Showing papers by "Andreas Schadschneider published in 2019"

A glossary for research on human crowd dynamics

Abstract: This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary.

Modeling the effect of visibility on upstairs crowd evacuation by a stochastic FFCA model with finer discretization

Abstract: In recent years, deep underground buildings appear more often than ever, and stairs are the main facilities for the upward evacuation of crowds from these buildings. As one of the most essential factors, the visibility might play a dominant role in the upward evacuation on stairs. To ensure the safety of the crowd in underground spaces, a finer discrete and stochastic floor field cellular automaton (FFCA) model integrating the visibility influence is established for the simulation of the crowd upward evacuation from a 21-storey staircase. By comparison, the simulation fits well with the observation and experiment, which implies that the model captures the main features of upward evacuations on stairs. Then, from the predicted results, it is found that the visibility reduction has a significant negative impact on the temporal–spatial distribution of pedestrians, especially on the inflow and outflow, while the entrance flow rate shows little impact as long as it is higher than 1.0 ped ⋅ m−1 ⋅ s−1.

Kardar-Parisi-Zhang universality of the Nagel-Schreckenberg model

Abstract: Dynamical universality classes are distinguished by their dynamical exponent z and unique scaling functions encoding space-time asymmetry for, e.g., slow-relaxation modes or the distribution of time-integrated currents. So far the universality class of the Nagel-Schreckenberg (NaSch) model, which is a paradigmatic model for traffic flow on highways, was not known. Only the special case v_{max}=1, where the model corresponds to the totally asymmetric simple exclusion process, is known to belong to the superdiffusive Kardar-Parisi-Zhang (KPZ) class with z=3/2. In this paper, we show that the NaSch model also belongs to the KPZ class for general maximum velocities v_{max}>1. Using nonlinear fluctuating hydrodynamics theory we calculate the nonuniversal coefficients, fixing the exact asymptotic solutions for the dynamical structure function and the distribution of time-integrated currents. The results of large-scale Monte Carlo simulations match the exact asymptotic KPZ solutions without any fitting parameter left. Additionally, we find that nonuniversal early-time effects or the choice of initial conditions might have a strong impact on the numerical determination of the dynamical exponent and therefore lead to inconclusive results. We also show that the universality class is not changed by extending the model to a two-lane NaSch model with lane-changing rules.

Mechanical Restriction vs. Human Overreaction: Modeling of Synchronized two-Lane Traffic

Abstract: This contribution presents a realistic cellular automaton model for multi-lane traffic, based on the model proposed by Lee et al. (2004). In contrast to current approaches, a limited deceleration capability, i.e., a mechanical restriction, has been assigned to the vehicles. Moreover, the velocity of the vehicles was determined on the basis of the local neighborhood, and the drivers were thus divided into optimistic or pessimistic drivers. The former were prone to underestimating their safety distance if their neighborhood admitted it, whereas the latter always kept a safe distance, thus over-reacting. This resulted in a convincing reproduction of the microscopic and macroscopic features of synchronized traffic. The anticipation of the leader’s velocity was thereby essential for the reproduction of synchronized traffic. Nevertheless, accidents occurred in the stationary state, and thus, the model approach required modification so as to be capable of simulating multi-lane traffic. The adapted model was enhanced by a realistic lane-change algorithm, and a multi-lane model, reproducing the empirical data even better than the single-lane approach, was formulated. In open systems with bottlenecks, such as an on-ramp or a speed-limit, the empirically observed complex structures of the synchronized traffic could be reproduced in great detail.

Artificial Neural Networks Predicting Pedestrian Dynamics in Complex Buildings

Abstract: The prediction of pedestrian movements in complex buildings is a difficult task. Recent experiments have shown that the behaviour of pedestrians tends to depend on the type of facility. For instance, flows at bottlenecks often exceed the maximal rates observed in straight corridors. This makes pedestrian behaviours geometry-dependent. Yet the types of geometries are various, and their systematic identification in complex buildings is not straightforward. Artificial neural networks are able to identify various types of patterns without supervision. They could be a suitable alternative for forecasts of pedestrian dynamics in complex architectures. In this paper, we test this assertion. We develop, train and test artificial neural networks for the prediction of pedestrian speeds in corridor and bottleneck experiments. The estimations are compared to those of an elementary speed-based model. The results show that neural networks distinguish the flow characteristics for the two different types of facilities and significantly improve the prediction of pedestrian speeds.

Stop-and-go waves induced by correlated noise in pedestrian models without inertia

Abstract: Stop-and-go waves are commonly observed in traffic and pedestrian flows. In most traffic models they occur through a phase transition after fine tuning of parameters when the model has unstable homogeneous solutions. Inertia effects are believed to play an important role in this mechanism. Here, we present a novel explanation for stop-and-go waves based on stochastic effects in the absence of inertia. The introduction of specific coloured noises in a stable microscopic first order model allows to describe realistic stop-and-go behaviour without requiring instabilities or phase transitions. We apply the approach to pedestrian single-file motion and compare simulation results to real pedestrian trajectories. Plausible values for the model parameters are discussed.