XTELLIGENT INC — Department of Energy SBIR Phase II: 13c

Description

The inefficient flow of vehicles on our roadways expend tremendous amounts of energy idling or reaccelerating their mass in stop-and-go traffic.This results in significant wasted energy, greenhouse gas emission, vehicle and roadway wear and tear, and economic inefficiency.According to the Centre for Economics and Business Research, the annual effect of traffic congestion in the U.S.includes 871 million gallons of wasted fuel, 8,610 kilotons of CO2 emitted, and 6.8 billion hours of time wasted.Despite this, 99% of U.S.intersections are controlled by a fixed timer, and incumbent adaptive traffic control technologies have only penetrated 1% of the marketplace over 35 years due to high cost and poor efficacy.We are proposing to develop the next generation Cooperative Intelligent Transport System (C-ITS) that can significantly reduce transportation network energy consumption by deploying the latest control algorithms in conjunction with emerging mobility concepts and sensing and learning technologies (i.e., connected/automated vehicles, Internet of Things, machine learning).The C-ITS system we envision is specifically designed to address the key hurdles, efficacy and cost, which are preventing wide-spread adoption of adaptive traffic signal capabilities and are therefore preventing system-level energy, environmental, and economic benefit.We have been validating and refining the core algorithms in live traffic intersections in California and Colorado that determine real-time green splits, offsets, and cycle time to optimize network level objectives, such as throughput, energy consumption, and travel time.We have received positive preliminary data confirming the potential for significant energy and environmental impact should the algorithm be deployed at the system level, thereby enforcing coordination and preparing the system-level ground rules for emerging mobility concepts.We will expand our live testing up to 100 intersections to better understand the efficacy of the algorithm at scale.Further, we will integrate connected vehicle and other emerging data streams to drive the traffic signal timing and assess its impact on city-level throughput, as well as energy usage and environmental impact on the system-level mobility system.We will also begin to develop a prototype that can be tested for commercialization purposes.By deploying the powerful algorithm on a wide scale, cities can benefit from much smarter signalized intersections that can improve traffic throughput, while also ensuring safety, energy, environmental, and economic benefits.