University of California, Riverside

Department of Electrical and Computer Engineering



Eco-Approach and Departure Techniques for Connected Vehicles at Signalized Traffic Intersections; Presented by Haitao Xia


Eco-Approach and Departure Techniques for Connected Vehicles at Signalized Traffic....
 
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Eco-Approach and Departure Techniques for Connected Vehicles at Signalized Traffic Intersections; Presented by Haitao Xia

June 5, 2014 - 10:00 am
Winston Chung Hall, 202

Currently there are a variety of strategies that are being considered to reduce fuel consumption and emissions in transportation sector. From the transportation operations perspective, one strategy that is gaining increasing interest worldwide is eco-driving. Eco-driving typically consists of changing a person’s driving behavior based on providing general advice to the driver, such as accelerating slowly, driving smoothly, and avoiding high speeds. More advanced dynamic eco-driving provides real-time advice to drivers based on changing traffic and infrastructure conditions for even greater fuel and emission savings without compromising traffic mobility. The concept of dynamic eco-driving takes advantage of real-time traffic sensing and infrastructure information, which can then be communicated to a vehicle with a goal of reducing fuel consumption and emissions. This research focuses dynamic eco-driving on an arterial corridor with traffic control signals, where signal phase and timing (SPaT) information of traffic lights is provided to the vehicle as it drives down the corridor. The vehicle can then adjust its velocity while traveling through the corridor with the goal of minimizing fuel consumption and emissions.

There are currently a few methods that are employed to realize eco-driving. Besides the dynamic approach used in this research, A-star and DP are also used in small scale networks. Compare with the dynamic approach, these other two methods requires the knowledge of signal information of all the proceeding signal lights and has to assume absolute free flow traffic condition. Additionally, extensive computation is also required by these two methods. Therefore, in order to be able to quickly determine a strategy to pass through signalized intersections and dynamically adjust the strategy to constantly changing traffic condition, dynamic approach is the most robust and efficient method to achieve eco-driving in real world.

A dynamic Eco-Approach and Departure algorithm has been developed and is described in detail in Chapter 4. This algorithm has then been extensively tested in simulations, showing individual vehicle fuel consumption and CO2 reductions of around 10% - 15%, depending corridor parameters including traffic volume, traffic speed, and other factors. This 10% - 15% improvement is realized directly by the vehicle that is equipped with this dynamic eco-driving technology and can be accomplished with very little time loss. An extensive analysis of the entire traffic stream under different traffic volumes and different penetration rates of the dynamic eco-driving technology was also carried out in this research. It is found that there are also significant indirect network-wide energy and emissions benefits on the overall traffic [27], even at low penetration rates of the technology-equipped vehicles. This is due primarily to the eco-driving of connected vehicle affecting other unconnected vehicles that follow behind. Extensive field tests have also been conducted in different vehicle types at various locations in the United States and the results were analyzed and discussed.

However, the design of the subject vehicle’s speed trajectory is optimized on single vehicle and does not consider the traffic conditions ahead. Therefore, the actual speed profile may not be able to follow the design target due to car-following constraints. As a result, an additional amount of fuel is wasted on unnecessary acceleration/idling. Therefore we proposed an enhanced eco-approach algorithm [35] that utilizes not only SPaT message but also the information of preceding equipped vehicles for better speed trajectory planning. 

Extensive field tests have been conducted in three different sites (Richmond Field Station, Riverside and Turner Fairbank Highway Research Center) in three different vehicles (BMW 535i, Nissan Altima and Jeep Grand Cherokee). Generally 10%-20% of energy and emissions savings were achieved depending on network geometry, traffic light settings, speed limit and vehicle’s entering speed into the communication range.

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Electrical and Computer Engineering
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