Can we learn where people go?

Gödel, Marion; Köster, Gerta; Lehmberg, Daniel; Gruber, Manfred; Kneidl, Angelika; Sesser, Florian

doi:10.17815/cd.2020.43

Cited by 5 publications

(6 citation statements)

References 14 publications

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“…We expect an accuracy of roughly 80%, similar to our observations in [2] where we predicted the target distributions. The results in Table 3 show an accuracy of about 80% of the setup with a standard deviation of about 15%.…”

Section: Retracing Using Simulated Datasupporting

confidence: 87%

“…Analogue to [2], the density heatmaps are calculated from the pedestrian trajectories with a Gaussian density function [5].…”

Section: Calculation Density Heatmapsmentioning

confidence: 99%

“…We decided to use random forest as machine learning model as in [2]. It is an easy and robust algorithm that needs only a few parameters and often provides good results.…”

Section: Random Forestmentioning

confidence: 99%

“…In previous work, we were able to demonstrate that it is possible to prediction direction distributions at crossing based on simulated data with an accuracy about 80% as a proof-of-concept [2]. Now, we want to test the methodology on data of pedestrians as e.g., surveillance data, experimental data, or similar.…”

Section: Introductionmentioning

confidence: 99%

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Can we learn where people come from? Retracing of origins in merging situations

Gödel,

Spataro,

Köster

2020

Preprint

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One crucial information for a pedestrian crowd simulation is the number of agents moving from an origin to a certain target. While this setup has a large impact on the simulation, it is in most setups challenging to find the number of agents that should be spawned at a source in the simulation. Often, number are chosen based on surveys and experience of modelers and event organizers. These approaches are important and useful but reach their limits when we want to perform real-time predictions. In this case, a static information about the inflow is not sufficient. Instead, we need a dynamic information that can be retrieved each time the prediction is started. Nowadays, sensor data such as video footage or GPS tracks of a crowd are often available. If we can estimate the number of pedestrians who stem from a certain origin from this sensor data, we can dynamically initialize the simulation. In this study, we use density heatmaps that can be derived from sensor data as input for a random forest regressor to predict the origin distributions. We study three different datasets: A simulated dataset, experimental data, and a hybrid approach with both experimental and simulated data. In the hybrid setup, the model is trained with simulated data and then tested on experimental data. The results demonstrate that the random forest model is able to predict the origin distribution based on a single density heatmap for all three configurations. This is especially promising for applying the approach on real data since there is often only a limited amount of data available.

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Section: Retracing Using Simulated Datasupporting

confidence: 87%

“…Analogue to [2], the density heatmaps are calculated from the pedestrian trajectories with a Gaussian density function [5].…”

Section: Calculation Density Heatmapsmentioning

confidence: 99%

“…We decided to use random forest as machine learning model as in [2]. It is an easy and robust algorithm that needs only a few parameters and often provides good results.…”

Section: Random Forestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can we learn where people come from? Retracing of origins in merging situations

Gödel,

Spataro,

Köster

2020

Preprint

Self Cite

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“…Previously, we investigated how to predict the distribution of a pedestrian flow across three different destinations [24]. The crowd simulation tool Vadere [25] was used to simulate the surrogate input data from which we obtained the heatmaps.…”

Section: Research Questionmentioning

confidence: 99%

Toward learning dynamic origin-destination matrices from crowd density heatmaps

et al. 2022

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Knowing the origins and destinations of pedestrians’ paths is key to the initialization of crowd simulations. Unfortunately, they are difficult to measure in the real world. This is one major challenge for live predictions during events such as festivals, soccer games, protest marches, and many others. Sensor data can be used to feed real-world observations into simulations in real-time. As input data for this study, we use density heatmaps generated from real-world trajectory data obtained from stereo sensors. Density information is compact, of constant size, and in general easier to obtain than e.g., individual trajectories. Therefore, the information limitation improves the applicability to other scenarios. We include the absolute pedestrian trip counts from origins to destinations during a brief time interval in an OD matrix, including unknown destinations due to sensor errors. Our goal is to estimate these OD matrices from a series of density heatmaps for the same interval. For this, we compute the ground truth OD matrices and density heatmaps using real-world trajectory data from a train station. We employ linear regression as a statistical learning method for estimation. We observe that the linear share of the relationship between density and OD matrix is estimated successfully. Nevertheless, a portion of the data remains that cannot be explained. We attempt to overcome this difficulty with random forest as a nonlinear model. The results indicate that both a linear and a nonlinear model can estimate some features of the OD matrices. However, there is no clear winner in terms of the chosen metric, the R 2 score. Overall, our findings are a strong indicator that OD matrices can indeed be estimated from density heatmaps extracted automatically from sensors.

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Decentralized Pedestrian Density Maps based on Sidelink Communication

Schuhbäck

Wischhof

2021

2021 IEEE International Conference on Communications Workshops (ICC Workshops)

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Can we learn where people go?

Cited by 5 publications

References 14 publications

Can we learn where people come from? Retracing of origins in merging situations

Can we learn where people come from? Retracing of origins in merging situations

Toward learning dynamic origin-destination matrices from crowd density heatmaps

Decentralized Pedestrian Density Maps based on Sidelink Communication

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