Considering evacuation from cramped interiors with aisle seats, the initial seating positions of pedestrians represent a non-negligible source of variance in total evacuation time. By means of Pathfinder simulations, this uncertainty is quantitatively investigated for a train and lecture hall geometry with homogeneous and heterogeneous groups of agents. Furthermore, an energy-inspired description of the occupation configuration is introduced that enables a proper numerical representation of the configuration space of the initial seating positions.

Knowing the capacity and the speed-density relation for different facility parts is a key component for a sustainable station design. But as the behaviour of pedestrians on railway platforms is complex, detailed data from railway stations is needed, which only became available recently. By using real-life tracking data from different railway stations in Switzerland, a method to estimate the capacity for different facility elements was derived and applied to the data aiming at verifying or improving the existing design values. As expressed in the fundamental diagram, the speed-density relation was useful in addressing these issues. The speed-density-flow relation was plotted at different levels ranging from a whole platform section to areas covering only a few square meters. Waiting pedestrians were treated separately to reflect their specific behaviour. Afterwards, the Kladek-curve proposed by Weidmann was fitted to the data using different parameter values. Although the data is biased towards low densities, the capacity of each area can be estimated using the fitted curve. The results show that the flow-density curves show a good fit to the mean of each density bin. Nevertheless, a large scatter of the individual data points exists. The derived maximum flow is considerably different depending on the measurement location and the area size and is generally lower than average values from literature. It is expected that the complex behaviour of pedestrians in railway stations and platforms has a significant influence on these differences, which therefore needs to be considered for station design.

Predicting pedestrian dynamics is a complex task as pedestrian speed is influenced by various external factors. This study investigates neighboring factors that can be used to improve pedestrian walking speed prediction accuracy in both low- and high-density scenarios. Different factors are proposed, including Mean Distance, Time-to-Collision, and Front Effect, and data for each factor is extracted from different public datasets. The collected data at time t is used to train a neural network to predict the pedestrian walking speed at time t + ∆t. Predictions are evaluated using the Mean Absolute Error. Our results demonstrate that incorporating the Front Effect significantly improves prediction accuracy in both low- and high-density scenarios, whereas the Mean Distance factor only proves effective in high-density cases. On the other hand, no significant improvement is observed when considering the Time-to-Collision factor. These preliminary findings can be utilized to enhance the accuracy of pedestrian dynamics predictions by incorporating these factors as additional features within the model.

Due to a variety of intricate topographic structures, many subway stations are constructed deeply. Traditional stairs and escalators in deeply buried subway stations can hardly meet passengers' demand for highly efficient travel. Buried depth of the subway station, passenger flow intensity, percentage of passengers choosing elevator, and elevator characteristics such as rated capacity and rated operating speed of the elevator are factors affecting the evacuation time of passengers. In order to study the impact of these factors on the evacuation efficiency under the daily commute of passengers, deeply buried subway evacuation model with an elevator exit was established by AnyLogic. Three kinds of simulation scenarios were analyzed. The results shows that average evacuation time is positively correlated with the buried depth of subway when the elevator is not set. When the passenger flow intensity is large, the higher the percentage of passengers choosing elevator, the longer average evacuation time of passengers. Compared to the simulation scenario with a rated capacity of 15 passengers and a rated operating speed of 1 m/s, average evacuation time can be reduced by up to 81.9% when the rated capacity and rated operating speed of the elevator are 40 passengers and 11 m/s respectively. The research can guide the subway planner reference on evacuation planning for the deeply buried subway station.

Mental simulation of people movement forms a core component of pedestrian/evacuation analysis and planning, albeit one that is rarely addressed. It can be defined as the process by which a practitioner develops a narrative of how people within a built environment may move to inform a decision-making process regarding architectural or procedural design. There are a range of contexts in which a practitioner may use mental simulation. These can include assisting with identifying problems associated with architecture/procedural design and comparing with pedestrian/evacuation modelling results to suggest if these are in line with expectations.. Little research has been conducted exploring the process by which practitioners mentally simulate people movement, its efficacy, and what factors influence this process. This paper is intended to provide insights regarding this process. Results from an online survey are presented where expert practitioners where asked questions about a range of hypothetical evacuation scenarios with increasing complexity regarding what they expected the total evacuation time to be and how many people they expected to use each exit if they were simulated in a pedestrian/evacuation model. Participants were also asked how confident they were with their results. The survey data was then compared with results with evacuation model results of the same scenarios. Key findings from the study highlight that as the floor plan layout and behavioural complexity increase in a scenario, the greater the level of variation in responses between practitioners along with decreasing levels of accuracy and levels of confidence in their perceived ability for performing mental simulation of people movement. Floor plan and exit symmetry appear to influence a practitioner’s ability to mentally simulate people movement in terms of estimating evacuation times and exit usage when layouts/exit locations change.

