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The inputs to a microscopic traffic simulation model generally include quantitative, but immeasurable data describing driver behavior and vehicle performance characteristics. Engineers often use default values for parameters such as car-following sensitivity and gap acceptance because it can be difficult to obtain accurate estimates for these parameters.
Microscopic traffic simulations are tools for simulation of traffic in form of individual vehicles. Road types have various traffic characteristics and therefore different models for their traffic simulation and analysis. The Rural Road Traffic Simulator, RuTSim, is a model which was developed by the Swedish National Road and Transport Research Institute, VTI. RuTSim is a microscopic traffic simulator for rural roads. The 2+1 roads are the type of rural roads that allocate 2 lanes to one direction and one lane to the other, with this configuration for the lanes changing sides after a certain distance. In this research a calibration of the current version of RuTSim for 2+1 roads is presented. The project clarifies microscopic traffic simulation models, RuTSim and its specific settings for 2+1 roads, different approaches for calibrating models and finally the calibration process for 2+1 roads in the current version of the RuTSim model.The calibration process provides a better understanding of the specific effects (of the change) of calibration parameters and their role in returning better simulation outputs on traffic of 2+1 roads.
With the recent widespread deployment of intelligent transportation systems (ITS) in North America there is an abundance of data on traffic systems and thus an opportunity to use these data in the calibration of microscopic traffic simulation models. Even though ITS data have been utilized to some extent in the calibration of microscopic traffic simulation models, efforts have focused on improving the quality of the calibration based on aggregate form of ITS data rather than disaggregate data. In addition, researchers have focused on identifying the parameters associated with car-following and lane-changing behavior models and their impacts on overall calibration performance. Therefore, the estimation of the Origin-Destination (OD) matrix has been considered as a preliminary step rather than as a stage that can be included in the calibration process. This research develops a methodology to calibrate the OD matrix jointly with model behavior parameters using a bi-level calibration framework. The upper level seeks to identify the best model parameters using a genetic algorithm (GA). In this level, a statistically based calibration objective function is introduced to account for disaggregate form of ITS data in the calibration of microscopic traffic simulation models and, thus, accurately replicate dynamics of observed traffic conditions. Specifically, the Kolmogorov-Smirnov test is used to measure the "consistency" between the observed and simulated travel time distributions. The calibration of the OD matrix is performed in the lower level, where observed and simulated travel times are incorporated into the OD estimator for the calibration of the OD matrix. The interdependent relationship between travel time information and the OD matrix is formulated using an Extended Kalman filter (EKF) algorithm, which is selected to quantify the nonlinear dependence of the simulation results (travel time) on the OD matrix. The two test sites are from an urban arterial and a freeway in Houston, Texas. The VISSIM model was used to evaluate the proposed methodologies. It was found that the accuracy of the calibration can be improved by using disaggregated data and by considering both driver behavior parameters and demand.
Microscopic traffic simulation models have been widely accepted and applied in transportation engineering and planning practice for the past decades because simulation is cost-effective, safe, and fast. To achieve high fidelity and credibility for a traffic simulation model, calibration and validation are of utmost importance. Most calibration efforts reported in the literature have focused on the informal practice with a specific simulation model, but seldom did they propose a systematic procedure or guideline for simulation model calibration and validation. The purpose of this study was to develop and evaluate a procedure for microscopic simulation model calibration and validation. Three widely used microscopic traffic simulation models, VISSIM, PARAMICS, and CORSIM, were selected for model review and practice of model calibration and validation. The validity of the proposed procedure was evaluated and demonstrated via two case studies including an actuated signalized intersection and a 5-mile freeway segment with a lane-closure work zone. The simulation results were compared against the field data to determine the performance of the calibrated models. The proposed procedure yielded acceptable results for all applications, thus confirming that it was effective for the different networks and simulation models used in the study. Although the calibrated parameters generated the performance measures that were representative of the field conditions, the simulation results of the default parameters were significantly different from the field data.
