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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.
The problem of calibration of microscopic simulation models with aggregate data has received significant attention in recent years. But day-to-day variability in inputs such as travel demand has not been considered. In this thesis, a general formulation has been proposed for the problem in the presence of multiple days of data. The formulation considers the day-to-day variability in all the inputs to the simulation model. It has then been formulated using Generalized least squares (GLS) approach. The solution methodology for this problem has been proposed and the feasibility of this methodology has been shown with the help of two case studies. One of them is with an experimental network and the other is with network from Southampton, UK. The results indicate that estimation of day-to-day OD flows is feasible. They also reinforce the importance of having good apriori information on the OD flows and locating the sensors so as to obtain maximum information.
Well-calibrated traffic simulation model predictions can be highly valid if various conditions arising due to time-of-day, work zones, weather, etc. are appropriately accounted for during calibration. Calibration of traffic simulation models for various conditions requires larger datasets to capture the stochasticity. In this study we use datasets spanning large time periods to, especially, incorporate variability in traffic flow and speed. However, large datasets pose computational challenges. With the increase in number of stochastic factors, the numerical methods suffer from curse of dimensionality. We propose a novel methodology to address the computational complexity in simulation model calibration under highly stochastic traffic conditions. This methodology is based on sparse grid stochastic collocation, which treats each stochastic factor as a different dimension and uses a limited number of points where simulation is performed. A computationally-efficient interpolant is constructed to generate the full distribution of the simulated output. We use real-world examples to calibrate for different times of day and conditions and show that proposed methodology is more efficient than traditional Monte Carlo-type sampling. We validate the model using a hold-out dataset and also show the drawback of using limited data for macroscopic simulation model calibration. Modelers could often face situations with limited data in calibrating for a particular condition, often when using traffic sensor data. We augment the current data with other sources when sensor data is missing. For calibrating microscopic traffic simulation models needing customized models augmenting the default modeling, require detailed site-specific data. In such cases same generic calibration methodology may not be applicable and specialized formulations are required. We propose the use of a simulation-based optimization (SBO) framework for calibration of toll plaza models that economizes on data requirements. The novelty of the SBO framework is that parameters corresponding to unavailable data can be used as calibration parameters. Using case studies the benefits of the SBO framework are demonstrated. Furthermore, we combine the sampling and interpolation using stochastic collocation with the SBO framework. Using this hybrid framework, we perform calibration to obtain distribution of output from the toll plaza model that closely follows the observed measures at the toll plaza.
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 increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.
This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.
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.