Calibration and Validation of Microscopic Traffic Simulation Tools: Stockholm Case Study

Author(s):  
Tomer Toledo ◽  
Haris N. Koutsopoulos ◽  
Angus Davol ◽  
Moshe E. Ben-Akiva ◽  
Wilco Burghout ◽  
...  

The calibration and validation approach and results from a case study applying the microscopic traffic simulation tool MITSIMLab to a mixed urban-freeway network in the Brunnsviken area in the north of Stockholm, Sweden, under congested traffic conditions are described. Two important components of the simulator were calibrated: driving behavior models and travel behavior components, including origin–destination flows and the route choice model. In the absence of detailed data, only aggregate data (i.e., speed and flow measurements at sensor locations) were available for calibration. Aggregate calibration uses simulation output, which is a result of the interaction among all components of the simulator. Therefore, it is, in general, impossible to identify the effect of individual models on traffic flow when using aggregate data. The calibration approach used takes these interactions into account by iteratively calibrating the different components to minimize the deviation between observed and simulated measurements. The calibrated MITSIMLab model was validated by comparing observed and simulated measurements: traffic flows at sensor locations, point-to-point travel times, and queue lengths. A second set of measurements, taken a year after the ones used for calibration, was used at this stage. Results of the validation are presented. Practical difficulties and limitations that may arise with application of the calibration and validation approach are discussed.

Author(s):  
Heng Wei

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.


Author(s):  
Iisakki Kosonen ◽  

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.


Author(s):  
Tomer Toledo ◽  
Moshe E. Ben-Akiva ◽  
Deepak Darda ◽  
Mithilesh Jha ◽  
Haris N. Koutsopoulos

Author(s):  
Byungkyu (Brian) Park ◽  
Hongtu (Maggie) Qi

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.


Author(s):  
Zenghao Hou ◽  
Joyoung Lee

This paper proposes an innovative multi-thread stochastic optimization approach for the calibration of microscopic traffic simulation models. Combining Quasi-Monte Carlo (QMC) sampling and the Particle Swarm Optimization (PSO) algorithm, the proposed approach, namely the Quasi-Monte Carlo Particle Swarm (QPS) calibration method, is designed to boost the searching process without prejudice to the calibration accuracy. Given the search space constructed by the combinations of simulation parameters, the QMC sampling technique filters the searching space, followed by the multi-thread optimization through the PSO algorithm. A systematic framework for the implementation of the QPS QMC-initialized PSO method is developed and applied for a case study dealing with a large-scale simulation model covering a 6-mile stretch of Interstate Highway 66 (I-66) in Fairfax, Virginia. The case study results prove that the proposed QPS method outperforms other methods utilizing Genetic Algorithm and Latin Hypercube Sampling in achieving faster convergence to obtain an optimal calibration parameter set.


2018 ◽  
Vol 130 ◽  
pp. 844-849 ◽  
Author(s):  
Bekir Bartin ◽  
Kaan Ozbay ◽  
Jingqin Gao ◽  
Abdullah Kurkcu

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