1. 1 Synthetic Approach for Scenario-based Performance Estimation of Connected Vehicles Operating at Highway Facilities 2015 International Conference on Information Technology and Intelligent Transportation Systems, Chang’an University December 12, 2015 Heng Wei, PhD, PE Professor and Director, ART-Engines Transportation Research Lab Hao Liu PhD Candidate The University of Cincinnati, USA

2. 2 Presentation Outline 1.Background 2.Problem Identification 3.Research Motivation 4.Methodology 5.Case Study Results and Discussion 6.Summary of Findings 7.Significance of Research

3. 3 CV Background Source: http://www.its.dot.gov/pilots/pdf/Pilot_Sunnyside.pdf An CV implementation scenario envisioned in the CV Pilots Deployment Program. Improve bottleneck throughput Reduce incident delay Manage diversions better

4. 4 Problem Identification DOTs and other stakeholders are more interested in scenarios about “what would happen when CV is deployed” Challenges to quantify effectiveness of CV: Complexity and uncertainty of interactions. ̶ Between the CV info and driver behaviors. ̶ Between CV affected driver behaviors and traffic operation. Lack of traffic flow models that capture the CV affected driver behaviors. Lack of simulation-based measurement system. ̶ Quantifying CV impact on traffic mobility, safety and emission in an integrated way.

5. 5 Research Motivation To clarify the cause-and-effect rationale between CV information and driver behaviors, as well as traffic performance in terms of the traffic mobility, safety, and emissions under various CV implementation scenarios. To quantitatively measure and understand the effectiveness and benefits of adapting CV and even long- term Autonomous Vehicles (AV) into today’s transportation systems. Only consider CV systems that provide information. Assume perfect communication environment.

6. 6 Methodology—Framework Simulation- Based System Case Studies 4 5

7. 7 Methodology—Modeling CV Affected Behaviors IDNameDefinition/Comments a Reaction time Delay between the emergence a traffic stimulus and the execution a relevant action. b Estimation errors The following distance and relative speed to the leader can only be estimated with limited accuracy. c Temporal anticipation Drivers can predict traffic situation for the next few seconds. d Spatial anticipation Drivers consider the immediate preceding and further vehicles ahead. e Desired distance Desired following distance a driver tries to maintain in the car-following state. f Lane-change desire Motivation for gaining speed advantage or continuing a route. g Desired speed The maximum speed a driver attempts to reach if there is no constraints from other road users. CV generates regulatory, warning or advisory messages based on safety and operation information exchanged among vehicles. Drivers adjust behaviors once receiving the messages. Details on modeling reaction time and estimation errors will be presented in the following slides. Remaining behaviors can be discussed in the Q&A session.

8. 8 Methodology—Modeling Reaction Time (RT) CV processes information faster than human drivers. RT modeling: add a delay term. Instant acceleration Acceleration function Delay term Spacing Speed Relative speed 1.35s 10%~50% reduction Design Value CV Non-CV CV

9. 9 Methodology—Modeling Estimation Errors

10. 10 Methodology—Modeling Traffic Flow under CV Incorporating CV affected driver behaviors into the car-following (CF) and lane-changing (LC) model.  Reaction time, estimation errors, spatial anticipation, desired speed, and desired distance are incorporated in the Intelligent Driver Model (IDM) CF model. Acceleration Maximum acceleration Speed term Spacing & relative speed term Free-flow component (FC) Interaction component (IC) IDM: Speed Desired speed Spacing Desired distance FC: IC: After incorporating driver behaviors, IDM becomes: Reaction time FC: Desired speed under CV IC: Estimation errors of relative speed Estimation errors of spacing Spatial anticipation Desired distance under CV

11. 11 Methodology—Modeling Traffic Flow under CV The lane-change desire is incorporated into the Hidas LC model. Non-CV case: LC desired if vehicles faster in target lane. LC essential if otherwise the driver could not continue the route. CV case: LC desired if vehicles faster in target lane. LC desired if a LC advisory message is received. LC essential if otherwise the driver could not continue the route.

