36 research outputs found
Microscopic features of moving traffic jams
Empirical and numerical microscopic features of moving traffic jams are
presented. Based on a single vehicle data analysis, it is found that within
wide moving jams, i.e., between the upstream and downstream jam fronts there is
a complex microscopic spatiotemporal structure. This jam structure consists of
alternations of regions in which traffic flow is interrupted and flow states of
low speeds associated with "moving blanks" within the jam. Empirical features
of the moving blanks are found. Based on microscopic models in the context of
three-phase traffic theory, physical reasons for moving blanks emergence within
wide moving jams are disclosed. Structure of moving jam fronts is studied based
in microscopic traffic simulations. Non-linear effects associated with moving
jam propagation are numerically investigated and compared with empirical
results.Comment: 19 pages, 12 figure
Two-lane traffic rules for cellular automata: A systematic approach
Microscopic modeling of multi-lane traffic is usually done by applying
heuristic lane changing rules, and often with unsatisfying results. Recently, a
cellular automaton model for two-lane traffic was able to overcome some of
these problems and to produce a correct density inversion at densities somewhat
below the maximum flow density. In this paper, we summarize different
approaches to lane changing and their results, and propose a general scheme,
according to which realistic lane changing rules can be developed. We test this
scheme by applying it to several different lane changing rules, which, in spite
of their differences, generate similar and realistic results. We thus conclude
that, for producing realistic results, the logical structure of the lane
changing rules, as proposed here, is at least as important as the microscopic
details of the rules
Congested Traffic States in Empirical Observations and Microscopic Simulations
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. {\bf 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.Comment: Now published in Phys. Rev. E. Minor changes suggested by a referee
are incorporated; full bibliographic info added. For related work see
http://www.mtreiber.de/ and http://www.helbing.org
Traffic and Related Self-Driven Many-Particle Systems
Since the subject of traffic dynamics has captured the interest of
physicists, many astonishing effects have been revealed and explained. Some of
the questions now understood are the following: Why are vehicles sometimes
stopped by so-called ``phantom traffic jams'', although they all like to drive
fast? What are the mechanisms behind stop-and-go traffic? Why are there several
different kinds of congestion, and how are they related? Why do most traffic
jams occur considerably before the road capacity is reached? Can a temporary
reduction of the traffic volume cause a lasting traffic jam? Under which
conditions can speed limits speed up traffic? Why do pedestrians moving in
opposite directions normally organize in lanes, while similar systems are
``freezing by heating''? Why do self-organizing systems tend to reach an
optimal state? Why do panicking pedestrians produce dangerous deadlocks? All
these questions have been answered by applying and extending methods from
statistical physics and non-linear dynamics to self-driven many-particle
systems. This review article on traffic introduces (i) empirically data, facts,
and observations, (ii) the main approaches to pedestrian, highway, and city
traffic, (iii) microscopic (particle-based), mesoscopic (gas-kinetic), and
macroscopic (fluid-dynamic) models. Attention is also paid to the formulation
of a micro-macro link, to aspects of universality, and to other unifying
concepts like a general modelling framework for self-driven many-particle
systems, including spin systems. Subjects such as the optimization of traffic
flows and relations to biological or socio-economic systems such as bacterial
colonies, flocks of birds, panics, and stock market dynamics are discussed as
well.Comment: A shortened version of this article will appear in Reviews of Modern
Physics, an extended one as a book. The 63 figures were omitted because of
storage capacity. For related work see http://www.helbing.org
Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers with New Insights into a Controversial Hysteresis
Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models
Drivers' use of deceleration and acceleration information in car-following process
Understanding driver behavior is important for the development of many applications such as microscopic traffic simulation models and advanced driver assistance systems. The car-following process is an important phase of driving behavior and takes place when a driver follows a lead vehicle and tries to maintain distance and relative speed within an acceptable range. A key to improving knowledge of driver behavior during this process is determining the information perceived by drivers that could influence their decisions. It has been believed for some time that the main kinematic parameters that affect driver judgment in car following are the relative speed, the distance separation, and the absolute speed. The research described investigated whether drivers are also able to use information on the lead vehicle's deceleration or acceleration during the car-following process through experimental validation of current car-following hypotheses. For this research, an instrumented vehicle was used to collect a large database of car-following time sequences, the analysis of which showed strong evidence that drivers are able to perceive information such as the deceleration or acceleration of the vehicle being followed, although no empirical relationship was determined. An example demonstrating the importance of such perception shows that modeling a driver trying to avoid a collision with a lead vehicle would lose 20% of its fit accuracy if the lead-vehicle acceleration state were not considered