TRANSIMS Microsimulator Application for Improving Fuel Consumption at Urban Corridor

TRANSIMS Microsimulator Application for Improving Fuel

Consumption at Urban Corridor

Jaesup LeeUniversity 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

Sponsor

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

Motivations

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

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

TRANSIMS Microsimulator

• Explicitly models individual vehicles • Updates vehicle status (e.g., speed and

acceleration) every 1-second

Calibration/Validation ProcedureSee: http://faculty.virginia.edu/brianpark/SimCalVal/

Case Study Network

Charlottesville, VA

Calibration ParametersParameters Unit

PLAN_FOLLOWING_DISTANCE mLOOK_AHEAD_DISTANCE m

LOOK_AHEAD_LANE_FACTOR -LOOK_AHEAD_TIME_FACTOR -

MAXIMUM_SWAPPING_SPEED m/secMAXIMUM_SPEED_DIFFERENCE m/sec

DRIVER_REACTION_TIME secPERMISSION_PROBABILITY %

SLOW_DOWN_PROBABILITY %SLOW_DOWN_PERCENTAGE %MINIMUM_WATING_TIME minMAXIMUM_WATING_TIME min

MAX_ARRIVAL_TIME_VARIANCE minMAX_DEPARTURE_TIME_VARIANCE min

Calibration Validation Procedure

• Experimental Design Approach – Exhaustive search infeasible– Latin Hypercube Sampling method

• Developed 200 sets and made 5 replications for each set

Default vs. Calibrated

Default Parameters

Calibration using anExperimental Design

Validation

Calibrated parameters were validated with untried field data

Achieving Sustainable Transportation: Saving Fuel Consumption…

Why VT-Micro Model?

• Current EPA Mobile uses average link speed…

VT-Micro Model

Fuel/Emission Estimation Procedure

Traffic Signal Timing Optimization

• SYNCHRO for minimizing delay and stops

• Proposed approachesi. Minimizing fuel consumptionii. Minimizing total vehicle-hours-

traveled

Genetic Algorithm Convergence

Performance Evaluation

MOE

Mobility EnergyEfficiency Emission

VHT (hr)

Number of Trips (veh) Fuel

(liter) HC (g) CO (g) NOX (g) CO2 (kg)started complet

edaverage 332.5 6062 5701 814.4 884.6 8064.5 898.7 1876.6STDEV 9.6 38.9 38.6 19.35 17.5 116.3 22.7 44.9

min 318.2 5999 5627 785.53 857.2 7827.2 862.3 1809.7max 370.3 6222 5783 894 956.6 8506.4 996.8 2061.6

SYNCHRO Optimized – Base Case

Note: Results are based on 50 TRANSIMS Microsimulator replications.

Performance Evaluation

Proposed Approach – VHT Minimization

Note: Results are based on 50 TRANSIMS Microsimulator replications.

MOE

Mobility EnergyEfficiency Emission

VHT (hr)Number of Trips (veh) Fuel

(liter) HC (g) CO (g) NOX (g) CO2 (kg)started completed

average 247.2 6222 5980 663.2 759.2 7540.6 738.5 1525.8

STDEV 2.6 0.14 17.7 6.4 6.5 57.8 7.6 14.9

min 242.6 6221 5935 651.6 748.1 7439.2 725.0 1498.9

max 254.6 6222 6013 680.6 776.2 7675.8 760.2 1566.2

Performance Evaluation

Proposed Approach – Fuel Consumption Minimization

Note: Results are based on 50 TRANSIMS Microsimulator replications.

MOE

Mobility EnergyEfficiency Emission

VHT (hr)Number of Trips (veh) Fuel

(liter) HC (g) CO (g) NOX (g) CO2 (kg)started Completed

average 243.76 6220.5 5962.6 648.9 749.0 7434.8 713.6 1492.1

STDEV 3.6 3.1 21.0 7.7 7.57 63.1 8.8 17.9

min 237.3 6207 5908 635.6 735.5 7308.4 698.7 1461.6

max 258.0 6222 6011 677.5 776.1 7616.8 744.5 1558.3

Comparison of VHT

Comparison of Fuel Consumption

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

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