Calibration of microscopic traffic-flow models using multiple data sources

Abstract

Parameter identification of microscopic driving models is a difficult task. This is caused by the fact that parameters-such as reaction time, sensitivity to stimuli, etc.-are generally not directly observable from common traffic data, but also due to the lack of reliable statistical estimation techniques. This contribution puts forward a new approach to identifying parameters of car-following models.One of the main contributions of this article is that the proposed approach allows for joint estimation of parameters using different data sources, including prior information on parameter values (or the valid range of values). This is achieved by generalizing the maximum-likelihood estimation approach proposed by the authors in previous work.The approach allows for statistical analysis of the parameter estimates, including the standard error of the parameter estimates and the correlation of the estimates. Using the likelihood-ratio test, models of different complexity (defined by the number of model parameters) can be cross-compared. A nice property of this test is that it takes into account the number of parameters of a model as well as the performance. To illustrate the workings, the approach is applied to two car-following models using vehicle trajectories of a Dutch freeway collected from a helicopter, in combination with data collected with a driving simulator.

A comparative study of bridge traffic load effect using micro-simulation and Eurocode load models

“…Treiber et al (2000a) present the Intelligent Driver Model (IDM), a car-following model that gives a good match, at macroscopic level, with real congested traffic (Treiber et al 2000b, Helbing et al 2009). It has a modest number of physically-meaningful parameters and has been calibrated with real trajectory data (Kesting & Treiber 2008, Hoogendoorn & Hoogendoorn 2010, Chen et al 2010. Results from the calibrated Intelligent Driver Model are comparable with more complex models (Brockfeld et al 2004, Punzo & Simonelli 2005.…”

Load effect of multi-lane traffic simulations on long-span bridges

“…It has also been calibrated with real trajectory data (Kesting & Treiber 2008, Hoogendoorn & Hoogendoorn 2010, Chen et al 2010) and compared to other calibrated car-following models, returning results comparable to more complex models (Brockfeld et al 2004, Punzo & Simonelli 2005.…”

Calibrating Car-Following Model Considering Measurement Errors

“…Therefore, the models have become widely preferred for applications in traffic and safety engineering, for example, for capacity analysis, traffic simulation, network analysis, and Advance Vehicle Control [1][2][3]. Especially, as microscopic traffic data (such as trajectory data and floating car data) have become more available, the problem of calibrating car-following traffic flow models with real microscopic data has raised some interest in the literatures [1][2][3][4][5][6][7][8][9].…”

“…First, the car-following models have been generally calibrated by using macroscopic traffic data and validated by comparing outputs aggregated at a macroscopic level [2,7,[10][11][12]. Using macroscopic data to calibrate a microscopic model would ignore differences of vehicle behavior (such as speed choice and headway choice) within a traffic stream.…”