Notes on CVs, CAVs and Measures of Effectiveness

In contrast with automated/autonomous vehicle technology, capable of sensing its environment and navigation without human input, to fulfill the transportation needs of a traditional vehicle (through sensing technology – radar, LiDAR, computer vision and sonar), CV entails communication with the surrounding environment.  Connected Vehicles (CV) communicate with nearby vehicles and infrastructure, wirelessly (e.g., between vehicles (V2V), between vehicles and Infrastructure (V2I/I2V), or between vehicles and devices (V2X/X2V). The following applications have been demonstrated at sometimes rudimentary levels:

USDOT’s CV program has included:

  • CV Technology Pilot Programs
    • Safety: Connected vehicle safety pilot
    • Mobility: Dynamic mobility applications (DMA)
    • Environment: Applications for the environment – real-time information synthesis (AERIS) –
    • Road Weather: Road Weather Connected Vehicle Applications
  • CV Pilot Deployment Program (Wave 1)
    • I-80 in Wyoming (truck safety and efficiency)
    • New York City (vehicle and pedestrian)
    • Tampa, Florida (traffic around reversible freeway lanes)
    • Also relevant: Smartcity Efforts, CAMP, Pilots, etc.

Broad classifications of CAV applications are:

  • Vehicle-centric: Distributed/decentralized maneuvers, equipped vehicles interacting with their surroundings,or other vehicles
  • Infrastructure-centric: Centralized surveillance,  Intelligent Traffic Management
    Centers (TMC), or Roadway infrastructure, e.g., inductive loop detectors, communication-capable roadside units
  • Traveler-centric:  Pedestrians, Bicycles, wheelchairs

Safety, mobility and environmental sustainability typically represent the three cornerstones when evaluating the effectiveness of a CAV application system. Most CAV applications are typically developed with the major goal of improving one of these key elements.

Measures of effectiveness relate to safety, mobility, and the environment

  • Safety: • Number of collisions, injury, deaths • Probability of collision • Time-to-collision • Vehicle spacing • Speed differences between vehicles • Queue length • Number of congestion occurrences
  • Mobility: • Average travel time • Overall Delay • Vehicle-to-Capacity ratio • Level of Service • Average/total speed • Vehicle-Miles-Traveled (VMT)/Vehicle Hours-Traveled (VHT) • Vehicle flow • Queue length • Average parking search time • Number of total stops • On-Time Performance
  • Environmental: • Energy consumption • Criteria pollutant emissions (CO, HC, NOx, PM) • GHG emissions (CO2, N2O, etc.) • Fuel use

Eco-signal operations have traffic energy benefits.  For example:

  • Eco-Approach and Departure at Signalized Intersections (similar to SPaT ) can produce a 10% energy savings.  This application utilizes traffic signal phase and timing (SPaT) data to provide driver recommendations that encourage “green” approaches to signalized intersections; e.g., coasting down earlier to a red light or speeding up to make it (safely) through the intersection on green light.  Testing with a partially AV found 5% energy savings over manual driving using human driving interface and 21% energy savings over manual driving, using automation.
  • Eco-Traffic Signal Timing (similar to adaptive traffic signal systems) can produce a 5% energy savings.
  • Eco-Traffic Signal Priority (similar to traffic signal priority) can produce a 6% energy savings.

Eco-lanes, with ecospeed harmonization/variable speed limits producing 5% energy savings and cooperative adaptive cruise control producing 19% energy savings.  With the latter, CV technologies can be used to collect the vehicle’s speed, acceleration, and location and feed these data into the vehicle’s ACC.  Receiving V2V messages between leading and following vehicles, the application performs calculations to determine how and if a platoon can be formed to improve environmental conditions, provides speed and lane information of surrounding vehicles in order to efficiently and safely form or decouple platoons of vehicles.

There are safety and mobility tradeoffs between collision avoidance and increased spacing, for example, and between mobility and energy, with higher speeds.

Lane Speed Monitoring Conclusions.  With a

  • 10% penetration rate: 8% travel time decrease versus 3% more fuel and higher potential conflict risks;
  • 20% penetration rate: similar to 10% penetration rate case;
  • 50% penetration rate: barely reduce travel time;
  • 80% penetration rate: all MOEs deteriorate

More related future research direction can be inspired by the drawbacks of current applications, e.g., the combination of several applications to overcome disadvantages of a single application; Many factors could affect the performance, e.g., penetration rate of application-equipped especially when there is growing trend toward mixed traffic within the next decade.

Future directions Overall Conclusions & Future Directions

  • To date, most CAV applications have been analyzed in isolation, attempting to improve a specific MOE; analysis of multiple MOEs is now increasing •
  • To mitigate potential negative MOEs, some applications can be combined together to overcome the disadvantage of one single application, leading to some co-benefits
  • In many cases, some applications can be tuned to maximize benefits across several MOEs
  • It is also possible to dynamically tune application parameters depending on space and time
  • It is important to analyze applications across multiple scenarios (exhaustive sensitivity analysis)
  • Many factors could affect the performance, e.g., penetration rate of application-equipped especially when there is growing trend toward mixed traffic within the next decade.

https://ncst.ucdavis.edu/wp-content/uploads/2016/08/NCST_Caltrans-TO-029-Barth-SME_Final-WP_March-2017.pdf