8/16/18 1 Xiaopeng (Shaw) Li Associate Professor, Susan A. Bracken Faculty Fellow Department of Civil and Environmental Engineering, University of South Florida 8/16/2018 CUTR Webinar Series Operations and Planning for Connected Autonomous Vehicles: From Trajectory Control to Capacity Analysis 2 Freeway Stop-and-Go Traffic
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8/16/18
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Xiaopeng (Shaw) LiAssociate Professor, Susan A. Bracken Faculty Fellow
Department of Civil and Environmental Engineering,
University of South Florida
8/16/2018
CUTR Webinar Series
Operations and Planning for Connected Autonomous Vehicles: From Trajectory Control to Capacity Analysis
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Freeway Stop-and-Go Traffic
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Arterial Operations
Suboptimal timing – extra delay
Stop-and-go waves – excessive fuel consumptions
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Adverse Impacts
Congestion Exacerbate delay (3.7 billion hours/year) and
Shooting heuristic (SH) A small number of analytical sections
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Benchmark vs. SH
Reference *Ma, J., Li, X., Zhou, F., Hu, J. and Park, B. 2017. “Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization” Transportation Research Part B, 95, 421-441.*Zhou, F., Li, X. and Ma, J. 2017. “Parsimonious shooting heuristic for trajectory design of connected automated traffic part I: Theoretical analysis with generalized time geography.” Transportation Research Part B, 95, 394-420.* Li, X., Ghiasi, A. and Xu, Z. “A piecewise trajectory optimization model for connected automated vehicles: Exact optimization algorithm and queue propagation analysis” Transportation Research Part B, under revision.
Reference *Yao, H., Cui, J., Li, X., Wang, Y. and An, S., 2018, “A Trajectory Smoothing Method at Signalized Intersection based on Individualized Variable Speed Limits with Location Optimization”, Transportation Research Part D, 62, pp. 456-473
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CAV Trajectory Optimization
Freeway Speed Harmonization
I. Predictionproblem
II. Shootingheuristicproblem
Reference: * Ghiasi, A., Li, X., Ma, J. and Qu, X. 2018. “A Mixed Traffic Speed Harmonization Model with Connected Automated Vehicles”, Transportation Research Part C. Under Revision
Exit time
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Joint Trajectory and Signal Optimization
Problem setting
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Joint Trajectory and Signal Optimization
Signalized intersection
2000, 1500 vph 20 m/s 500 m
4000 vph 40 6 m
7 m 2.7 s 0.6 s
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Joint Trajectory and Signal Optimization
Work-zone
2000, 1500 vph 20 m/s 500 m
4000 vph 40 6 m
250 m 27 s 0.6 s
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Deep Learning Based Trajectory Control
• Using deep neural networks to design adaptive CAV controllers
Implication to Capacity Analysis & Planning
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Trajectory Control → Capacity Analysis
CAV control → Heterogeneous headways in mixed traffic
0.7 2.4 h (s)
Freq.
0.3 2.0 h (s)
0.5 2.6 h (s)
0.6 2.6 h (s)
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Capacity Analysis
CAV technology uncertainties Will CAV reduce headways?
Google car pulled over for being too slowhttp://www.bbc.com/news/technology-34808105
Theorem 1: ≤ for any finite N Theorem 2: When 1, → → ∞
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Capacity analysis
Numerical analysis
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Optimistic Headway Conservative Headway
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Application – Lane Management
Determine the optimal number of CAV lanes
≔ 1/≔ ,
≔0, 1,
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∑ ∈ , ∈
≔ min ,
Reference: * Ghiasi, A., Hussein, O., Qian, S.Z. and Li, X., 2017. “A mixed traffic capacity analysis and lane management model for connected automated vehicles: a Markov chain method”, Transportation Research Part B, 106, pp. 266-292.
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Field Experiments
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Field Experiments – Pure HVs
15 HVs following tests in Harbin, China (collaborating with Harbin Institute of Technology)
spee
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Lead vehicle Following vehicles
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Data Collection on Public Roads Video-Based Intelligent Road Traffic Universal Analysis Tool
(VIRTUAL) (Provisional Patent #. 62/701,978)
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Data Collection on Public Roads Video-Based Intelligent Road Traffic Universal
HV following CAV/HV at the 2.4 km test track at Chang’an University, China
Test different drivers, different CAV speed
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Field Experiments
HV following CAV/HV at the 2.4 km test track at Chang’an University, China
Test different drivers, different CAV speed
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Field Experiments – Mixed Traffic
Difference between HV →CAV and HV→HV
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Acknowledgements Students Fang Zhou (Li’s student) Amir Ghiasi (Li’s student) Omar Hussain (Li’s student) Handong Yao (Harbin Institute of Technologies) Zhen Wang (Chang’an University)
Collaborators Jiaqi Ma (University of Cincinnati) Zhigang Xu (Chang’an University) Jianxun Cui (Harbin Institute of Technologies) Sean Qian (CMU)
Funding agencies
Thank you!Q & A?
Xiaopeng (Shaw) Li, Ph.D.Assistant Professor, Susan A. Bracken Faculty FellowDepartment of Civil and Environmental EngineeringUniversity of South Florida4202 E. Fowler Avenue, ENG 207 Tampa, FL 33620-5350E-mail: [email protected]: 813-974-0778; Fax: 813-974-2957Website: http://cee.eng.usf.edu/faculty/xiaopengli/