Field operational testing of ECO-approach technology at a fixed-time signalized intersection

Xia, Haitao; Boriboonsomsin, Kanok; Schweizer, Friedrich; Winckler, Andreas; Zhou, Kun; Zhang, Wei-Bin; Barth, Matthew

doi:10.1109/itsc.2012.6338888

Cited by 80 publications

(57 citation statements)

References 7 publications

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“…The proposed system has no adverse effect on the mobility. In other words, the proposed system could maintain optimal mobility under different traffic conditions, unlike some previous eco-driving system that has negative impacts on throughput at the intersection under high congestion levels [16,22,38,39]. Figure 6 shows the fuel consumption benefits under various levels of CL and MPR of CAVs.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…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%

“…Barth and Xia simplified the optimization by modelling the acceleration and deceleration stage as trigonometric functions travel time to the intersection [16][17][18][19][20][21][22]. Rakha utilized simulation to generate various speed trajectories which comply with the constraints of signal control and then selected the trajectory with lowest fuel consumption as output [36].…”

Section: Introductionmentioning

confidence: 99%

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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: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Jiang¹,

An²,

Jian³

et al. 2018

Sustainability

View full text Add to dashboard Cite

show abstract

“…A recent study showed that faster but shorter acceleration/deceleration is more fuel efficient than milder but longer acceleration/deceleration when compared over the same time span [11]. A later research study also showed a similar finding in both simulation and field testing for a single vehicle scenario [12]. When traffic constraints are taken into account, the design of an eco-approach speed trajectory becomes much more complicated [13].…”

Section: Development and Evaluation Of An Enhanced Eco-approach Traffmentioning

confidence: 80%

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

Xia

Boriboonsomsin

et al. 2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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The eco-approach and departure application for traffic signals has shown promising results for fuel and carbon dioxide savings. This application uses the signal phase and timing (SPaT) information from the traffic signal to better guide a driver through the signalized intersection in an environmentally-friendly way. This paper proposes an enhanced eco-approach and departure system which takes into account not only the SPaT information, but also the information from the preceding equipped vehicles using connected vehicle technology. Compared to the previously developed eco-approach algorithm which only utilizes the SPaT information, the proposed system has shown much greater network-wide benefits, especially during higher levels of congestion. The system performance has been evaluated in a series of simulation studies by varying different parameters, including congestion level, penetration rate, communication range, and communication delay.

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“…A machine learningbased approach is presented in [19], where smart phones equipped with cameras predict the phase of the traffic lights. Field testing of fuel optimal control strategies using SPAT information with real vehicles is shown by Xia et al [20].…”

mentioning

confidence: 99%

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

HomChaudhuri

Vahidi

Pisu

2017

IEEE Trans. Contr. Syst. Technol.

158

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In this paper, we develop a fast model predictive control (MPC)-based fuel economic control strategy for a group of connected vehicles in urban road conditions. The proposed control strategy is decentralized in nature, as every vehicle evaluates its own strategy using only neighborhood information. Along with the vehicle-to-vehicle communication, we exploit the signal phase and timing information from traffic lights to develop computationally efficient MPC-based strategies that reduce stopping at red lights and improve the fuel economy for a group of vehicles. The simulation results indicate the improvement in group performance and computational advantages of our proposed method.Index Terms-Connected vehicles, fast model predictive control (F-MPC), fuel economy, intelligent transportation system, optimal control.

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Field operational testing of ECO-approach technology at a fixed-time signalized intersection

Cited by 80 publications

References 7 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

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

Fast Model Predictive Control-Based Fuel Efficient Control Strategy for a Group of Connected Vehicles in Urban Road Conditions

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