Development and evaluation of an enhanced eco-approach traffic signal application for Connected Vehicles

Xia, Haitao; Wu, Guoyuan; Boriboonsomsin, Kanok; Barth, Matthew

doi:10.1109/itsc.2013.6728248

Cited by 39 publications

(23 citation statements)

References 11 publications

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“…Mandava developed an arterial velocity planning algorithms which could minimize the acceleration/deceleration rates to reduce the fuel consumption and emissions of individual vehicle [15]. Later, Barth proposed an upgraded algorithm with the minimum objective function of tractive power requirements of individual vehicle which shows a much better performance than before [16,[22][23][24][25][26]. Kamal used the model predictive control for improve vehicle's ecology in varying traffic environment [27].…”

Section: Introductionmentioning

confidence: 99%

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Jiang¹,

An²,

Jian³

et al. 2018

Sustainability

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Abstract:This research proposed an eco-approach and departure system for left-turn vehicles at a fixed-time signalized intersection. This system gives higher priority to enhancing traffic safety than improving mobility and fuel efficiency, and optimizes the entire traffic consisted of connected and automated vehicles (CAVs) and conventional human-driven vehicles by providing ecological speed trajectories for left-turn CAVs. All the ecological speed trajectories are offline optimized before the implementation of system. The speed trajectory optimization is constructed in Pontryagin's Minimum Principle structure. The before and after evaluation of the proposed system shows the percentage of vehicles that drive pass the intersection at safe speed increases by 2.14% to 45.65%, fuel consumption benefits range 0.53% to 18.44%, emission benefits range from 0.57% to 15.69%, no significant throughput benefits is observed. The proposed system significantly enhances the traffic safety and improves the fuel efficiency and emission reduction of left-turn vehicles with no adverse effect on mobility, and has a good robustness against the randomness of traffic. The investigation also indicates that the computation time of proposed system is greatly reduced compared to previous eco-driving system with online speed optimization. The computation time is up to 0.01 s. The proposed system is ready for real-time application.

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Section: Introductionmentioning

confidence: 99%

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Jiang¹,

An²,

Jian³

et al. 2018

Sustainability

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“…±1 stands for the current distance to signal of vehicle 's potential neighbors on target lane . Constraint (11) ensures that each vehicle is assigned to only one position in the sequence for some particular lane. Constraint (12) guarantees that no more than one vehicle is assigned to any position in the sequence along any lane.…”

Section: B Intra-cluster Sequence Optimizationmentioning

confidence: 99%

Cluster-Wise Cooperative Eco-Approach and Departure Application for Connected and Automated Vehicles Along Signalized Arterials

Wang

Hao

et al. 2018

IEEE Trans. Intell. Veh.

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In recent years, various versions of the Eco-Approach and Departure (EAD) application have been developed and evaluated. This application utilizes Signal Phase and Timing (SPaT) information to allow connected and automated vehicles (CAVs) to approach and depart from a signalized intersection in an energy-efficient manner. To date, most existing work have studied the EAD application from an ego-vehicle perspective (Ego-EAD) using Vehicle-to- Infrastructure (V2I) communication, while relatively limited research takes into account cooperation among vehicles at intersections via Vehicle-to-Vehicle (V2V) communication. In this research, we developed a cluster-wise cooperative EAD (Coop-EAD) application for CAVs to further reduce energy consumption compared to existing Ego-EAD applications. Instead of considering CAVs traveling through signalized intersections one at a time, our approach strategically coordinates CAVs' maneuvers to form clusters using various operating modes: initial vehicle clustering, intra-cluster sequence optimization, and cluster formation control. The novelCoop-EAD algorithm is applied to the cluster leader, and CAVs in the cluster follow the cluster leader to conduct EAD maneuvers. A preliminary simulation study with a given scenario shows that, compared to an Ego-EAD application, the proposed Coop-EAD application achieves 11% reduction on energy consumption, up to 19.9% reduction on pollutant emissions, and 50% increase on traffic throughput, respectively.

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“…Chen [35] and Jin [36] estimated the optimal speed profiles for individual vehicles with the consideration of vehicle queues and road grades. Xia et al [37] and Hao et al [38] improved the EAD system with the consideration of surrounding traffic to estimate vehicle speed profiles. However, these studies assumed that the queue length are given or can be detected by other sensors.…”

Section: Introductionmentioning

confidence: 99%

Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

Yang

Almutairi

Rakha

2021

IEEE Trans. Intell. Transport. Syst.

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Consecutive traffic signalized intersections can increase vehicle stops, producing vehicle accelerations on arterial roads and potentially increasing vehicle fuel consumption levels. Eco-driving systems are one method to improve vehicle energy efficiency with the help of vehicle connectivity. In this paper, an eco-driving system is developed that computes a fuel-optimized vehicle trajectory while traversing more than one signalized intersection. The system is designed in a modular and scalable fashion allowing it to be implemented in large networks without significantly increasing the computational complexity. The proposed system utilizes signal phasing and timing (SPaT) data that are communicated to connected vehicles (CVs) together with real-time vehicle dynamics to compute fuel-optimum trajectories. The proposed algorithm is incorporated in the INTEGRATION microscopic traffic assignment and simulation software to conduct a comprehensive sensitivity analysis of various variables, including: system market penetration rates (MPRs), demand levels, phase splits, offsets and traffic signal spacings on the system performance. The analysis shows that at 100% MPR, fuel consumption can be reduced by as high as 13.8%. Moreover, higher MPRs and shorter phase lengths result in larger fuel savings. Optimum demand levels and traffic signal spacings exist that maximize the effectiveness of the algorithm. Furthermore, the study demonstrates that the algorithm works less effective when the traffic signal offset is closer to its optimal value. Finally, the study highlights the need for further work to enhance the algorithm to deal with over-saturated traffic conditions.

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Development and evaluation of an enhanced eco-approach traffic signal application for Connected Vehicles

Cited by 39 publications

References 11 publications

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Eco-Approach and Departure System for Left-Turn Vehicles at a Fixed-Time Signalized Intersection

Cluster-Wise Cooperative Eco-Approach and Departure Application for Connected and Automated Vehicles Along Signalized Arterials

Eco-Driving at Signalized Intersections: A Multiple Signal Optimization Approach

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