Fluid-dynamical and microscopic description of traffic flow: a data-driven comparison

Abstract

Much work has been done to compare traffic-flow models with reality; so far, this has been done separately for microscopic, as well as for fluid-dynamical, models of traffic flow. This paper compares directly the performance of both types of models to real data. The results indicate that microscopic models, on average, seem to have a tiny advantage over fluid-dynamical models; however, one may admit that for most applications, the differences between the two are small.Furthermore, the relaxation times of the fluid-dynamical models turns out to be fairly small, of the order of 2 s, and are comparable with the results for the microscopic models. This indicates that the second-order terms are weak; however, the calibration results indicate that the speed equation is, in fact, important and improves the calibration results of the models.

Traffic jams: dynamics and control

“…Despite its simplicity, model (3.12) can reproduce qualitatively almost all kinds of traffic behaviour and also transitions between different behaviours. A shortcoming of the model is that some parameters (like the desired time gap) do not appear explicitly, while others (like the relaxation time T ) may be smaller than expected when fitting the model to data (Wagner 2010).…”

“…Data-fitting for traffic systems is far from trivial, caused partly by the possibly large number of model parameters and partly by the fact that the studied high degree-of-freedom system changes in time; it is not that easy to fit an elephant in motion (see the quote at the beginning of this paper). In Wagner (2010), macroscopic data are used to estimate model parameters for both macroscopic and microscopic models, while, in Hoogendoorn & Hoogendoorn (2010), microscopic models are fitted to microscopic data. In Laval & Leclercq (2010), microscopic data are used to build microscopic models, and it has been proved essential to add dynamics to kinematic models to be able to reproduce traffic jam formation as observed in data.…”

Microscopic driving theory with oscillatory congested states: Model and empirical verification

“…The models include the NaSch model (Nagel and Schreckenberg, 1992), Newell's model (Newell, 2002), the OV model (Bando et al, 1995), the Cell Transmission model (Daganzo, 1994), Gipps's model (Gipps, 1981), the SK model (Krauss et al, 1997), the IDM (Treiber et al, 2000), and the macroscopic model proposed by Aw and Rascle (2000). The best model tested by Wagner (2010) is the SK model, which was also tested by Brockfeld et al (2005). The calibration and validation errors (U values) presented by Brockfeld et al (2005) are in the range of 0.14-0.16 and 0.14-0.23, respectively.…”

IA2P: Intrusion-Tolerant Malicious Data Injection Attack Analysis and Processing in Traffic Flow Data Collection Based on VANETs

“…With the rapid development of information technology, more and more traffic flow data are collected by installing sensors (usually double induction loop detectors) along the road that measure flux and speed at a certain location. The nonlinearity of traffic flow has been proven, and more nonlinear theory and model, such as the fluiddynamical model [22], are improved to describe the traffic flow.…”