NetLogo Reproduction of Walk This Way

Several months ago, we posted that we had just had a paper accepted entitled "Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis. The original model was created in MASON but know it has been reproduced in NetLogo. The purpose of this exercise was to see if the model could be reproduced from the description in said paper along with the availability of the source code and data. Specifically whether there is sufficient information in the paper to reproduce the model and the results. It was an interesting exercise translating methods from MASON into NetLogo procedures (also a lot less lines of code). 

A: NetLogo Graphical User Interface (GUI), B: Original MASON GUI.

In order to really ensure this was a good reproduction it was also necessary to provide the data we compared the results to from the original model (something which is not very common in ABM publications). This way we could could see if the re-implemented model really did match the results of the original model. 

To the left, you can see the graphical user interfaces for the NetLogo model and the original model implemented in MASON.

More information about the re-implementation and the code can be found over at Yang's Blog: http://bit.ly/geospatialcss

Full Reference to the Original Paper:

Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015),  Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis,  ISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf) 

Walk this Way

We recently had published in ISPRS International Journal of Geo-Information a paper entitled "Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis". In the paper we explore how new data can help inform our agent-based models. Specifically, pedestrian modeling which has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. Below is the abstract for the paper:

Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene- and Activity-Aware Agent-Based Model (SA ² -ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using real-world datasets. We demonstrate that the use of real scene information allows us to better inform our model and enhance its predictive capabilities.

Keywords: pedestrian modeling; pedestrian tracking; activity monitoring; spatiotemporal trajectories; agent-based modeling

As with many of our models, the source code of the model can be downloaded from here. To give a sense of the model, the movie below shows how agents traverse the scene.

Full Reference: 

Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015),  Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis,  ISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf)

Tracking Pedestrians

Tracking peoples movement is an extremely time consuming task to do by hand however such analysis informs our ability to realistically model peoples moment in pedestrian models.

Researchers at the Computer Vision Laboratory, ETH have been working on multi-object tracking which combines object detection and space-time trajectory estimation. The ability to automate such a process obviously has benefits for understanding pedestrian movement in cities

To see a video of the work click here.

References

B. Leibe, K. Schindler, N. Cornelis, L. Van Gool. (2008). Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles. In IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 10, pp. 1683-1698.

A. Ess, B. Leibe, K. Schindler, L. Van Gool. (2008). A Mobile Vision System for Robust Multi-Person Tracking. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, USA, June 2008.

Digital Urban pedestrian tracking tags