Data-Driven Modeling of Pedestrian Crowds

At the starting point of the work leading to this doctoral thesis, in January 2005, the work on pedestrians was almost exclusively oriented towards computer simulations and on evacuation experiments. Since then, there have been many studies on new methods for extracting empirical data of pedestrian movements (mainly based on video analysis, lasers, and infrared cameras), but most of the work is still focused on artificial setups for crowds moving through corridors and crowds passing bottlenecks. Even though these controlled experiments are important to understand crowd dynamics, there is a knowledge gap between these experiments and the understanding of the dynamics leading to and occurring during large crowd disasters, when sometimes hundreds of thousands or even millions of pedestrians are involved. Numerous crowd disasters occur every year at large gatherings around the world. Unfortunately, the information about the (spatio-temporal) development of these events tend to be qualitative rather than quantitative. Video recordings from the crowd disaster in Mina, Kingdom of Saudi Arabia, on the 12th of January 2006, where hundreds of pilgrims lost their lives during the annual Muslim pilgrimage to Makkah, gave the possibility to scientifically evaluate the dynamics of the crowd. With this video material, it was possible to observe and analyze the behavior of the crowd under increasing crowd density, leading to the disaster. Based on the insights from the analysis of the crowd disaster described above, new tools and measures to detect and avoid critical crowd conditions have been proposed, and some of them have been implemented in order to reduce the likelihood of similar disasters in the future. Further contributions of this thesis are to empirically evaluate many of the previous assumptions used for pedestrian simulations. These assumptions are: * A pedestrian avoids collisions by changing her or his walking speed by an acceleration which is exponentially decaying with the distance to the pedestrian or object being avoided. * A pedestrian reacts stronger to what happens in front of her or him, than to what happens behind the back. * The movement of a crowd of pedestrians always follows a smooth flow-density relationship, called the fundamental diagram. * The walking speed will settle at 0 m/s at a specific maximum crowd density. The first two assumptions were found to be consistent with the data, but the pedestrian-flow theory had to be revised, since the two latter assumptions do not always hold. When these fundamental parts of pedestrian motion and avoiding maneuvers had been investigated, an improved version of the social-force-model was formulated. In order to enable the revision of previous works and the analysis of the crowd disaster mentioned above, algorithms used for video-tracking have been introduced. The novelty of this work concerns the uniqueness and quantity of data on which the algorithms are validated and calibrated, but also the focus on analyzing millions of pedestrians rather than hundreds. The aim of this thesis is to move from theoretical models and controlled lab conditions to applicable models for real-world conditions.

Level thesis:degree_level

thesis.doctoral

Grantor dc:publisher

Technische Universität Dresden

Year

2009

Author dc:creator

• Johansson, Anders

Contributors dc:contributor

• Helbing, Dirk
• Al-Abideen, Habib Z.
• Haase, Knut

• pedestrians
• crowds
• modeling
• Menschenmenge
• Simulation
• Fußgänger