TRAFFIC SIGNAL CONTROL SYSTEMS AT CONNECTED VEHICLE CORRIDORS: THEORIES AND IMPLEMENTATION

Transcription

1 TRAFFIC SIGNAL CONTROL SYSTEMS AT CONNECTED VEHICLE CORRIDORS: THEORIES AND IMPLEMENTATION LI ZHANG, PH.D., P.E CONNECTED INC., PRINCIPLE ASSOCIATE PROFESSOR DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING MISSISSIPPI STATE, MS LEI ZHANG, PH.D. CONNECTED INC., PRINCIPLE RESEARCH ASSISTANT PROFESSOR DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING MISSISSIPPI STATE, MS 39762

2 OUTLINE Introduction Architectures Traffic Models Algorithms Case Study Conclusion

3 INTRODUCTION Connected Vehicle Technology Vehicle-to-Infrastructure (V2I) FHWA support research project Project Manager: Debra Curtis Benefits of connected vehicle technology

4 FHWA PROJECT Connected Vehicle Technology in Signalized Intersection Version 1 ( ) Federal Highway Administration. Safety and Mobility Benefits of Connected Vehicles_Vol_I. McLean, VA. Publication No. FHWA- JPO Version 2 (2016) Federal Highway Administration. Safety and Mobility Benefits of Connected Vehicles_Vol_II. McLean, VA. Publication No. FHWA- JPO

5 CONNECTED VEHICLE TECHNOLOGY Vehicle-to-Infrastructure (V2I) Wireless communication between on-board device (vehicle) and road side infrastructure (traffic signal) Basic Safety Message (BSM) contain vehicle status (speed, location, etc.)

6 RETIMING Volume Turning Percentage Travel Time LI ZHANG, PH.D. P.E., F. ASCE., F.ITE 9/17/2018 6

7 CONNECTED VEHICLE + REAL TIME RETIMING Volume: Loop detector Turning Percentage, BSM Vehicle Trajectories: BSM + Loop Detector LI ZHANG, PH.D. P.E., F. ASCE., F.ITE 9/17/2018 7

8 OBJECTIVES AND STATUS OVERVIEW A Framework of V2I Development Environment Connected Vehicle Application Progression/Coordination Strategies Queue Management Strategies Delay Minimization Implementable Mobility/Energy/Safety Benefits With Connected Vehicle Information Without Connected Vehicle Information Motivation: Encourage State/Local Early Deployment

9 SYSTEM ARCHITECTURE Fiber/Wifi ALTPOM Field Master NTCIP Optional Field Processor NTCIP Complaint Controller

10 NTCIP 1202 A FRAMEWORK OF V2I DEVELOPMENT ENVIRONMENT Communications-in-the-loop Simulation Scope of Work Connected Vehicle OBEs BSM BSM RSE Signal Timing and Phaseing SPaT Systems SPaT Applications Equipment/ Other Projects BSM Interface SBIR Phase I, II and IIB ETFOMM ETAPI SNMP Vehicle Trajectory NTCIP Compliant Controller or SCOPE Hardware-in-the-loop Simulation TCA/BSM (Noblis)

11 Signal Timing monitoring NTCIP NTCIP Towards Implementation Traffic Signal Detector Information From Detector Units Signal Indications ETFOMM 2. NTCIP ETFOMM D-CS Detectors From Field D-CS Detectors From Simulation Detector Interface 2. NTCIP 3. D-CS System Drop/Place Extension Call Signal Status Low Cost Computer Signal Status Signal Indications Traffic Signal Detector From Simulation 1. Enhanced SCOPE Drop/Place Extension Call LI ZHANG, PH.D. P.E., F. ASCE., F.ITE 9/17/ SCOPE of This Project Industrail Computer US DOT SBIR

12 MOBILITY, SAFETY AND ENVIRONMENTAL PERFORMANCE EVALUATION FHWA Open Source Microscopic Simulation Software ETFOMM FHWA Open Source Surrogate Safety Assessment Model ETFOMM+SSAM 10% of all FHWA s Open Source Downloads EPA Physical Emission Rate Estimator Model

13 ITSFORGE.NET

14 ARCHITECTURES: CENTRALIZED SYSTEM BSM DATA DETECTOR DATA TRAFFIC SIGNAL TIMING PLAN BSM DATA DETECTOR DATA TRAFFIC SIGNAL TIMING PLAN BSM DATA DETECTOR DATA TRAFFIC SIGNAL TIMING PLAN Centralized Server BSM DATA DETECTOR DATA TRAFFIC SIGNAL TIMING PLAN Intersection Intersection Intersection Intersection

