A social force evacuation model driven by video data

Liu, Baoxi; Liu, Hong; Zhang, Hao; Qin, Xin

doi:10.1016/j.simpat.2018.02.007

Cited by 93 publications

(38 citation statements)

References 30 publications

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“…For the former, [7][8][9][10][11] are of particular interest in our context. The latter was carried out in [12] together with the Social Force Model. In our application, we want to predict macroscopic traffic quantities, such as density and flow, through simulations with an explanatory microscopic model, the Optimal Steps Model [13,14].…”

Section: Introductionmentioning

confidence: 99%

Can we learn where people go?

Gödel¹,

Köster²,

Lehmberg³

et al. 2020

Coll Dyn

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In most agent-based simulators, pedestrians navigate from origins to destinations. Consequently, destinations are essential input parameters to the simulation. While many other relevant parameters as positions, speeds and densities can be obtained from sensors, like cameras, destinations cannot be observed directly. Our research question is: Can we obtain this information from video data using machine learning methods? We use density heatmaps, which indicate the pedestrian density within a given camera cutout, as input to predict the destination distributions. For our proof of concept, we train a Random Forest predictor on an exemplary data set generated with the Vadere microscopic simulator. The scenario is a crossroad where pedestrians can head left, straight or right. In addition, we gain first insights on suitable placement of the camera. The results motivate an in-depth analysis of the methodology.

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Section: Introductionmentioning

confidence: 99%

Can we learn where people go?

Gödel¹,

Köster²,

Lehmberg³

et al. 2020

Coll Dyn

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show abstract

“…The information generated form the truncated weighted shortest path algorithm for agent n is then communicated to a lower layer of modelling, the step-taking module. This layer is basically a calibrated but standard social-force model [65]. This information determines the desired direction and therefore the desired force of agent n which in combination with the social and wall forces determines the next step of the pedestrians at each time step of the simulation.…”

Section: Rationality Analysis At the Macro (System) Levelmentioning

confidence: 99%

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

Haghani

Sarvi

2019

Journal of Advanced Transportation

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Background. Evacuation behaviour of human crowds is often characterised by the notion of ‘irrational behaviour’. While the term has been frequently used in the literature, clear definitions and methods for measuring rationality do not exist. Objective. Here, we suggest that rationality, in this context, can alternatively and more effectively be formulated as a question of ‘optimal behaviour’. Decision optimality can potentially be measured and quantified. The main challenges, however, include (i) distinctly identifying the level at which we measure optimality, and (ii) identifying proper reference points at each level. Methods. We differentiate between optimality at the individual (i.e., micro) and the system (i.e., macro/aggregate) levels and illustrate how certain reference points can be established at each level. We suggest that, at the micro level, optimality of individual decisions can be quantified by comparing the outcome of each individual’s decision to those of their ‘nearly equal peers’. At the macro level, optimality can be measured by simulating the system using parametric numerical models and measuring the system performance while altering the behavioural parameters compared to their empirical estimates. Results. Having applied these methods, we observed that variation in micro level decision optimality rises rapidly as the space becomes more heavily crowded. As crowdedness increases in the environment, the difference between ‘good’ and ‘bad’ decisions becomes more distinct; and suboptimal decisions become more frequent. In other words, optimality at individual level seems to be moderated by the level of crowdedness. At the macro level, numerical simulations showed that, for certain exit attributes (like exit congestion), extreme marginal valuations (or preferences) were optimal, whereas for certain other attributes (like exit visibility), intermediate levels of valuation were closer to the optimal. In most cases, the natural observed (or estimated) tendency of evacuees (at the aggregate level) was not quite at the optimum level, meaning that the system could improve by modifying individuals’ marginal valuations of exit attributes. Applications and Recommendations. These results highlight the importance of guiding evacuation decisions particularly in heavily crowded spaces. They also theoretically illustrate the potential benefit of influencing/modifying people’s evacuation strategies, so they make decisions that are collectively more efficient. A crucial step to this end, however, is to identify what optimum strategy is and under what circumstances people are likelier to make suboptimal decisions.

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“…The SFM [13]- [15] is a spatiotemporal continuous model based on Newton's second law, which fully considers the individual willingness, the interactions among pedestrians and the interactions between the pedestrian and surrounding obstacles. The SFM has been constantly improved to meet new requirements, including introducing the real video data and the mental state [16]. The cellular automata model [17]- [20] belongs to a spatial discrete model, and pedestrians in the cellular world decide to choose which cellular in the next step according to their maximum motion probability.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-Theory-Based Social Force Model for Studying the Impact of Alighting Area Width on Alighting and Boarding Behaviors

2020

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Alighting and boarding efficiency (A&Be) of passengers plays an important role in the formulation of subway timetable. This paper studies the impact of alighting area width on A&Be based on the improved social force model. This improved model adopts the fuzzy logic theory considering factors of train dwell time and passengers ahead to determine the desired speeds of passengers instead of setting fixed values. The t-test method verifies the validity of the model. The passenger movement dynamics characterized by the alighted or boarded passenger quantities, the desired speeds, and the actual speeds over time is then analyzed. The simulation results indicate when people keep to the civilized rules of alighting first, A&Be will be improved significantly with increasing the alighting area width. Otherwise, the arbitrary increase of alighting area width does not necessarily improve A&Be. The alighting area width during the actual design should be determined by the specific passenger volume and passenger civility. Whether the countermeasure of adding the fences to separate the alighting passengers from the boarding passengers is effective to improve the travel efficiency is finally explored through analyzing the simulation results. Our findings could provide theoretical supports for decision-making of waiting area division on the subway platform and passenger flow management in reality. INDEX TERMS Social force model, alighting area width, alighting and boarding efficiency, fuzzy logic theory.

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A social force evacuation model driven by video data

Cited by 93 publications

References 30 publications

Can we learn where people go?

Can we learn where people go?

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

A Fuzzy-Theory-Based Social Force Model for Studying the Impact of Alighting Area Width on Alighting and Boarding Behaviors

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