregated speed, density, and volumes at a single point

Infrastructure-to-Vehicle Difficult to communicate with the driver both in real-time and across a wide area

Connected Vehicles

Wireless Vehicle Communication

How it Works Transmit data from the vehicle Captured from GPS, accelerometers, magnetometers, or in-vehicle sensors Transmit to other vehicles or roadside equipment Cellular, Bluetooth, WiMAX, Wi-Fi, DSRC

Potential of Connected Vehicles Three ways to connect: Vehicle-to-vehicle: Electronic brake lights Crash avoidance Vehicle-to-infrastructure: Incident detection Weather/ice detection Infrastructure-to-vehicle Broadcast traffic signal timing Dynamic re-routing

Similarity to Other Safety Systems Similar to radar- and laser-based safety systems, but much cheaper Adaptive Cruise Control Google Self-Driving Car

Connected Vehicles Today Real-time speed data from cell phones

Research VDOT is the lead state in the Cooperative Transportation Systems Pooled Fund Study Traffic signal control Broadcasting traffic signal timing to approaching vehicles Potential of aftermarket add-on devices Standardization Pavement maintenance

Connected Vehicle Test Bed University Transportation Centers grant for two small-scale field deployments of these technologies Will use combination of cellular and Dedicated Short-Range Communications (DSRC) Low latency, high bandwidth Allows for most powerful safety and mobility applications

Connected Vehicle Test Bed Partners: VDOT Virginia Tech University of Virginia Morgan State Nissan and Volvo (advisory roles) Available to other universities to test projects

Connected Vehicle Testbed Virginia Tech Smart Road 7 RSUs Northern VA 48 installed RSUs 2 portable RSUs

Roadside Units

Roadside Units I-495 I-66 US-29 Gallows Rd US-50

On Board Equipment

On Board Equipment 200 Aftermarket Safety Devices are being developed 10 instrumented cars 4 sedans (GM brand) 2 SUVs (GM brand) 4 motorcycles 2 instrumented heavy vehicles Semi-truck Motorcoach System offers Road Scout (Lane Detection), MASK (Head Tracker) and epoch detection Data is captured over the vehicle network (CAN) Parametric Data Accel X,Y,Z Gyro X,Y,Z GPS Speed and Position Network speed Turn signal Brake Accelerator position

Connected Vehicle Research Projects 19 projects have been funded that focus on freeway and arterial applications: Adaptive Stop/Yield Adaptive Lighting Intersection Management Using Speed Adaptation Eco-Speed Control Awareness System for Roadway Workers Emergency V2V Communication Freeway Merge Management Infrastructure Safety Assessment Safety and Congestion Issues Related to Public Transportation Connected Motorcycle Crash Warning Connected Motorcycle System Performance Smartphone App Reducing Motorcycle and Bicycle Crashes CV Freeway Speed Harmonization Systems Reducing School Bus Conflicts through CVI NextGen Transit Signal Priority with CVI Smartphone DMS Application Willingness to Pay and User Acceptance Increasing Benefits at Low Penetration Rates

Background Rollout of connected vehicles will not be instantaneous 16 years between kickoff and 80% Projected rollout of on-board equipment in US Fleet (Volpe, 2008)

Connected Vehicle Applications Lots of connected vehicle mobility applications in development Most of these applications need at least 25% of vehicles to be “connected” to see benefits Higher percentages = more benefit Application % Connected Vehicles Needed for Benefits Traffic signal control 20-30% Incident detection 20% Queue length estimation 30% Performance measurement 10-50%

Assumed Location of Unequipped Vehicle What it Means Problem – Mobile sensors and connected vehicle data are not constant or ubiquitous. There are gaps. Solution – “Location Estimation” Behavior of equipped vehicles may suggest location of unequipped vehicles. Assumed Location of Unequipped Vehicle Equipped Vehicles

Methodology How to estimate vehicle locations Depends on unexpected behavior of equipped vehicles – indicates an unequipped vehicle ahead What is “unexpected”? Car-following model

Algorithm Vehicles assumed to follow Wiedemann car-following model Widely accepted, basis for VISSIM A deviation from expected acceleration indicates an unequipped vehicle ahead Headway = 97 feet Wiedemann is widely accepted Vehicle B Vehicle A Vehicle C Speed = 30 mph Acceleration = -4 ft/s2 Expected Accel = 7 ft/s2 Inserted Vehicle (Estimate) Speed = 29 mph Acceleration = 0 Speed = 45 mph Acceleration = 0 ft/s2 Unexpected Behavior

Testing Using NGSIM datasets as ground truth 30-minutes of individual vehicle movements ¼ mile segment of I-80 in Emeryville, CA Designate some vehicles as “unequipped” and remove from data set

Heat Map of Vehicle Densities 32

Densities Along I-80 at 25% Market Penetration Actual Densities (Sampled and Observed Vehicles) Distance (1/4 mile total) Estimated Densities (Sampled and Estimated Vehicles) Distance (1/4 mile total) Time (s)

Ramp Metering Application

Applied to Ramp Metering GAP Algorithm 35

Summary Connected vehicles is important, innovative, and evolving VCTIR/VDOT is committed to being at the forefront Ensure that connected vehicles will meet the needs of Virginia

For more information: Noah Goodall noah.goodall@vdot.virginia.gov