Connected Signals: The Environmental Impact
November 22, 2017 — Traffic signal V2I systems offer enormous environmental potential. In the Unites States, the USDOT reports that two trillion vehicle miles were travelled on urban roadways in 2014, two thirds of all miles driven nationwide , 84% in private vehicles . Providing real-time, predictive traffic signal information to vehicles offers the potential to reduce urban fuel consumption by 8–15% or more, according to estimates from automakers such as BMW [3,4] and Audi [5,6], as well as from the National Renewable Energy Laboratory .
The availability of real-time, predictive, signal information enables a number of fuel saving and emissions reduction technologies, helping meet air quality and carbon-reduction goals, and saving drivers fuel. The key environmentally beneficial applications include:
• Engine stop/start: Some automakers already offer the option to automatically turn off their vehicles’ engines when the vehicles are stopped. Provided the engine is off for more than a few seconds, this can provide net fuel and emissions savings. There are two drawbacks with such systems, however: In many cases, such as at stop signs or in stop-and-go traffic, the vehicle may only be stopping momentarily. Moreover, many drivers are unhappy with the momentary hesitation that occurs when the engine restarts as they prepare to drive away. Signal-aware vehicles can address both of these issues by only turning off the engine when stopped at a red signal that will be red for more than just a second or two, and automatically restarting just before the signal turns green.
• Speed to green: Given information about vehicle position, direction, and speed, predictive signal information can be used to relay speed recommendations to drivers that will get them to the next signal during the green phase. This lets them avoid burning fuel to maintain their current speed (or even speed up) approaching a signal, only to have to slow to a stop when they arrive at a red light (and then have to use even more fuel to accelerate once the light turns green).
• Green wave speed: In much the same way as a speed can be recommended that will get drivers to the next light on green, speeds can be recommended that will let them transit a series of signals on green, part of the so-called “green wave”. This can be done not only for groups of signals explicitly set up for green waves, but any time a reasonable speed recommendation would let drivers catch multiple lights.
• Deceleration to red: It is not always possible to recommend a reasonable speed that will let drivers catch the next light. In such cases drivers can be told that they will miss the next signal regardless, letting them smoothly decelerate (and avoid accelerating), saving fuel. In the case of hybrid vehicles, vehicles can be programmed to stop the engine and use the electric motor when approaching a to-be-red signal. Much of the energy used for the approach will be recovered through regenerative braking as the vehicle comes to a stop.
• Signal-aware navigation: With predictive signal and traffic information, vehicles can be routed to minimize travel time, deceleration to red lights, and idling at lights. Connected Signals’ routing algorithms are designed to avoid redirecting vehicles through residential districts in their efforts to optimize flow.
In addition to the benefits for internal combustion, hybrid and alternative fuel vehicles, many of these same benefits apply to pure electric vehicles (EVs). By telling the EV driver that an upcoming light will be red, the driver can begin to decelerate before the light changes, avoiding a later, sudden, deceleration that overloads the regenerative braking’s ability to fully transfer power back to the battery. Similarly, knowing what the light will do can help avoid accelerating too much and then wasting energy having to slow down. In addition, signal-aware navigation that can enable arriving at fewer red lights can also optimize directly for power consumption while taking signal behavior into account.
Finally, the potential for urban-driving fuel (or power) savings opens up the possibility for those savings to be included in the Corporate Average Fuel Economy (CAFE) standard numbers  of automakers who deploy systems in their vehicles to exploit it. The EPA has expressed its willingness to consider these types of fuel savings when automakers request that they do so. Achieving a level of savings sufficient to trigger CAFE recognition, however, requires involvement on the parts of traffic agencies (to make signal data available), automakers (to deploy systems that exploit this data to save fuel), and government laboratories (to validate fuel savings). Traffic agencies are in a unique position to spur this process, since availability of their signal data is the prerequisite to automakers deploying the relevant fuel-savings technologies. They are thus also in a unique position to catalyze significant cooperative efforts across both government agencies and commercial entities.