Microscopic traffic simulation models are widely used in the transportation engineering field. Because of their cost-effectiveness, risk-free nature, and high-speed benefits, areas of use include transportation system design, traffic operations, and management alternatives evaluation. Despite their popularity and value, the credibility of simulation models falls short due to the use of default parameters without careful consideration. Improper model parameters prevent simulation models from accurately mimicking field conditions, limiting their ability to aid decision-making. Therefore, the user needs to pay more attention to fine-tune each model that they are using by calibrating the parameters inside the model. To summarize, we can define calibration as the adjustment of model parameters such that the model's output more closely represents field conditions. The intention of this handbook is to outline and explain the calibration and validation procedure for the parameters controlling human and vehicle characteristics for CORSIM and VISSIM.
The calibration of microscopic traffic simulation models is an area of intense study; however, additional research is needed into how to select which parameters to calibrate. In this project a procedure was designed to eliminate the parameters unnecessary for calibration and select those which should be examined for a VISSIM model. The proposed iterative procedure consists of four phases: initial parameter selection, measures of effectiveness selection, Monte Carlo experiment, and sensitivity analysis and parameter elimination. The goal of the procedure is to experimentally determine which parameters have an effect on the selected measures of effectiveness and which do not. This is accomplished through the use of randomly generated parameter sets and subsequent analysis of the generated results. The second phase of the project involves a case study on implementing the proposed procedure on an existing VISSIM model of Cobb Parkway in Atlanta, Georgia. Each phase of the procedure is described in detail and justifications for each parameter selection or elimination are explained. For the case study the model is considered under both full traffic volumes and a reduced volume set representative of uncongested conditions.
Traffic simulation models are increasingly being used in the transportation engineering profession—often, to solve complex problems that may not lend themselves to traditional analysis techniques. The application of traffic simulation models has traditionally been at the individual vehicle (microscopic) level or aggregate traffic stream (macroscopic) level. Recently, the Virginia Department of Transportation and other agencies have shown interest in mesoscopic traffic simulation models, which allow for a level of detail higher than macroscopic models and model execution times better than those of microscopic models. This study proposed a procedure for mesoscopic simulation model calibration and validation. The proposed procedure was demonstrated on a test bed along I-95 in the City of Richmond and Chesterfield County, Virginia, using Aimsun Next. Results of the case study indicated that the proposed procedure appears to be properly calibrating and validating the mesoscopic simulation model of the test bed.
In recent years, due to the advances in computation technology, microscopic vehicular traffic simulation has become one of the main tools used by transportation professionals to solve various design and analysis problems (e.g. safety performance evaluation of highways, impact of different design scenarios in units of safety and efficiency, etc.). The effective use of any of the existing simulation models is limited by the calibration of specific parameters that are based on observed real-life conditions. However, because the calibration of the simulation models is a time consuming and resource intensive process, one might resort to using the default parameter values. In this study, a soft computing-based methodology which synergistically combines Artificial Neural Networks and Genetic Algorithm (GA) applications, is proposed as an alternative for calibration methodology that considerably reduces the computation time in comparison to other commonly used methods. First, a Latin Hypercube Sampling method is used to select representative sets of values for VISSIM’s main calibration parameters. Second, the effect of each set of parameter values on the simulated traffic stream speed is recorded. Third, a neural-network is trained to determine the relationship between the input parameter values and the output vehicular speed. Finally, a genetic-algorithm uses the trained neural-network in its fitness function to determine the appropriate set of values for the calibration parameters. The proposed methodology allows for the calibration of microscopic traffic models with fewer computational resources than is commonly used. The feasibility of the method and its applicability to real-world traffic conditions is proved by employing the model using a real-world High Occupancy Vehicle (HOV) lane along a freeway segment. The results of proposed calibration method are compared with those from GA-only based calibration method.. It is concluded that the proposed method performs faster than the GA based calibration method while maintinaing a certain level of accuracy. To highlight the potential benefits of the proposed calibration method, a before-and-after calibration conflict analysis is presented. It is recommended to apply the proposed method to urban environments and to consider other performance measures (travel time, queue length, etc.) to investigate proposed method’s generality.