arXiv subjects
Publications and source records attributed to Dirk Helbing.
From single vehicle data a number of new empirical results about the temporal evolution, correlation, and density-dependence of macroscopic traffic quantities have been determined. These have relevant implications for traffic modeling and allow to test existing traffic models.
On the basis of assumptions about the behavior of driver-vehicle units concerning acceleration, deceleration, overtaking, and lane-changing maneuvers, a gas-kinetic traffic model for uni-directional multi-lane freeways is constructed. Queuing effects are explicitly taken into account in an overall manner. The resulting model is a generalization of Paveri-Fontana's Boltzmann-like traffic model and allows the derivation of macroscopic traffic equations for interacting lanes, including velocity equations. The related effective macroscopic traffic model for the total freeway cross-section is also derived. It provides corrections with respect to previous traffic models, but agrees with them in special cases.
Macroscopic traffic models have recently been severely criticized to base on lax analogies only and to have a number of deficiencies. Therefore, this paper shows how to construct a logically consistent fluid-dynamic traffic model from basic laws for the acceleration and interaction of vehicles. These considerations lead to the gas-kinetic traffic equation of Paveri-Fontana. Its stationary and spatially homogeneous solution implies equilibrium relations for the `fundamental diagram', the variance-density relation, and other quantities which are partly difficult to determine empirically. Paveri-Fontana's traffic equation allows the derivation of macroscopic moment equations which build a system of non-closed equations. This system can be closed by the well proved method of Chapman and Enskog which leads to Euler-like traffic equations in zeroth-order approximation and to Navier-Stokes-like traffic equations in first-order approximation. The latter are finally corrected for the finite space requirements of vehicles. It is shown that the resulting model is able to withstand the above mentioned criticism.
The game dynamical equations are derived from Boltzmann-like equations for individual pair interactions by assuming a certain kind of imitation behavior, the so-called proportional imitation rule. They can be extended to a stochastic formulation of evolutionary game theory which allows the derivation of approximate and corrected mean value and covariance equations. It is shown that, in the case of phase transitions (i.e. multi-modal probability distributions), the mean value equations do not agree with the game dynamical equations. Therefore, their exact meaning is carefully discussed. Finally, some generalizations of the behavioral model are presented, including effects of expectations, other kinds of interactions, several subpopulations, or memory effects.
It is shown, that the Boltzmann-like equations allow the formulation of a very general model for behavioral changes. This model takes into account spontaneous (or externally induced) behavioral changes and behavioral changes by pair interactions. As most important social pair interactions imitative and avoidance processes are distinguished. The resulting model turns out to include as special cases many theoretical concepts of the social sciences. A Kramers-Moyal expansion of the Boltzmann-like equations leads to the Boltzmann-Fokker-Planck equations, which allows the introduction of ``social forces'' and ``social fields''. A social field reflects the influence of the public opinion, social norms and trends on behaviorial changes. It is not only given by external factors (the environment) but also by the interactions of the individuals. Variations of the individual behavior are taken into account by diffusion coefficients.
This paper presents and derives the interrelations between survival analysis and master equation. Survival analysis deals with modeling the transitions between succeeding states of a system in terms of hazard rates. Questions related with this are the timing and sequencing of the states of a time series. The frequency and characteristics of time series can be investigated by Monte-Carlo simulations. If one is interested in cross-sectional data connected with the stochastic process under consideration, one needs to know the temporal evolution of the distribution of states. This can be obtained by simulation of the associated master equation. Some new formulas allow the determination of path-related (i.e. longitudinal) quantities like the occurence probability, the occurence time distribution, or the effective cumulative life-time distribution of a certain sequencing of states (path). These can be efficiently evaluated with a recently developed simulation tool (EPIS). The effective cumulative life-time distribution facilitates the formulation of a hidden state concept of behavioral changes which allows an interpretation of the respective time-dependence of hazard rates. Hidden states represent states which are either not phenomenological distinguishable from other states, not externally measurable, or simply not detected.
A stochastic model for behavioral changes by imitative pair interactions of individuals is developed. `Microscopic' assumptions on the specific form of the imitative processes lead to a stochastic version of the game dynamical equations. That means, the approximate mean value equations of these equations are the game dynamical equations of evolutionary game theory. The stochastic version of the game dynamical equations allows the derivation of covariance equations. These should always be solved along with the ordinary game dynamical equations. On the one hand, the average behavior is affected by the covariances so that the game dynamical equations must be corrected for increasing covariances. Otherwise they may become invalid in the course of time. On the other hand, the covariances are a measure for the reliability of game dynamical descriptions. An increase of the covariances beyond a critical value indicates a phase transition, i.e. a sudden change in the properties of the considered social system. The applicability and use of the introduced equations are illustrated by computational results for the social self-organization of behavioral conventions.
An analytical formula for the occurence probability of Markovian stochastic paths with repeatedly visited and/or equal departure rates is derived. This formula is essential for an efficient investigation of the trajectories belonging to random walk models and for a numerical evaluation of the `contracted path integral solution' of the discrete master equation [Phys. Lett. A 195, 128 (1994)].
In the last decade, stochastic models have shown to be very useful for quantitative modelling of social processes. Here, a configurational master equation for the description of behavioral changes by pair interactions of individuals is developed. Three kinds of social pair interactions are distinguished: Avoidance processes, compromising processes, and imitative processes. Computational results are presented for a special case of imitative processes: the competition of two equivalent strategies. They show a phase transition that describes the selforganization of a behavioral convention. This phase transition is further analyzed by examining the equations for the most probable behavioral distribution, which are Boltzmann-like equations. Special cases of Boltzmann-like equations do not obey the H-theorem and have oscillatory or even chaotic solutions. A suitable Taylor approximation leads to the socalled game dynamical equations (also known as selection-mutation equations in the theory of evolution).
