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 propos...

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

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 calibrate...

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

In the application of microscopic simulation models, the importance of model calibration and validation cannot be overemphasized. A recent study proposed a systematic approach for conducting a simulation model calibration and validation procedure on the basis of experimental design and optimization and applied it to an isolated intersection with a VISSIM simulation model. The present study further evaluates the previously developed simulation model calibration and validation procedure by applying it to an urban arterial network consisting of 12 coordinated actuated signalized intersections. Both VISSIM and CORSIM simulation models were used. Travel time was used for the calibration measure, and maximum queue length was used for the validation measure. Study results showed that calibrated and validated simulation models were able to represent field conditions adequately, whereas default parameter-based models could not. As such, the previously developed simulation model calibration and validation procedure...

Application of Microscopic Simulation Model Calibration and Validation Procedure: Case Study of Coordinated Actuated Signal System

Stochastic Optimization for Sustainable Traffic Signal Control

In an effort to speed the development of new products and processes, many companies are turning to computer simulations to avoid the time and expense of building prototypes. These computer simulations are often complex, taking hours to complete one run. If there are many variables affecting the results of the simulation, then it makes sense to design an experiment to gain the most information possible from a limited number of computer simulation runs. The researcher can use the results of these runs to build a surrogate model of the computer simulation model. The absence of noise is the key difference between computer simulation experiments and experiments in the real world. Since there is no variability in the results of computer experiments, optimal designs, which are based on reducing the variance of some statistic, have questionable utility. Replication, usually a "good thing", is clearly undesirable in computer experiments. Thus, a new approach to experimentation is necessary.

Design and analysis for the Gaussian Process model

The process of validation is crucial for the use of computer simulation models in transportation policy, planning, and operations. This article lays out obstacles and issues involved in performing a validation. Described is a general process that emphasizes five essential ingredients for validation: context, data, uncertainty, feedback, and prediction. A test bed is used to generate specific and general questions as well as to give concrete form to answers and to methods used in providing them. The traffic simulation model CORSIM serves as the test bed; it is applied to assess signal timing plans on a street network of Chicago. The validation process applied in the test bed demonstrates how well CORSIM can reproduce field conditions, identifies flaws in the model, and shows how well CORSIM predicts performance under new (untried) signal conditions. The article finds that CORSIM, though imperfect, is effective with some restrictions in evaluating signal plans on urban networks.

L R Rilett

Papers

Statistically-based validation of computer simulation models in traffic operations and management