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TRANSIMS Microsimulator Application for Improving Fuel Consumption at Urban Corridor Jaesup Lee University of Virginia & Virginia DOT Byungkyu “Brian”

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Presentation on theme: "TRANSIMS Microsimulator Application for Improving Fuel Consumption at Urban Corridor Jaesup Lee University of Virginia & Virginia DOT Byungkyu “Brian”"— Presentation transcript:

1 TRANSIMS Microsimulator Application for Improving Fuel Consumption at Urban Corridor Jaesup Lee University of Virginia & Virginia DOT Byungkyu “Brian” Park & Jaeyoung Kwak University of Virginia Presented at the 12th TRB National Transportation Planning Applications Conference, May 17-21, 2009, Houston, Texas

2 Sponsor Federal Highway Administration – Broad Agency Announcement – Project Manager: Brian Gardner, FHWA

3 Motivations

4 TRANSIMS Extensively developed in 1990s and demonstrated in Dallas/Ft. Worth and Portland case studies Integrated activity based modeling and microscopic traffic simulation (cellular automata) Open source community

5 Proposed Research Application of TRANSIMS for Sustainable Transportation: mainly focused on Microsimulator Microscopic simulator calibration/validation Integration of the Microsimulator, a fuel consumption and emission model, and an optimizer Demonstrate feasibility via a case study

6 TRANSIMS Microsimulator Explicitly models individual vehicles Updates vehicle status (e.g., speed and acceleration) every 1-second

7 Calibration/Validation Procedure See: http://faculty.virginia.edu/brianpark/SimCalVal/

8 Case Study Network Charlottesville, VA

9 Calibration Parameters ParametersUnit PLAN_FOLLOWING_DISTANCEm LOOK_AHEAD_DISTANCEm LOOK_AHEAD_LANE_FACTOR- LOOK_AHEAD_TIME_FACTOR- MAXIMUM_SWAPPING_SPEEDm/sec MAXIMUM_SPEED_DIFFERENCEm/sec DRIVER_REACTION_TIMEsec PERMISSION_PROBABILITY% SLOW_DOWN_PROBABILITY% SLOW_DOWN_PERCENTAGE% MINIMUM_WATING_TIMEmin MAXIMUM_WATING_TIMEmin MAX_ARRIVAL_TIME_VARIANCEmin MAX_DEPARTURE_TIME_VARIANCEmin

10 Calibration Validation Procedure Experimental Design Approach – Exhaustive search infeasible – Latin Hypercube Sampling method Developed 200 sets and made 5 replications for each set

11 Default vs. Calibrated Default Parameters Calibration using an Experimental Design

12 Calibration parameterunitDefaultCalibrated PLAN_FOLLOWING_DISTANCEm5251308 LOOK_AHEAD_DISTANCEm2601415 LOOK_AHEAD_LANE_FACTOR-411.76 LOOK_AHEAD_TIME_FACTOR-14.32 MAXIMUM_SWAPPING_SPEEDm/sec37.59.65 MAXIMUM_SPEED_DIFFERENCEm/sec7.52.39 DRIVER_REACTION_TIMEsec11.99 PERMISSION_PROBABILITY%5083.77 SLOW_DOWN_PROBABILITY%045.83 SLOW_DOWN_PERCENTAGE%030.71 MINIMUM_WAITING_TIMEmin370.71 MAXIMUM_WAITING_TIMEmin60140.23 MAX_ARRIVAL_TIME_VARIANCEmin60162.03 MAX_DEPARTURE_TIME_VARIANCEmin6045.89 Posted limit 45 mph (20.11 m/sec)m/sec20.1120.53 Posted limit 40 mph (17.88 m/sec)m/sec17.8817.13 Posted limit 30 mph (13.41 m/sec)m/sec13.4112.83 Posted limit 15 mph (6.70 m/sec)m/sec6.78.88 Note: Colored Bold parameters indicate statistically significant factors.

13 Validation Calibrated parameters were validated with untried field data

14 Achieving Sustainable Transportation: Saving Fuel Consumption…

15 Why VT-Micro Model? Current EPA Mobile uses average link speed…

16 VT-Micro Model

17 Fuel/Emission Estimation Procedure Input Data Network Geometry Traffic Signal Timing Vehicle Information Traveler Activity Information TRANSIMS Traffic Simulation ConvertTrips Router PlanPrep Microsimulator Vehicle Snapshot Vehicle ID Time Position Speed Acceleration VT-micro Model Fuel Consumption HC NO X CO CO 2

18 Traffic Signal Timing Optimization SYNCHRO for minimizing delay and stops Proposed approaches i.Minimizing fuel consumption ii.Minimizing total vehicle-hours- traveled

19 Genetic Algorithm Convergence

20 Performance Evaluation MOE Mobility Energy Efficiency Emission VHT (hr) Number of Trips (veh) Fuel (liter) HC (g)CO (g)NOX (g) CO2 (kg) started complet ed average 332.560625701814.4884.68064.5898.71876.6 STDEV 9.638.938.619.3517.5116.322.744.9 min 318.259995627785.53857.27827.2862.31809.7 max 370.362225783894956.68506.4996.82061.6 SYNCHRO Optimized – Base Case Note: Results are based on 50 TRANSIMS Microsimulator replications.

21 Performance Evaluation Proposed Approach – VHT Minimization Note: Results are based on 50 TRANSIMS Microsimulator replications. MOE Mobility Energy Efficiency Emission VHT (hr) Number of Trips (veh) Fuel (liter) HC (g)CO (g)NOX (g)CO2 (kg) startedcompleted average247.262225980663.2759.27540.6738.51525.8 STDEV2.60.1417.76.46.557.87.614.9 min242.662215935651.6748.17439.2725.01498.9 max254.662226013680.6776.27675.8760.21566.2

22 Performance Evaluation Proposed Approach – Fuel Consumption Minimization Note: Results are based on 50 TRANSIMS Microsimulator replications. MOE Mobility Energy Efficiency Emission VHT (hr) Number of Trips (veh) Fuel (liter) HC (g)CO (g)NOX (g)CO2 (kg) startedCompleted average 243.766220.55962.6648.9749.07434.8713.61492.1 STDEV 3.63.121.07.77.5763.18.817.9 min 237.362075908635.6735.57308.4698.71461.6 max 258.062226011677.5776.17616.8744.51558.3

23 Comparison of VHT

24 Comparison of Fuel Consumption

25 Concluding Remarks Calibration/Validation is necessary for TRANSIMS Microsimulator to properly reflect field condition Successfully integrated VT-Micro emission estimation module, an GA-based optimizer, and Microsimulator

26 Concluding Remarks (cont’d) The integrated approach improved fuel consumption and emission over state-of-the- practice tool (i.e., SYNCHRO) More efficient computation is required for a large scale optimization

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