Several types of stochastic equations are important in thermodynamics, chemistry, evolutionary biology, population dynamics and quantitative social science. For systems with pair interactions four different types of equations are derived, starting from a master equation for the state space: First, general mean value and (co)variance equations. Second, Boltzmann-like equations. Third, a master equation for the configuration space allowing transition rates which depend on the occupation numbers of the states. Fourth, a Fokker-Planck equation and a ``Boltzmann-Fokker-Planck equation''. The interrelations of these equations and the conditions for their validity are worked out clearly. A procedure for a selfconsistent solution of the nonlinear equations is proposed. Generalizations to interactions between an arbitrary number of systems are discussed.
It is suggested that the motion of pedestrians can be described as if they would be subject to `social forces'. These `forces' are not directly exerted by the pedestrians' personal environment, but they are a measure for the internal motivations of the individuals to perform certain actions (movements). The corresponding force concept is discussed in more detail and can be also applied to the description of other behaviors. In the presented model of pedestrian behavior several force terms are essential: First, a term describing the acceleration towards the desired velocity of motion. Second, terms reflecting that a pedestrian keeps a certain distance to other pedestrians and borders. Third, a term modeling attractive effects. The resulting equations of motion are nonlinearly coupled Langevin equations. Computer simulations of crowds of interacting pedestrians show that the social force model is capable of describing the self-organization of several observed collective effects of pedestrian behavior very realistically.
The movement of pedestrians is supposed to show certain regularities which can be best described by an ``algorithm'' for the individual behavior and is easily simulated on computers. This behavior is assumed to be determined by an intended velocity, by several attractive and repulsive effects and by fluctuations. The movement of pedestrians is dependent on decisions, which have the purpose of optimizing their behavior and can be explicitly modelled. Some interesting applications of the model to real situations are given, especially to formation of groups, behavior in queues, avoidance of collisions and selection processes between behavioral alternatives.
A kind of fluid dynamic description for the collective movement of pedestrians is developed on the basis of a Boltzmann-like gaskinetic model. The differences between these pedestrian specific equations and those for ordinary fluids are worked out, for example concerning the mechanism of relaxation to equilibrium, the role of ``pressure'', the special influence of internal friction and the origin of ``temperature''. Some interesting results are derived that can be compared to real situations, for example the development of walking lanes and of pedestrian jams, the propagation of waves, and the behavior on a dance floor. Possible applications of the model to town- and traffic-planning are outlined.
Related to an idea of Lewin, a mathematical model for behavioral changes under the influence of a social field is developed. The social field reflects public opinion, social norms and trends. It is not only given by external factors (the environment) but also by the interactions of individuals. Two important kinds of interaction processes are distinguished: Imitative and avoidance processes. Variations of individual behavior are taken into account by ``diffusion coefficients''.
A new representation of the exact time dependent solution of the discrete master equation is derived. This representation can be considered as contraction of the path integral solution of Haken. It allows the calculation of the probability distribution of the occurence time for each path and is suitable as basis of new computational solution methods.
The behavior of pedestrians shows certain regularities, which can be described by quantitative (partly stochastic) models. The models are based on the behavior of individual pedestrians, which depends on the pedestrian intentions and on the aspects of movement. The pedestrian intentions concerning a sequence of destinations are influenced by the demand for certain kinds of commodities, by the location of stores selling these, and by the expenditures to get the required commodities. The actual pedestrian movement starts and ends at special city entry points like parking lots. It is guided by the pedestrian intentions, but is subject to deceleration processes and avoidance maneuvers due to obstacles. As a consequence, the pedestrians have to speed up to reach the next destination well-timed. In addition, the pedestrian behavior is influenced by unexpected attractions (e.g. street artists). The model for the behavior of individual pedestrians is an ideal starting point for computer simulations of pedestrian crowds. Such simulations take into account the limited capacity of pedestrian ways and places, and allow to determine an optimal design of pedestrian areas and an optimal arrangement of store locations. Therefore, they can be applied for town- and traffic-planning. The model for the behavior of individual pedestrians also allows the derivation of mathematical equations for pedestrian crowds and for pedestrian groups. Pedestrian crowds can be described by a stochastic formulation, by a gaskinetic formulation or by a fluiddynamic formulation. The gaskinetic formulation (mezoscopic level) can be derived from the stochastic formulation (microscopic level), and the fluiddynamic formulation (macroscopic level) from the gaskinetic formulation.
Many human social phenomena, such as cooperation, the growth of settlements, traffic dynamics and pedestrian movement, appear to be accessible to mathematical descriptions that invoke self-organization. Here we develop a model of pedestrian motion to explore the evolution of trails in urban green spaces such as parks. Our aim is to address such questions as what the topological structures of these trail systems are, and whether optimal path systems can be predicted for urban planning. We use an `active walker' model that takes into account pedestrian motion and orientation and the concomitant feedbacks with the surrounding environment. Such models have previously been applied to the study of complex structure formation in physical chemical and biological systems. We find that our model is able to reporduce many of the observed large-scale spatial features of trail systems.
Two complementary mathematical models for attitude formation are considered: Starting from the model of Weidlich and Haag (1983), which assumes indirect interactions that are mediated by a mean field, a new model is proposed, which is characterized by direct pair interactions. Three types of pair interactions leading to attitude changes can be found: First, changes by some kind of avoidance behavior. Second, changes by a readiness for compromises. Third, changes by persuasion. Different types of behavior are distinguished by introducing several subpopulations. Representative solutions of the model are illustrated by computational results.