This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Eco-Approach and Departure (EAD) has been considered as a promising eco-driving strategy for vehicles traveling in an urban environment, where information such as signal phase and timing (SPaT) and geometric intersection description is well utilized to guide vehicles passing through intersections in the most energy-efficient manner. Previous studies formulated the optimal trajectory planning problem as finding the shortest path on a graphical model. While this method is effective in terms of energy saving, its computation efficiency can be further enhanced by adopting machine learning techniques. In this paper, we propose an innovative deep learning-based queue-aware eco-approach and departure (DLQ-EAD) system for a plug-in hybrid electric bus (PHEB), which is able to provide an online optimal trajectory for the vehicle considering both the downstream traffic condition (i.e. traffic lights, queues) and the vehicle powertrain efficiency. Based on optimal solutions obtained from the graph-based trajectory planning algorithm (GTPA), a deep neural network (DNN) is developed to learn the optimal vehicle speed for the next time step given its current state. It is demonstrated that the trained DNN can provide energy-efficient trajectories with high computational efficiency and high flexibility adopting to dynamic changes in the surrounding environment. To address the impact of downstream traffic, a queue prediction model is further developed using data from radars and connected vehicles (CVs), as well as signal timing data from SPaT messages. A comprehensive simulation study in the microscopic traffic modeling software PTV VISSIM shows that the proposed DLQ-EAD can achieve 18.7%-24.0% energy efficiency improvements for a single PHEB on various traffic congestion levels. The proposed queue prediction model can be of practical significance even at low penetration rates of CVs. Specifically, additional energy savings of 2.0%-8.2% can be further achieved with 20% vehicles in the network.
- Fei Ye - University of California
- Peng Hao - University of California
- Guoyuan Wu - University of California
- Danial Esaid - University of California
- Kanok Boriboonsomsin - University of California
- Zhiming Gao - Oak Ridge National Laboratory
- Tim LaClair - Oak Ridge National Laboratory
- Matthew Barth - University of California
CitationYe, F., Hao, P., Wu, G., Esaid, D. et al., "Deep Learning-Based Queue-Aware Eco-Approach and Departure System for Plug-In Hybrid Electric Buses at Signalized Intersections: A Simulation Study," SAE Technical Paper 2020-01-0584, 2020, https://doi.org/10.4271/2020-01-0584.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- “National Greenhouse Gas Emissions Data Report,” Tech. Rep., Washington, DC, 2013.
- Transportation Energy Data Book, U.S. Department of Energy, Washington, DC, 2014.
- Zheng, J. and Liu, H.X. , “Estimating Traffic Volumes for Signalized Intersections Using Connected Vehicle Data,” Transportation Research Part C: Emerging Technologies 79:347-362, 2017.
- Yang, K. and Menendez, M. , “Queue Estimation in a Connected Vehicle Environment: A Convex Approach,” IEEE Transactions on Intelligent Transportation Systems 20:2480-2496, July 2019.
- Ye, F., Wu, G., Boriboonsomsin, K., Barth, M.J., Rajab, S., and Bai, S. , “Development and Evaluation of Lane Hazard Prediction Application for Connected and Automated Vehicles (CAVS),” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2872-2877, Nov 2018.
- Xu, Z., Zhao, X., Zhang, M.H., Xu, Z. et al. , “DSRC Versus 4G-LTE for Connected Vehicle Applications: A Study on Field Experiments of Vehicular Communication Performance,” Journal of Advanced Transportation 2017(2750452), 2017.
- Hao, P., Wu, G., Boriboonsomsin, K., and Barth, M.J. , “Preliminary Evaluation of Field Testing on Eco-Approach and Departure (EAD) Application for Actuated Signals,” in Proc. Int. Conf. Connected Veh. Expo (ICCVE), 2015, 279-284.
- European Commission , “The Compass4d Project,” [Online], available at: https://trimis.ec.europa.eu/project/compass4d.
- Ye, F., Hao, P., Qi, X., Wu, G. et al. , “Prediction-Based Eco-Approach and Departure at Signalized Intersections with Speed Forecasting on Preceding Vehicles,” IEEE Transactions on Intelligent Transportation Systems 20:1378-1389, April 2019.
- Hao, P., Wu, G., Boriboonsomsin, K., and Barth, M.J. , “Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic,” IEEE Transactions on Intelligent Transportation Systems, 2018.
- Barth, M., Mandava, S., Boriboonsomsin, K., and Xia, H. , “Dynamic Eco-Driving for Arterial Corridors,” in Integrated and Sustainable Transportation System (FISTS), 2011 IEEE Forum on, 2011, 182-188, IEEE.
- Chen, Z., Zhang, Y., Lv, J., and Zou, Y. , “Model for Optimization of Ecodriving at Signalized Intersections,” Transportation Research Record 2427(1):54-62, 2014.
- Huang, X., and Peng, H. , “Speed Trajectory Planning at Signalized Intersections Using Sequential Convex Optimization,” in Proc. Amer. Control Conf. (ACC), 2992-2997, 2017.
- Hao, P., Boriboonsomsin, K., Wu, G., Gao, Z., LaClair, T.J., and Barth, M.J. , “Deeply Integrated Vehicle Dynamic and Powertrain Operation for Efficient Plug-In Hybrid Electric Bus,” Jan. 2019.
- Comert, G. and Cetin, M. , “Queue Length Estimation from Probe Vehicle Location and the Impacts of Sample Size,” European Journal of Operational Research 197(1):196-202, 2009.
- Badillo, B.E., Rakha, H., Rioux, T.W., and Abrams, M. , “Queue Length Estimation Using Conventional Vehicle Detector and Probe Vehicle Data,” in 2012 15th International IEEE Conference on Intelligent Transportation Systems, 1674-1681, Sep. 2012.
- Lighthill, M.J. and Whitham, G.B. , “On Kinematic Waves I. Flood Movement in Long Rivers,” Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences 229(1178):281-316, 1955.
- Ye, F., Hao, P., Wu, G., Esaid, D., Boriboonsomsin, K., and Barth, M.J. , “An Advanced Simulation Framework of an Integrated Vehicle-Powertrain Eco-Operation System for Electric Buses,” in 2019 IEEE Intelligent Vehicles Symposium (IV), 2080-2085, June 2019.
- U. D. of Transportation , “NGSIM - Next Generation Simulation,” accessed May 2007, [Online], available at: http://www.ngsim.fhwa.dot.gov, 2019.
- Transportation Research Board , “Highway Capacity Manual, Sixth Edition: A Guide for Multimodal Mobility Analysis.”