Congested traffic states in empirical observations and microscopic simulations

Abstract

We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber, Phys. Rev. Lett. 82, 4360 (1999)]. We simulate these situations with a novel continuous microscopic single-lane model, the "intelligent driver model" (IDM), using the empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has practically the same effect as an on-ramp.

Delays, inaccuracies and anticipation in microscopic traffic models

“…For matters of illustration, we will apply the HDM to the intelligent-driver model [26], which has a built-in anticipative and smooth braking strategy, and which reaches good scores in a first independent attempt to benchmark micromodels based on real traffic data [29].…”

“…The class of suitable basic models is characterized by continuous acceleration functions depending on the velocity, the gap, and the relative velocity with respect to the preceding car and includes, for example, the optimal-velocity model (OVM) [14], the Gipps model [23], the velocity-difference model [25], the intelligent-driver model (IDM) [26], and the boundedly rational driver model [27,28].…”

An algorithm for combining autonomous vehicles and controlled events in driving simulator experiments

“…The test leader observed that the autonomous vehicles never, or seldom, were able to reach their desired speed. The reason for this was found to be the design of the IDM car-following model (Treiber et al, 2000), more precisely, the function for calculating acceleration due to interactions with a preceding vehicle. The observed deficiency leads to a lower average speed than what is expected for a specific distribution of desired speeds.…”

“…However, the car-following and lane changing models used in Janson Olstam et al (2008) have been replaced by versions of the HDM/IDM car-following model (Treiber et al, 2000(Treiber et al, , 2006 and a combination of the ARCHISIM (El Hadouaj and Espié, 2002) and the MOBIL (Kesting et al, 2007) lane-changing models. The lateral position update algorithm has been replaced by the steering model presented in Salvucci et al (2001).…”

ATLAS: A language to specify traffic models using Cell-DEVS

“…Some of them include queuing networks [6,36], Markov models, Cellular Automata [12,38,30,33], DEVS [8,22], software agents [2], object-oriented programming [34] and learning automata [25]. Several other approaches have also been used, from Game Theory [7], Petri Nets [37], up to fluid or electrical flow models equivalent to traffic.…”