1. 1 Evaluation of Traffic Delay Reduction from Automatic Workzone Information Systems Using Micro-simulation Lianyu Chu CCIT, UC Berkeley Henry X. Liu Utah State University Hee Kyung Kim UC Irvine Will Recker UC Irvine

2. 2 OUTLINE Introduction Methodology Model calibration Evaluation Conclusion

3. 3 Introduction Work zone – Noticeable source of accidents and congestion AWIS: – Automated Workzone Information Systems – Components: Sensors Portable CMS Central controller – Benefits: Provide traffic information Potentially –Improve safety –Enhance traffic system efficiency

4. 4 Introduction (cont.) AWIS systems in market – ADAPTIR by Scientex Corporation – CHIPS by ASTI – Smart Zone by ADDCO Traffic Group – TIPS by PDP Associates Evaluation studies – Most System functionalities Reliability – Few Effectiveness on safety, diversion

5. 5 Objective & Approach Effectiveness of AWIS in traffic delay reduction Methods – Field operational test Uncontrolled factors, e.g. incidents, variations of demands – Traffic analysis tools Freq, QuickZone, QUEWZ –Difficulty in representing complicated traffic congestion analytically – Proposed method: Microscopic simulation: Paramics –Model vehicles in fine details

6. 6 Study site and CHIPS system Site Location – City of Santa Clarita, 20 miles north of LA – On I-5: 4-lane freeway with the closure of one lane on the median side – Construction zone: 1.5 miles long – Parallel route: Old Road – Congestion: occurred in Holidays and Sundays CHIPS configuration – 3 traffic sensors – 5 message signs

7. 7 System Setup Scenario Queue DetectorCMS Combo Message RTMS-1RTMS-2RTMS-3PCMS-1PCMS-2PCMS-3PCMS-4 PCMS-5 SBS01FFFCMB01 SBS02TFFCMB02CMB03CMB05 SBS03TTFCMB06CMB07CMB03CMB10 SBS04TTTCMB06CMB07CMB08CMB09CMB11 T = Queue being detected, F = No queue being detected CMB06 : SOUTH 5/TRAFFIC/JAMMED, AUTOS/USE NEXT/EXIT CMB07 : JAMMED/TO MAGIC/MOUNTAIN, EXPECT/10 MIN/DELAY CMB08 : JAMMED/TO MAGIC/MOUNTAIN, EXPECT/15 MIN/DELAY CMB09 : JAMMED TO MAGIC MTN, AVOID DELAY USE NEXT EXIT CMB11: SOUTH 5 ALTERNAT ROUTE, AUTOS USE NEXT 2 EXITS

8. 8 Evaluation methodology - Before-after study:

9. 9 Building network Based on – aerial photos – geometry maps inputs: – roadway network, – traffic detection, – traffic control, – vehicle data, – driving behavior – route choice – traffic analysis zones

10. 10 Calibration Calibration: – Adjust model parameters to obtain a reasonable correspondence between the model and observed data – Time-consuming, tedious – Models need to be calibrated for the specific network and the intended applications Methods – Trail-and-error method – Gradient- based and GA Proposed 2-step method: – Calibrate driving behavior models – Simultaneous estimation of OD matrix and route choice

11. 11 Data collection Before: May 18 th, 2003 After: Sep 1 st, 2003 (Labor Day) Link flows: – 5 on-ramps and off-ramps – RTMS-1 and RTMS-3 – Loop detector station at Hasley Canyon Rd Probe data – Two routes: Mainline and the Old road – GPS-equipped vehicles

12. 12 Calibrate driving behavior models Calibrate capacities at major bottlenecks Three parameters: – Mean headway – Drivers’ reaction time – Headway factor for mainline links Trial-and-error method – Choose several parameter combinations – Check their performances - Mean headway = 0.9 - Drivers’ reaction time = 0.8

13. 13 Simultaneous estimation of OD and routing parameters Connected and affected each other Formulated as Solution algorithm: – two-stage heuristic search method – Assisted by Paramics OD estimator using Paramics modeling engine inputs s.t.

14. 14 two-stage heuristic search method Stage 1: Generate n sets of OD tables and routing parameters with Paramics OD estimator – (1) Choose n routing parameters θ 1 to θ n from multiple dimension parameter space. – (2) Let i = 1, set θ= θ i. – (3) Estimate OD table Гi based on traffic counts at measurement locations. The objective function is: – (4) i = i+1. If i < n, go to step 3; otherwise go to the next stage.

15. 15 two-stage heuristic search method Stage 2: Evaluate these OD tables and routing parameters with Paramics Modeler. – (1) Let i = 1 – (2) Run Paramics simulation with OD table Гi and θi. – (3) Evaluate the estimated OD and routing parameters: – (4) i = i+1. If i < n, go to step 2, otherwise go to Step 5. – (5) Compare all MAPE(i), the lowest one corresponds to i = μ. – Therefore, Гμ and θμ are the best calibrated OD table and routing parameter.

16. 16 Model calibration for step 2 Route choice model in simulation models – Dynamic feedback assignment – Parameters: feedback cycle, compliance rate Routing parameter θ : 1 parameter: compliance rate Inputs to OD estimator: – Reference OD table from planning model – 6 cordon flows – 5 measurement locations Simulation period: 3-5 pm – Warm-up: 3-4 pm

17. 17 Calibration results Stage 1: – routing parameter θ i : an estimated OD table Г i Stage 2:

18. 18 Calibrated “after” model Location/RouteObservedSimulated APE (%) Traffic count (veh/hour) main_Hasley389038670.59 main_Rye Canyon456845260.92 off_Hasley7648136.41 on_SR-1264804770.63 on_Magic Mountain7136745.47 Travel time (min) Mainline1313.10.48 Old Road1111.10.92 MAPE 2.20

19. 19 Evaluation Run two simulation models: – Demand: after OD table – Before: calibrated before network, 3% compliance rate – After: Calibrated after network, 18% compliance rate Number of simulation runs – Median run with respect to VHT

20. 20 Evaluation results

21. 21 Evaluation results (cont.)

22. 22 Conclusion a microscopic simulation method to evaluate traffic delay reduction from AWIS – Calibration of two simulation models two-step calibration – a simultaneous estimation of OD table and routing parameters A two-stage heuristic solution algorithm Evaluation: – AWIS can effectively reduce traffic delay Improve overall performance of the traffic system