12. 12 Methodology—Measuring Traffic Mobility, Safety and Emission Triggering events that cause traffic breakdowns. Caused by LCs, overreactions and delay in responses. Time (s) Distance (m) Speed (km/h) Shockwaves CV affected behaviors Traffic Disturbances Reduced frequency Altered temporospatial patterns Transformed intensity Interrupted propagation Mobility Emission Safety V2I V2I+V2V Throughput, density, delay, and speed Emission rate # of conflicts, conflict time%

13. 13 Case Study Site description: NB I-71, near Exit 12 in greater Cincinnati area, Ohio. 3 freeway lanes, 1 on-ramp lane. Freeway peak hour volume 4400 veh/hr, 4.5% truck; Ramp peak hour volume 950 veh/hr, 1% truck. Recurrent congestions, isolated bottleneck. Case Study Site Data collection: Traffic count from 6-22 to 26, 2015, 3:30- 6:30pm. Travel time and speed collected 2013-2015, multiple weekdays, 7:00-9:00am and 4:00- 6:00pm. Travel time and speed collected using GPS equipped probe cars.

14. 14 Case Study—Case Description Forward collision warning (FCW) system: FCW sends warning information to a driver if there is risk of rear- end collision. Decreased reaction time and estimation errors  Stabilize traffic. Decreased number of anticipated vehicles and increased desired following distance (time headway)  Destabilize traffic. Impact of FCW on traffic mobility, safety and emission? Case study description: Two cases: non-CV and CV cases. Multiple FCW penetration rates considered: 35%, 50%, 65%, 80% and 100%.

15. 15 Case Study—Preliminary Results Analysis Overview of FCW Impact Mobility Safety Emission FCW improves mobility, emission and safety. Improvement increases with larger penetration rate. 40%  turning point for mobility and emission curves. 80%  turning point for safety curves.

16. 16 Case Study—Preliminary Results Analysis Driver behavior adjustments because of FCW Behavioral Parameters Penetration Rate of FCW 0%35%50%65%80%100% Mean reaction time (s)1.401.251.211.181.121.03 Mean estimation error of following distance (m) 0.810.640.590.550.480.38 Mean number of anticipated vehicles 6.005.925.905.89 5.87 Mean desired time headway (s)0.720.730.74 Demonstration of decreased reaction time, estimation errors and number of anticipated vehicles. (averaged value per vehicle over the 4400 mainstream vehicles plus 950 on-ramp vehicles) Demonstration of increased time headway. Stabilization effect  affect reaction time and estimation errors Destabilization effect  affect number of anticipated vehicles and headway VS.

17. 17 Case Study—Preliminary Results Analysis Stabilization effect vs. destabilization effect  mobility and emission SEO: stabilization effect only  assume FCW only affects reaction time and estimation errors. DEO: destabilization effect only  assume FCW only impacts anticipated vehicles and headway. Parameters (5350 simulated vehicles) Penetration Rate of FCW 100%100% (SEO)100% (DEO) Total delay (hour) 2.332.127.10 Total CO 2 (kg) 292.58275.90514.28 Total PM 2.5 (g) 4.934.399.89 Parameters 0% (Non-CV) 100% Penetration Rate of FCW Total delay (hour) 10.942.332.954.176.8714.79 Total CO 2 (kg) 608.93292.58295.48322.87401.57642.95 Total PM 2.5 (g) 10.284.935.296.698.8210.40

18. 18 Case Study—Preliminary Results Analysis Stabilization effect vs. destabilization effect  safety Follower without FCW (normal follower) vs. follower with FCW (equipped follower). FCW Active Leader Normal Follower (NF) Equipped Follower (EF) Impact of stabilization and destabilization effects Increase of min TTC Parameters 0% (Base) 100% Penetration Rate of FCW # of conflicts 49461331362253391076 Conflict time%1.38%0.049%0.054%0.061%0.071%0.141% Stabilization effect stronger  decreased number of conflicts. Collective effect of stabilization and destabilization effect  reduced risk level of conflicts.

19. 19 Summary of Case Study Findings FCW benefits traffic mobility, safety, and efficiency. Two critical FCW penetration rates – 40% and 80%: ̶ 40% marks the turning point of mobility and emission curves. ̶ 80% marks the turning point of safety curve. Stabilization effect and destabilization effect of FCW: ̶ Stabilization effect more prominent than destabilization effect. ̶ Destabilization effect increases more than 1.75 times  no mobility and emission benefits. The safety benefit of FCW. ̶ Stabilization effect responsible for the reduction of the number of conflicts. ̶ The two effects jointly lower the severity of the occurred conflicts.

20. 20 Significance of Research Cause-and-effect mechanism between CV info and driver behaviors. Expanded modeling capability of state-of-the- art traffic flow models. Scenario-based virtual evaluation system. Assessment benchmark.

21. Acknowledgement Thanks for Supports of US Environmental Protection Agency/Ohio DOT/NSF (RET/REU Site) 21