This research centers on analyzing frontal encounters between dyads (two-person groups) and individuals, aiming to measure each participant's role in avoiding collisions based on their deviation from their intended path. To achieve this, we establish the intended trajectory of each party by taking into account their walking direction leading up to the encounter. The largest discrepancy between this intended path and the observed path can be interpreted as the pedestrian's maximum lateral deviation.We show a noteworthy discrepancy in deviation between group members and individuals in face-to-face encounters. Furthermore, we conduct an in-depth analysis of how the intensity of interaction among group members impacts collision avoidance dynamics. Notably, the contrast in deviation between individuals and group members is most pronounced when the level of interaction within the group is high. Ultimately, our findings consistently indicate that higher levels of interaction lead to more substantial deviations in the trajectories of encountered individuals and underscore the significant role of social dynamics in influencing pedestrian behavior during frontal encounters.

We recently proposed the "Congestion Number" (CN) as a metricto evaluate the state of a pedestrian crowd. Such metric, whose definition is based on the gradient of the rotor of the crowd velocity field, appears to provide additional information with respect to traditional metrics based on pedestrian density and flow. We also published two works on the dynamics of orthogonally crossing pedestrian flows under different density regimes. In the first manuscript we analysed experimental data by using traditionalobservables such as density, velocity and relative position between pedestrians, along with less explored ones such as body orientation. In the second one we proposed a hierarchy of simulation models to reproduce the cross-flow dynamics, and used the aforementioned observables to compare such models. Based on theoretical considerations and analysis of real world data, we believe the crossing flow setting to be a good arena to test the CN metric, and in this work we perform a CN analysis on the empirical and simulation data. Results show that simulation models, which reproduced almost perfectly the density time dependence of the pedestrian crowd, fail to reproduce the CN one. Actually, models "outperform" the pedestrian crowd when analysed using CN. These preliminary results suggest that the CN metric may provide useful information not only in crowd assessment but also in model evaluation.

We apply the concept of time-to-collision (TTC) to the modeling of pedestrian dynamics. The TTC combines the spatial distances with the velocities to quantify the 'distance' to a collision. Therefore, it is a promising candidate for modeling the interactions between pedestrians. Empirical studies also indicate that the interaction between pedestrians can be described by the TTC: While the pair distribution of the distances, i.e. the probability of two pedestrians to have a certain spatial distance, was found to strongly depend on the relative velocity, the TTC accurately parametrizes its pair distribution. However, there are still few pedestrian models that use the TTC. After giving a general definition of the TTC, we present the widely used approximations for its calculation, especially in a one-dimensional setting. Combined with a desired time-gap, these give rise to different models, namely an Optimal-Velocity model and a new Time-to-Collision model. The TTC model exhibits, however, generic inconsistencies which are related to the estimates we use to approximate the speed of the predecessor. The estimates have a large impact on the dynamics and must therefore be interpreted as reflecting the pedestrians behavior, i.e. as anticipation strategies. We propose new estimates for the predecessor's speed. These give rise to a rich family of models based on the TTC which are analyzed by means of linear stability analysis and simulations.

Pedestrian flows and densities have increased in recent years within transport-related public facilities such as train stations, as well as in private buildings such as shopping centers, event halls or convention centers. Increasing flows and high densities often raise comfort, safety, operational and delay issues; and therefore, require pedestrian flow optimization, intervention or even revised regulation. Recent technological advances enhanced pedestrian sensing; however, they disregard adaptive data capture, processing, and strategic communication within reasonable time, or real-time, such as tactic occupancy or density alarms trigger rules. Content of this research is twofold. First, new data capturing and processing advances of recent technological developments are combined in an integral software and hardware-based framework. Second, applied methods highlight projects and experiences on both pedestrian research and on existing and operating pedestrian facilities. Based on the described, two-sided approach, proposed framework is able to fulfil high safety and comfort standards of facilities such as train stations, retail facilities or event halls. In this research, past semi-automatic video analysis processing of pedestrian behavioral studies is replaced with combined sensor and data processing system within proposed framework. In train stations of major operators, real-time pedestrian observation increases safety levels on station platforms. Tactic algorithms and alarm trigger schemes enable on-time surveillance, e.g. at overcrowded floor levels in shopping centers for escalator or door closure. Sensor data is used to train models for underpass pedestrian flow regarding path choice and fundamental diagram. In retail, queue length, trajectory analysis and floor occupancy are determined for economic, comfort as well as safety evaluation. Using trajectory classification, movement and dwell time is analyzed for staff and visitors separately (see Figure 1).

This study quantitatively described how the desired speed, which may reflect the emergency level, and the angle of bend affect the pedestrian flow by comparing fundamental diagrams derived from trajectory data collected through laboratory experiments. Results showed that the slow running (≈ 2.8 m/s speed) can increase the maximum flow through a bend by around 60 % compared to normal walking (≈ 1.4 m/s speed) regardless of the turning angle. Further, it was found that the turning angle of the bend has a stronger negative impact on the moving speed of crowds under running conditions. Compared to the turning angle, congestion level seemed to have a minor impact on the average moving speed through the bends. On the other hand, for 90° and 180° bends, the variations of the speed were observed to decrease with the increase of density which indicated that although congestion level deteriorated the flow conditions at bends, it homogenized the collective moving speed of pedestrians.