Quantitative analysis of pedestrian counterflow in a cellular automaton model

Abstract

Pedestrian dynamics exhibits various collective phenomena. Here we study bidirectional pedestrian flow in a floor field cellular automaton model. Under certain conditions, lane formation is observed. Although it has often been studied qualitatively, e.g. as a test for the realism of a model, there are almost no quantitative results, neither empirically nor theoretically. As basis for a quantitative analysis we introduce an order parameter which is adopted from the analysis of colloidal suspensions. This allows to determine a phase diagram for the system where four different states (free flow, disorder, lanes, gridlock) can be distinguished. Although the number of lanes formed is fluctuating, lanes are characterized by a typical density. It is found that the basic floor field model overestimates the tendency towards a gridlock compared to experimental bounds. Therefore an anticipation mechanism is introduced which reduces the jamming probability.

Reducing Bidirectional Pedestrian Conflict Based on Lane Formation Phenomenon in Subway Corridors

“…When the pedestrian volume of the bi-bidirectional flow increases, the lane formation has three patterns that are a free pattern, a lanes pattern and a jam pattern with the increase of volumes [43]. The average speed decreases with the increase of volume in the whole system [44]. The literature mentioned the above concentrated mainly on the macroscopic characteristics of the bidirectional flow but did not involve the microscope characteristics related to individual pedestrians.…”

Pedestrian simulations in hexagonal cell local field model

“…Many researchers work are based on CA models [16][17][18][19]. Schadschneider et al [20] and Nowak et al [21] calibrated the floor field model in single-file and counterflow Supposing that a pedestrian is in cell o and that the exit is in oa direction, he/she will have three moving direction: oa, ob and oc, besides, oa direction is his/her first choice. (c) If the exit is in other directions, for example: the angles to oa, ob are θ and φ respectively, the pedestrian will have φ/60°chance to select oa and θ/60°chance to select ob as forward direction.…”

Bidirectional non-Markovian exclusion processes

“…In case of slow position exchange interactions, the particle flux is determined by the exchange times of particles from different species similar to a defect in the unidirectional TASEP [20]. Bidirectional TASEP models have been modified by introducing a second dimension to describe pedestrian dynamics [21,22] or intracellular traffic on polar filaments by adding additional lanes [23,24,9]. Despite situations where symmetry is broken and the system organizes into lanes [22], particle interactions often lead to cluster formation and is therefore a limiting factor for transport [25].…”