15 ARCHITECTURES: DISTRIBUTED SYSTEM Forecasting Queue Length Queue Length Queue Length Data Server Queue Length BSM + Detector Data Queue Length Forecasting BSM + Detector Data Queue Length Forecasting BSM + Detector Data Queue Length Forecasting BSM + Detector Data Queue Length Forecasting Intersection Intersection Intersection Intersection

16 TWO-STAGE DISTRIBUTED SYSTEM 16

17 TRAFFIC MODELS Arrival on Green Queue Length Optimization Delay Optimization

18 MAXIMUM ARRIVAL ON GREEN Real-Time Data Collection Predicted Arr./Dept Arr. On Green-Residual Que Split Adjustment Offset Optimization Predicted Queue Length Split/Cycle Length Adjustment Split/Cycle Length/ Offset Optimization Implement New Offset, Split, and Cycle Length

19 QUEUE LENGTH FORECAST Initial Queue Length at the beginning of each projection horizon Signal indication is red Signal indication is green Four possible regions Queue Region Queue Formulation Region Progression Region 1 Progression Region 2 Connected Vehicle Technology in Queue Length Forecast Adjustment 19

20 SPACE-TIME DIAGRAM OF QUEUE LENGTH FORECAST 20

21 QUEUE LENGTH FORECAST ADJUSTMENT Vehicle Location Vehicle Speed is Zero? 21

22 QUEUE LENGTH OPTIMIZATION

23 DELAY MINIMIZATION

24 ALGORITHMS Newton s Method Steepest Descent Method Nelder-Mean Method

25 OPTIMIZATION ALGORITHMS NEWTON METHOD 25

26 OPTIMIZATION ALGORITHMS STEEPEST DESCENT METHOD 26

27 OPTIMIZATION ALGORITHMS NELDER-MEAN METHOD fminsearch in Matlab Estimate function to Matlab Call fminsearch to get optimize value 27

28 CASE STUDY Two Case Studies State Street in Jackson MS VA123 in Mclean VA

29 STATE STREET IN JACKSON, MS

30 MS Case Studies, with Maximum Arrivals on Green

31 PENETRATION RATE AND DELAYS Arterial Control Delays Both Directions Base Case 10% 25% 50% Connected Vehicle Penetration Rate Progression Opposite Progression Opposite Both Directions LI ZHANG, PH.D. P.E., F. ASCE., F.ITE 9/17/

32 Results of Major Streets LOS E LOS D LOS C LOS C LOS C LOS C LOS D LOS C LOS C 9/17/ Control Delays Per Vehicle and LOS of Major Streets

33 RESULTS OF MINOR STREETS LOS F LOS D LOS D LOS D LOS D LOS D LOS E LOS D LOS D 33 9/17/2018 Figure 8: Control Delays Per Vehicle and LOS of Minor Streets

34 MS Case Studies, with Minimizing Queue Lengths

35 MS CASE STUDY WITH MINIMIZING QUEUE LENGTHS Same case as Arrival on Green 5 Coordinated Intersections Optimized by TRANSYT-7F Penetration Rate 50% (Arrival on Green, 10~50%) Simulation Run Times: Each Scenario: 10 Times Average Delay and Travel Time for 5 Intersections

36 ARRIVAL ON GREEN VS QUEUE LENGTH MINIMIZATION ARRIVAL ON GREEN VS QUEUE LENGTH MINIMIZATION IN MS CASE STUDY MAJOR MINOR BASE Arrival On Green Queue Length Minimization

37 VA123 IN MCLEAN, VA

38 SCENARIOS DESIGN Scenario (Volume Ratio) 1.0 (Base) Intersection 4 WB NB SB Intersection 5 NB SB Intersection 6 NB SB Intersection 7 EB NB SB

39 SIMULATION RESULT EVALUATION Simulation Results on Mobility Control Delay and Throughput Simulation Results on Fuel Consumption and Emission Fuel consumption of total network Emission (CO, CO2, HC, and NOx) Simulation Results on Safety Surrogate Safety Assessment Model (SSAM) safety analysis tool Bar chart of conflicts Signal Timing Plan Analysis Simulation Results on Optimized Time Consumption Different types of optimized algorithm 39

