Kernel Smoothing Method Applicable to the Dynamic Calibration of Traffic Flow Models

Abstract

Real time traffic flow simulation models are used to provide traffic information for dynamic traffic management systems. Those simulation models are supplied by traffic data in order to estimate and predict traffic conditions in unobserved sections of a traffic network. In general, most of recent real time traffic simulators are based on the macroscopic model because the macroscopic model replicates the average traffic behavior in terms of observable variables such as (time–space) flow and speed at a relatively fast computational time. Like other simulation models, an important aspect of the real time macroscopic simulator is to calibrate the model parameters online. The most conventional way of the online calibration is to add a random walk to the parameters to constitute an augmentation of the traffic variables and the model parameters to be estimated. Actually, this method allows the parameters to vary at every time step and, therefore, describes the adaptation of the model to the prevailing traffic conditions. However, it has been reported that the use of the random walk results in a loss of information and an increase of the covariance of parameters, which consequently leads to posteriors that are far more diffuse than the theoretical posteriors for the true parameters. To this end, this article puts forward a Kernel density estimation technique in the calibration process to handle the covariance issue and to avoid the information loss. The Kernel density estimation technique is embedded in the particle filter algorithm, which is extended to the calibration problems. The proposed framework is investigated using real‐life data collected in a freeway in England.

Intelligent Road Sign Inventory (IRSI) with Image Recognition and Attribute Computation from Video Log

“…The KDE is proposed to serve this purpose. KDE is a nonparametric method (Duong and Hazelton, 2003) that has found many applications in transportation (Ngoduy, 2011). It can model the multimodel distributions without specifying the number of components (number of clusters).…”

Multianticipative Nonlocal Macroscopic Traffic Model

“…The data were used in our recent publication (Ngoduy, ; Ngoduy and Maher, ) for the calibration of the model of Treiber et al. ().…”

“…Traffic flow theory is used in a wide range of operational and planning applications, from real‐time information/control (Adeli and Samant, ; Adeli and Jiang, ; Wang and Papageorgiou, ; Wang et al., ; van Lint and Hoogendoorn, ; Ngoduy, ; Heilmann et al., ) to medium‐to‐long‐term forecasting (Szeto et al., ; Balijepalli et al., ). In principle, there are three types of traffic flow models in the state‐of‐the‐art of traffic flow theory: microscopic models, mesoscopic models, and macroscopic models.…”

“…E-mail: d.ngoduy@ leeds.ac.uk. 2006;van Lint and Hoogendoorn, 2009;Ngoduy, 2011;Heilmann et al, 2011) to medium-to-long-term forecasting (Szeto et al, 2011;Balijepalli et al, 2012). In principle, there are three types of traffic flow models in the state-of-the-art of traffic flow theory: microscopic models, mesoscopic models, and macroscopic models.…”

Probabilistic travel time progression and its application to automatic vehicle identification data

“…Other methods have been recently proposed to enable the on-line prediction of traffic state, including travel time, in urban networks using heterogeneous data (Nantes et al, 2015). Such methods rely on traffic flow models, as a means of predicting the next state of traffic, which are embedded in the Bayesian estimation fitlers such as Kalman filters or particle filters (Ngoduy, 2008, 2011, Wang and Papageorgiou, 2005, Wang et al, 2007.…”