Intelligent Transportation System Technologies

New "intelligent" technologies at the driver-, vehicle-, and transportation system-level are attracting more research attention for their potential to improve the energy efficiency, convenience, and environmental impact of current transportation systems. High computing power, analytical software, connectivity, and other features mean that these technologies can enable communication between transportation infrastructure, vehicles, and people that can lead to system-level efficiencies. These efficiencies rely on connectivity, including vehicle-to-vehicle and vehicle-to-infrastructure communication, and by the mobile devices that most travelers carry in their pockets.

Connected and Automated Vehicles

Connected vehicle technology enables vehicles, roads and other infrastructure, and smartphones to communicate and share vital transportation information through advanced wireless communication technology. Automated vehicle (AV) technologies use hardware and software to perform some or all driving tasks on a sustained basis. AVs can steer, accelerate, and brake with little to no human input. Connected and automated vehicle (CAV) technology could potentially improve traffic flow and safety, increase vehicle efficiency, and reduce transportation energy in certain applications. SAE International has defined six levels of driving automation, ranging from no driving automation (level 0) to full driving automation (level 5). With level 5 automation, a vehicle can operate autonomously on all roads in the United States that are navigable by a human driver.

CAVs provide the opportunity to transform the logic, operations, and performance of traffic signal control, thereby reducing congestion and increasing transportation system efficiency. The potential impacts of CAVs on vehicle miles traveled (VMT), vehicle fuel efficiency, and costs to consumers are unknown. The U.S. Department of Energy's Vehicle Technologies Office, Energy Efficient Mobility Systems (EEMS) program research shows even a modest market share of CAVs reduces congestion and energy consumption in situations such as vehicle merging at highway ramps. Simulations on I-75 indicate that a 20% light-duty CAV penetration leads to 4% corridor fuel consumption savings for a range of mixed traffic scenarios. They could also facilitate increased rideshare, thereby promoting greater vehicle operating efficiency. However, if individually owned, CAVs could substantially increase VMT by allowing riders to do other activities while they spend time in a vehicle, thus reducing the inconvenience of longer trips.

One beneficial application of CAVs is truck platooning, a vehicle-to-vehicle communication strategy that uses sensors to virtually connect two or more trucks in a convoy. This allows vehicles to save fuel by accelerating and braking together and traveling at a closer distance. DOE's EEMS program has investigated the energy, technology, and usage implications of vehicle connectivity and automation and identified efficient CAV solutions. For example, based on test-track data, platoons of three close-following trucks can achieve a combined 13% reduction in fuel use. If trucks nationwide operated in these CAV three-truck platoons, 2.1 billion gallons of fuel could be saved each year. A 2017 study analyzed telematics data on 57,000 vehicles traveling 210 million miles and determined that 63% of U.S. interstate and highway miles may be platoonable.

The U.S. Department of Transportation is collaborating with public and private partners, including state and local governments, vehicle and device manufacturers, and academia, to advance connected vehicle development and implementation.

Automated Mobility Districts

An Automated Mobility District (AMD) is the implementation of CAV technology across a focused area, such as a campus for a university, medical facility, or business campus. AMDs are characterized by driverless, on-demand, low-speed shuttles that provide direct origin-to-destination service to either individuals or small groups. Personal vehicles may or may not be prohibited, but the area is designed to be most efficiently accessed using the automated mobility services provided. An AMD has the potential to reduce total system fuel consumption, but the amount is largely dependent on operating and ridership assumptions.

Traffic Signal Control

Much more fuel is required to stop at a red light and then accelerate up to speed rather than cruising through a green light. Currently, traffic signals use sensing and control to safely coordinate the flow of vehicles, pedestrians, bikes, and scooters, while also prioritizing safe and efficient public transportation. However, modern control systems are limited by the information provided to them from sensors. Advances in CAV technologies provide an opportunity to transform how traffic signals are controlled to reduce delay, conserve energy, and enhance safety at intersections.

Many traffic signals are controlled by software within signal cabinets that run simple pre-timed sequences for certain times and days of the week. Some can respond to changes in demand, varying their timing in response to feedback from infrastructure sensors. At best, such signals only offer a partial picture of the state of traffic, leaving out details about the location and velocity of all vehicles.

Improvements are possible by pairing CAVs with more sophisticated infrastructure-based sensing that provides the location and speed of all vehicles (and possibly pedestrians, cyclists, and other moving objects) to optimize movement in intersections. DOE’s EEMS program investigated the potential to reduce delay, conserve energy, and enhance safety with an advanced signal control network (either through vehicles or at the intersection) and control at signalized intersections in the SMART Mobility Urban Science Capstone Report.

Green Routing

Green routing is a connected vehicle strategy under which drivers receive information about the most fuel-efficient route taking speed and stops into account before departing for a given destination. A study using a large-scale, high-resolution dataset from the California Household Travel Survey indicates that 31% of actual routes have fuel savings potential, and among these routes the cumulative fuel savings could reach 12%. The Connected Traveler project is designed to boost the energy efficiency of personal trips and the overall transportation system by maximizing the accuracy of predicted traveler behavior in response to real-time feedback and incentives. Google Maps now offers a green routing option that extends this service to a much larger audience.

Transportation on Demand

Transportation network companies (TNC) such as Uber and Lyft provide a for-hire transportation mode like taxis and their on-demand service is typically priced similarly to traditional taxi services. They also offer real-time observability through map-based monitoring and mobile phone-friendly payment methods. The emerging impacts of services like these have become an increased area of city attention and research, as their increased use has implications for transportation, energy use, parking revenues, and future infrastructure.

Solo rides in TNC vehicles, and especially the driving of empty vehicles to pick up new passengers, may actually increase overall VMT. In fact, ride-hailing increases energy use by an estimated 41%–90% compared to prior mode. But when ride-hailing is used to complement transit, communities could see increased mobility and transportation efficiencies. One study in Bloomington, Illinois, showed that if ride-hailing to transit stations was subsidized for 2,000 new transit riders, transit use would increase by 11% and VMT would be reduced by 33,000 miles.

Additionally, when transportation-on-demand is combined with shared vehicles, such as with Via, Scoop, Bridj, or micro-transit programs, there are improved outcomes for congestion, efficiency, and mobility. Research has shown that combining high vehicle sharing and high automation decreases total energy consumption by 23% and VMT by 18% and increases network speed by 17% due to fewer vehicles on the road.

Policymakers, transit agencies, and urban planners can use these types of insights to foster opportunities for mobility as a service, such as on-demand transit or public-private partnerships between TNCs and local transit agencies, while ensuring public transit services meet user needs and encourage mode shift.