Abstract
Calibration plays a fundamental role in successful applications of traffic simulation and Intelligent Transportation Systems. In this research, the calibration of car–following models is seen as a dynamic problem, which is solved at each individual time–step. The optimization of model parameters is fulfilled using the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. The output of the optimization is a distribution of parameter values, capturing a wide range of various traffic conditions. The methodology is demonstrated via a case study, where the proposed framework is implemented for the dynamic calibration of the car–following model used in the TransModeler traffic simulation model and Gipps′ model. This method results to model parameter distributions, which are superior to simply using point parameter values, as they are more realistic, capturing the heterogeneity of driver behavior. Flexibility is thus introduced into the calibration process and restrictions generated by conventional calibration methods are relaxed.
Highlights
Over the past few decades Intelligent Transportation Systems (ITS) have matured and nowadays are widely applied to many different operational scenarios
The methodology is demonstrated via a case study, where the proposed framework is implemented for the dynamic calibration of the car–following model used in the TransModeler traffic simulation model and Gipps′ model
Their performance is largely independent of network’s initial condition, data that could be incorporated in a model through different parameter values or small methodology changes
Summary
Over the past few decades Intelligent Transportation Systems (ITS) have matured and nowadays are widely applied to many different operational scenarios. If the speed of the subject vehicle Vj[t] is lower than the speed of the front vehicle Vj-1[t] the acceleration rate will be positive (i.e. subject will accelerate) Otherwise, it will be negative (i.e. TransModeler’s users’ manual (Caliper, 2012) provides some initial values for the model parameters alpha±, beta±, gamma±, which are obviously not able to represent all traffic. 3.1 Overview Car–following models include several parameters that need to be calibrated These parameters are arguably constantly changing, guidelines with various approaches, such as top-down approach (Dowling et al, 2004) and genetic algorithms (Cheu et al, 1998; Lee et al, 2001; Kim and Rilett, 2004) should be followed (Antoniou et al, 2014a). Their mathematical representation is the minimization (or maximization) of some scalar-valued objective function with respect to a vector of adjustable parameters (Spall, 1998)
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