Microscopic Simulation Model Calibration and Validation: Case Study of VISSIM Simulation Model for a Coordinated Actuated Signal System

2003 ◽  
Vol 1856 (1) ◽  
pp. 185-192 ◽  
Author(s):  
Byungkyu (Brian) Park ◽  
J. D. Schneeberger
Keyword(s):  
Data Collection ◽  
Simulation Model ◽  
Model Calibration ◽  
Simulation Models ◽  
Test Bed ◽  
Microscopic Simulation ◽  
Calibration And Validation ◽  
Parameter Values ◽  
Model Calibration And Validation

Microscopic simulation models have been widely used in both transportation operations and management analyses because simulation is safer, less expensive, and faster than field implementation and testing. While these simulation models can be advantageous to engineers, the models must be calibrated and validated before they can be used to provide meaningful results. However, the transportation profession has not established any formal or consistent guidelines for the development and application of these models. In practice, simulation model–based analyses have often been conducted under default parameter values or bestguessed values. This is mainly due to either difficulties in field data collection or lack of a readily available procedure for simulation model calibration and validation. A procedure was proposed for microscopic simulation model calibration and validation and an example case study is presented with real-world traffic data from Route 50 on Lee Jackson Highway in Fairfax, Virginia. The proposed procedure consisted of nine steps: ( a) measure of effectiveness selection, ( b) data collection, ( c) calibration parameter identification, ( d) experimental design, ( e) run simulation, ( f) surface function development, ( g) candidate parameter set generations, ( h) evaluation, and ( i) validation through new data collection. The case study indicates that the proposed procedure appears to be properly calibrating and validating the VISSIM simulation model for the test-bed network.

Development and Evaluation of a Procedure for the Calibration of Simulation Models

2005 ◽  
Vol 1934 (1) ◽  
pp. 208-217 ◽  
Author(s):  
Byungkyu (Brian) Park ◽  
Hongtu (Maggie) Qi
Keyword(s):  
Simulation Model ◽  
Model Calibration ◽  
Field Data ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Transportation Engineering ◽  
Microscopic Simulation ◽  
Microscopic Traffic Simulation ◽  
Calibration And Validation ◽  
Simulation Results

Microscopic traffic simulation models have been playing an important role in the evaluation of transportation engineering and planning practices for the past few decades, particularly in cases in which field implementation is difficult or expensive to conduct. To achieve high fidelity and credibility for a traffic simulation model, model calibration and validation are of utmost importance. Most calibration efforts reported in the literature have focused on the informal practice, and they have seldom proposed a systematic procedure or guideline for the calibration and validation of simulation models. This paper proposes a procedure for microscopic simulation model calibration. The validity of the proposed procedure was demonstrated by use of a case study of an actuated signalized intersection by using a widely used microscopic traffic simulation model, Verkehr in Staedten Simulation (VISSIM). The simulation results were compared with multiple days of field data to determine the performance of the calibrated model. It was found that the calibrated parameters obtained by the proposed procedure generated performance measures that were representative of the field conditions, while the simulation results obtained with the default and best-guess parameters were significantly different from the field data.

Core Models for State-of-the-Art Microscopic Traffic Simulation and Kay Elements for Applications

2011 ◽  
pp. 92-124 ◽  
Author(s):  
Heng Wei
Keyword(s):  
Model Calibration ◽  
Traffic Simulation ◽  
State Of The Art ◽  
Choice Model ◽  
Microscopic Simulation ◽  
Calibration And Validation ◽  
Building Models ◽  
Car Following Model ◽  
Vehicle Trajectory ◽  
Model Calibration And Validation

This chapter summarizes fundamental models for microscopic simulation (such as vehicle generation model and car-following model) and other critical models (such as lane-choice model, lane-changing model, and route-choice model). Most of the critical models introduced in this chapter reflect the latest research results by the author. The primary purpose of this chapter is to provide fundamentals for better understanding of the travel behaviors that are modeled for traffic simulations. To facilitate the applications of traffic simulation models, several key elements for applying state-of-the-art computer traffic simulation tools are summarized. They include the procedure for building models, model calibration and validation. Further more, techniques for collecting vehicle trajectory data, critical elements used for model calibration and validation, are also introduced.

Using Automatic Calibration with Microscopic Traffic Simulation

2008 ◽  
Vol 12 (2) ◽  
pp. 106-110
Author(s):  
Iisakki Kosonen ◽  
Keyword(s):  
Error Function ◽  
Simulation Models ◽  
Automatic Calibration ◽  
Microscopic Simulation ◽  
Detailed Model ◽  
Microscopic Traffic Simulation ◽  
Calibration And Validation ◽  
University Of Technology ◽  
Detailed Simulation

The microscopic simulation is getting increasingly common in traffic planning and research because of the detailed analysis it can provide. The drawback of this development is that the calibration and validation of such a detailed simulation model can be very tedious. This paper summarizes the research on automatic calibration of a high-fidelity micro-simulation (HUTSIM) at the Helsinki University of Technology (TKK). In this research we used ramp operation as the case study. The automatic calibration of a detailed model requires a systematic approach. A key issue is the error-function, which provides a numeric value to the distance between simulated and measured results. Here we define the distance as combination of three distributions namely the speed distribution, gap distribution and lane distribution. We developed an automated environment that handles all the necessary operations. The system organizes the files, executes the simulations, evaluates the error and generates new parameter combinations. For searching of the parameter space we used a genetic algorithm (GA). The overall results of the research were good demonstrating the potential of using automatic processes in both calibration and validation of simulation models.

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