Network-Wide Impacts of Connected Vehicles on Mobility: An ...

Network-Wide Impacts of Connected Vehicles on Mobility: An ...

TSPCV Experiment at the Virginia Tech Smart Road Presenter: Seyedehsan Dadvar, PhD Candidate US DOT ITS T3e Webinar Introduction Transit Signal Priority (TSP) has been proposed and studied as an efficient way of improving transit operations. Evaluation of theoretical benefits: Simulation-based evaluations Many Field tests Few Limited number of field tests under CV environment Multi-Modal Intelligent Traffic Signal System (MMITSS) project and some others almost in parallel with this study. 2 US DOT ITS T3e Webinar Objectives To implement TSPCV in real world on the Virginia Smart Road The 1st TSPCV field experiment on the Smart Road of the Virginia Tech Transportation Institute (VTTI) To confirm software and hardware compatibility To illuminate TSPCV performance and reveal Global

Positioning System (GPS) requirements (regular and differential) 3 US DOT ITS T3e Webinar Methodology Bus arrival time components: TSPCV components: Roadway geometry Bus detection Roadway speed limit TSP timing plan and bus speed calculation Speed of other vehicles Logic assessment and implementation Distance of RSE to the intersection on Virginia Tech Smart Road 4 US DOT ITS T3e Webinar

Experiment Site Virginia Tech Smart Road A test-bed research facility managed by VTTI Owned and maintained by the Virginia Department of Transportation (VDOT) Length: 2.2 miles (3.5 km) 2 paved lanes 7 roadside equipment units that facilitate CV communications + 2 mobile roadside equipment sites A signalized intersection with complete signal phase and timing (SPaT) using remote controls A connected-vehicle-compatible intersection controller model Source: http://www.vtti.vt.edu/facilities/virginia-smart-road.html US DOT ITS T3e Webinar 5 6 US DOT ITS T3e Webinar Experiment Vehicle & Devices Vehicle: Nissan Infiniti FX35 (2005) enabled with CV features: GPS devices: Regular GPS (GPS from NextGEN Head Unit) Differential GPS (Novatel Flexpak6 located in vehicle trunk) OBE: Savari OBE S100 located in trunk DAS: Nextgen DAS located in trunk for data collection:

GPS position, GPS speed, etc. TSPCV algorithm: on a Dell laptop User interface: display screen (HDMI Feelworld 5 HD TFT LCD Monitor RSE: Savari StreetWave Traffic signal controller: Custom proprietary interface with D4 Controller connected to Control Room US DOT ITS T3e Webinar 7 TSP Algorithm (Laptop) RSE TSP GPS and Other OBE Devices RSE Distance (0.5 + d) 0.5 miles 0

d ` 8 US DOT ITS T3e Webinar Experiment Experimental Scenarios Signal Phasing Cycle length = 90 seconds TSPCV30 Experiment Scenarios Arrival types Speed Limit Beginning of Red phase Cycle Middle of Red phase length Start Time End of Red phase Subtotal Total

GPS Type 3 Regular GPS 40 sec. X 10 trials 50 sec. X 3 trials 60 sec. X 3 trials 70 sec. X 3 trials 80 sec. X 3 trials 22 trials 55 45 mph Differential GPS 2 40 sec. X 3 trials 50 sec. X 2 trials 60 sec. X 3 trials 70 sec. X 3 trials 80 sec. X 3 trials 14 trials 36 trials Regular Differential

Speed Limit 45 mph (major arterials) 9 US DOT ITS T3e Webinar Experiment Examination Prior to the experiment; The study team visually confirmed the compatibility of the algorithm and the equipment (OBE and RSE) at the VTTI. Data flow was checked and tested. 10 US DOT ITS T3e Webinar Advisory Speed Original TSPCV Signal Phasing RSE

0.5( )mile 11 US DOT ITS T3e Webinar Experiment Data Collection From the beginning of each trial the end: Time Coordinated Universal Time (UTC) Time Original Timing Plan TSPCV Timing Plan (actually was activated after the bus passed 0.5 miles point) Bus speed Bus location Distance to intersection Traffic light status at intersection 12 US DOT ITS T3e Webinar Experiment Data Analysis Delay computation Predicted arrival time without TSPCV Predicted Relative Cycle length Arrival Time without TSPCV at 0.5 miles Predicted arrival time Actual arrival time