40 QUEUE LENGTH VS. DELAY MINIMIZATIONS 50.00% QUEUE MINIMIZATION VS DELAY MINIMIZATION 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% 10% 25% 50% 60% 70% QUEUE MAJOR DELAY MAJOR QUEUE MINOR DELAY MINOR

41 SCENARIO CONTROL DELAY 41

42 MOBILITY BENIFTS Delay Reduction 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 33.80% 38.91% 29.30% 31.20% 25.10% 24.90% 28.10% 24.20% 23.80% 27.80% 26.40% 29.47% 17.30% 16.07% 20.90% 15.43% 16.40% 19.10% 25.98% 9.70% 19.70% 20.40% 8.40% 14.71% 15.40% 6.70% 5.10% 9.10% 16.00% 15.50% 7.70% 6.30% 5.40% 2.70% 13.10% 8.30% 15.60% 10.54% 5.60% 2.50% 6.80% 11.80% 12.60% 4.30% 7.50% 10.90% 6.10% 9.57% 4.80% 1.90% 8.80% 3.50% 1.80% 5.30% 2.20% 2.70% 3.90% 1.80% S1 Minor S2 Minor S3 Minor S4 Minor S5 Minor S6 Minor S1 Major S2 Major S3 Major S4 Major S5 Major S6 Major 40.92% 43.19% Penetration Rate 10% 25% 50% 60% 70% 42

43 MOBILITY BENEFITS (DISTRIBUTED SYSTEM) 1.Two-Stage DS without Time Limit t with All Optimization Variables Summary 10% 25% 50% 60% 70% Major % % % % % Direction Minor Direction % % % % % 2. One-Stage CS without Time Limit Summary 10% 25% 50% 60% 70% Major % % % % % Direction Minor Direction -9.57% % % % % 3.Two-Stage CS without Time Limit with Two Set Optimization Variables Summary 10% 25% 50% 60% 70% Major % % % % % Direction Minor Direction % % % % % 43

44 MOBILITY RESULT (DISTRIBUTED SYSTEM) 1. Two-Stage DS with Time Limit Summary 10% 25% 50% 60% 70% Major Direction % % % % % Minor Direction -7.43% -9.54% % % % 1. Two-Stage DS without Time Limit Summary 10% 25% 50% 60% 70% Major % % % % % Direction Minor Direction -8.37% % % % % 1. Two-Stage DS without Time Limit & Full Optimization Summary 10% 25% 50% 60% 70% Major Direction Minor Direction % % % % % % % % % %

45 FUEL CONSUMPTION AND EMISSION (EPA S PERE MODEL) 30.00% Emission Reduction Penetration S1 S2 S3 S4 S5 S6 10 percent percent percent percent percent % 20.00% 15.00% 10.00% 5.00% 0.00% S1 S2 S3 S4 S5 S % 25% 50% 60% 70%

46 SAFETY Total Rear end Crossing Lane change 46

47 ALGORITHMS PERFORMANCE MEASURE Optimization Method Newton Method S6 Steepest Descent Method S6 Nelder-Mead Method S6 Average Time Consumption Seconds Seconds Seconds

48 CONCLUSION Architectures Traffic Models Algorithms

49 ARCHITECTURES Distributed System VS Centralized System in VA-123 Mclean, VA Two Stages Distributed System with all variables optimization (26%-44% control delay reduction on major; 10%-16% control delay reduction on minor) Centralized System (25%-43% control delay reduction on major; 9%-16% control delay reduction on minor)

50 TRAFFIC MODELS Arrivals on Green in State St. Jackson, MS 30.3% control delay reduction on major and 14.7% control delay reduction on minor Queue Length Optimization in State St. Jackson, MS 42.1% control delay reduction on major and 20.9% control delay reduction on minor Queue Length Optimization in VA123 Mclean, VA 15%-33% control delay reduction on major and 5%-10% control delay reduction on minor Delay Optimization in VA123 Mclean VA 25%-43% control delay reduction on major; 9%-16% control delay reduction on minor

51 ALGORITHMS Time Consumption Steepest Descent Method (103.8 seconds) is faster than Newton s Method and Nelder- Mead Method Performance on Mobility Newton s Method (29.7% total network control delay reduction) is better than Steepest Descent Method (28.9%) and Nelder-Mead Method (28.1%)

52 FUTURE WORK Hardware-Communication-in-the Loop Simulation Improvement Model/Algorithm Efficiency AI and Trajectory Based Algorithms Distributed Algorithm Architecture for Large Network

53 Thank you!