CAV Trajectory Optimization & Capacity Analysis - Modeling Methods and Field Experiments Xiaopeng (Shaw) Li Assistant Professor, Susan A. Bracken Faculty Fellow Department of Civil and Environmental Engineering, University of South Florida 6/25/2018 Session 2B: Are We Ready for the AV Future? 7th Innovations in Travel Modeling Conference
Hope for CAV: Capacity Booster 2 People expect connected automated vehicles can significantly increase (or even multiple) high way capacity How to realize this potential? Human-driven traffic CAV traffic
Steps to Improve CAV Capacity 3 Microscopic trajectory control Reduce headway Improve traffic smoothness Macroscopic capacity analysis Understand the relationship between cav traffic characteristics (e.g., CAV penetration ratio) and macroscopic measures (e.g., traffic throughput) Validation Field experiments Data analysis
CAV Trajectory Optimization 4 Signalized Intersections Coordinate signal timing with vehicle trajectory control Human-driven traffic CAV traffic
Parsimonious Algorithms 5 Shooting heuristic (SH) A small number of analytical sections
Benchmark vs. SH 6 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.
CAV Trajectory Optimization 7 1000 900 800 700 Signalized Intersections Mixed Traffic (CAVs + Human-driven vehicles (HVS)) 1000 900 800 700 IVSL2 1000 900 800 700 IVSL2 Distance (m) 600 500 400 Distance (m) 600 500 400 Distance (m) 600 500 400 300 300 300 200 200 200 100 100 IVSL1 0 0 50 100 150 200 250 300 0 350 0 50 100 150 200 250 300 350 Time (s) (a) Time (s) (b) 100 IVSL1 0 0 50 100 150 200 250 300 350 Time (s) (f) 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
CAV Trajectory Optimization 8 Freeway Speed Harmonization Exit time I. Prediction problem II. Shooting heuristic problem 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
Trajectory Control Capacity Analysis 9 CAV control Heterogeneous headways in mixed traffic Freq. Freq. Freq. Freq. 0.7 2.4 h (s) 0.3 2.0 h (s) 0.5 2.6 h (s) 0.6 2.6 h (s)
Capacity Analysis 10 CAV technology uncertainties Will CAV reduce headways? Google car pulled over for being too slow http://www.bbc.com/news/technology-34808105
Capacity Analysis 11 Different technology scenarios Freq. Freq. Freq. Freq. 0.7 2.4 h (s) 0.3 2.0 h (s) 0.5 2.6 h (s) 0.6 2.6 h (s)
Capacity Analysis 12 CAV market penetration rate Low CAV market penetration rate High CAV market penetration rate 12
Capacity Analysis 13 CAV platooning intensity Low CAV platooning intensity High CAV platooning intensity 13
Analytical Capacity Formulation 14 Markov chain model 1 0 nn tt 11, h 11 tt 01, h 01 tt 10, h 10 tt 00, h 00 nn + 1
Analytical Capacity Formulation 15 Markov chain model PP 1 0,1 : CAV market penetration rate OO [ 1,1]: CAV platooning intensity TT tt 11 tt 10 tt 01 tt 00 15
Analytical Capacity Formulation 16 Approximate capacity cc NN 1 = NN 1 EE h nn nn=1 NN 1 NN 1 = h AA nnaann+1 nn=1 Theorem 1: cc cc for any finite N Theorem 2: When OO < 1, PPPP cc 1 ss SS,rr SS PP ss tt ssss h ssss cc aaaa NN 16
Capacity analysis 17 Numerical analysis Optimistic Headway Conservative Headway 17
Application Lane Management 18 Determine the optimal number of CAV lanes cc A 1/ h 11 qq AA mmmmmm PP 1 DD, ll AA cc A pp 1 mmmmmm 0, PP 1DD ll AA cc A mmmmmm 1, DD qq AA cc mixx 1 ss SS,rr SS pp ss tt ssss h ssss ll AA LL ll AA QQ qq AA + min DD qq AA, LL ll AA cc mixx 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.
CAV Fundamental Diagrams 19 Ongoing Research Throughput Capacity Density Reference: Qian, Z.S., Li, J., Li, X., Zhang, M. and Wang, H., 2017. Modeling heterogeneous traffic flow: A pragmatic approach. Transportation Research Part B, 99, pp.183-204.
Field Experiments 20 10 HVs following tests in Harbin, China (collaborating with Harbin Institute of Technology) 50 45 40 35 30 speed 25 20 15 10 5 0 100 200 300 400 500 600 time Lead vehicle Following vehicles
Field Experiments 21 HV following CAV/HV at the 2.4 km test track at Chang an University, China Test different drivers, different CAV speed
Field Experiments 22 HV following CAV/HV at the 2.4 km test track at Chang an University, China Test different drivers, different CAV speed
Field Experiments 23 Difference between HV-following-CAV and HV-following-AV
Acknowledgements 24 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 Fellow Department of Civil and Environmental Engineering University of South Florida 4202 E. Fowler Avenue, ENG 207 Tampa, FL 33620-5350 E-mail: xiaopengli@usf.edu Phone: 813-974-0778; Fax: 813-974-2957 Website: http://cee.eng.usf.edu/faculty/xiaopengli/