Delay w/o TSPCV Delay TSPCV Reduced Delay (sec. & %) 13 US DOT ITS T3e Webinar Evaluation Regular GPS 40 50 60 70 80 All 10 3 3 3 3 22 10 3 3

3 3 22 100% 100% 100% 100% 100% 100% US 10 3 3 3 DOT 3 22 100% 100% 100% 100% ITS T3e 100% 100%

% # TSP Green with Shorter Delay without Green Extension % # TSP Green with Shorter Delay % # TSP Green Provided # Cycle length Start Time Scenario Success Rate 4 2 1 1 Webinar 2 10 40% 67%

33% 33% 67% 45% 14 Evaluation Delay Reduction and Original Red Light Arrival Time Combined GPS Devices 45.0 Reduced Delay with TSP (Sec.) 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 0.0 5.0 10.0 15.0

20.0 25.0 30.0 35.0 40.0 Predicted Relative Original Red Light Arrival Time w/o TSP (Sec.) US DOT ITS T3e Webinar 45.0 50.0 15 Evaluation Delay Reduction and Original Red Light Arrival Time (Cont.) Combined GPS Devices 80% Reduced Delay with TSP (%) 70% 60%

50% 40% 30% 20% 10% 0% 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 Predicted Relative Original Red Light Arrival Time w/o TSP (Sec.) US DOT ITS T3e Webinar 45.0

50.0 16 Evaluation GPS Type Effect Regular GPS Differential GPS 45.0 Reduced Delay with TSP (Sec.) 40.0 35.0 30.0 25.0 20.0 15.0 10.0 5.0 0.0 0.0 5.0 10.0 15.0 20.0

25.0 30.0 35.0 40.0 Predicted Relative Original Red Light Arrival Time w/o TSP (Sec.) US DOT ITS T3e Webinar 45.0 50.0 17 Evaluation GPS Type Effect (Cont.) Regular GPS Differential GPS 80% Reduced Delay with TSP (%) 70% 60%

50% 40% 30% 20% 10% 0% 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 Predicted Relative Original Red Light Arrival Time w/o TSP (Sec.) US DOT ITS T3e Webinar 45.0

50.0 18 Evaluation GPS Type Effect (Cont.) t-Test: Paired Two Sample for Means Mean Variance Observations Pearson Correlation Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail Actual Overall Time R-GPS D-GPS Reduced Delay (Sec.) R-GPS D-GPS

Reduced Delay (%) R-GPS D-GPS Green Extension R-GPS D-GPS 59.14 48.13 14 0.75 0 13 1.36 0.099 1.77 0.20 2.16 26.36 156.86 14 0.98 0 13 -0.65 0.26 1.77 0.53

2.16 0.56 0.015 14 0.87 0 13 -0.41 0.35 1.77 0.69 2.16 1.07 2.84 14 -0.040 0 13 -0.12 0.45 1.77 0.90 2.16 57.43 16.11 14

26.86 168.75 14 0.57 0.022 14 1.14 1.67 14 19 US DOT ITS T3e Webinar Conclusions TSPCV algorithm implementation program, data packets, and communications devices worked properly in a CV environment. The implementation of the TSPCV algorithm was successful reduce an average of 57% of bus delay or an average of 26.9 seconds TSPCV algorithm provided the bus green time at a 100% success rate (50% with Green Extension) & delay reduction (32%-75%) The difference in performance of regular and differential GPS devices was not statistically significant. US DOT ITS T3e Webinar

20 Future Work TSPCV worked properly at a controlled environment but future research at real world with general traffic is necessary. It is likely that the 100% TSP success rate would not be always possible if queues and unexpected delays happen at the intersections. Test was conducted under a suburban environment (0.5 miles intersection spacing), it was because the controller that was developed had the limitation. Since experiment, the research team has enhanced the controller for more urban and tight spacing, future work could consider extending evaluation to urban environment. US DOT ITS T3e Webinar 21 More Information Report: Next Generation Transit Signal Priority with Connected Vehicle Technology (Chapter 4) https://vtechworks.lib.vt.edu/handle/10919/72257 ASCE Journal of Transportation Engineering Transit Signal Priority Experiment in a Connected

Vehicle Technology Environment In Press 22 US DOT ITS T3e Webinar Contact Seyedehsan Dadvar Email: [email protected] Google Scholar: https://scholar.google.com/citations?user=vLrVEZoAAAAJ&hl=e n ResearchGate: https://www.researchgate.net/profile/Seyedehsan_Dadvar 23 US DOT ITS T3e Webinar

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