The crowd movement study is concerned with the natural and obvious attitude of people on space and environment. whether it was singular interaction or groups collective interactions, in all cases, it takes every single movement trend into consideration. In some cases the crowd pursues an ultimate destination, in some other cases each crowd member has an independent path, but in all situations, they all follow the common collective spatial behaviors. these behaviors vary between following visual signs or tending towards calling sounds. the crowd mechanism is influenced by the environment characteristics and by the crowd itself as a bigger organism, or by other aggregating crowd movements in the same environment. for example, a member of crowd walking down a street, can be influenced by the neighbouring pedestrians in their walking patterns, he can also be affected by the street circumstances heading up the hills or so, or by the other kind of traffic, whether they were cycles, cars or a wave of confronting pedestrians.
Understanding crowd patterns can be very useful in conceiving, planning, and controlling the urban environment. our streets and public spaces are always influenced by the capacity and dynamics of its pedestrians and users. dozens of people using the side of the streets every day, they inhabit the squares and swarm the plazas, and attend the concerts. people have similar habits and correlated behaviors made them use these spaces at the same time. that because they are social, they want to feel the gathering, its the psychology forming their behavior. sometimes because they want to make stands and raise issues, so they assemble and create the protests or even the rebellions events(riots).
Crowd modeling is considered a comprehensive way to experience the architectural space before building it. without crowd simulation, an Architect design a building idealistically, and then get confronted later on by unexpected issues like overcrowding and disturbed circulation. in large building and structures, there are many issues emerge to surface beyond the vision of a conceptual designer. the long distances and the bottle-necked turns cause unfixable user experience. as such in airports, and stadiums, people get lost, they lose time finding ways, or they get the creepy panic attacks of overcrowding and long waiting lines. you can also see that in train stations and other places with spectators or mass audiences. people are looking for smooth flow and easy maneuvering while passing and using architectural spaces, especially if that is directly linked to tight schedules or enjoyment moments.
Experts and planners always looking for methods allow them to predict how the crowd responds to disasters and emergencies. urban areas are exposed and bared to the risk of natural catastrophe, and artificial ones as well. but what really lead to the worse consequences are the crowd collective behavior in the case of danger informing or sudden warnings. people start to feel the panic and they start to act irresponsibly. the kind of studies can save lives by conceiving the needs for safe zones or proper systems of egress.
Crowd simulation is not always a tool for disturbance, we incorporate the technology in game industry and film making. one of the most famous successful stories on the field the one we all saw in the Lord of the ring Trilogy. thousands of mediated armies were modeled and visualized using crowd algorithms. we also use it for military training and sports team coordinating. monitoring and sensing multiple groups behaviors is a strategical approach to see beyond the member and single actor eye.
To define the crowd simulation technically, it’s mainly defined as the process of using a computer program to mimic the collective human behavior, Its a kind of an agent-based design used to visualize the complexity and characteristics of the people interaction with each other or with their environments. an N-number of agents is poured into an environment or space to act independently and collectively at the same time. each member has the tendency to act according to a number of rules deeply embedded to its mechanical procedures. while the agent walks or move, it observes the environment contents by hearing, seeing or sensing topography and energy. it avoids colliding with the obstacle or other agents passing by. the groups usually as in real life, follow visible and invisible paths, pavements are the path, street corners and signs used to maneuver and take milestones. destinations are set collectively or independently. people follow rules of marching, they stop for a bigger mass of agents, such like cars and cycles, a singular agent turn around groups, they open passage for each other. all these behaviors are undertaken into consideration.
These algorithms help us resolve issues in architectural design we don’t even know it exists. this what I call it a materialistic approach into architecture, an approach which doesn’t rely on previous experiences and reference books. we are using a solid methodology to address the realistic issues. it gives us the power to point out to social issues that no one can touch.
Hypothesis 1/ we are not animals
In my experiment, I was trying to simulate how the motion of different particle groups can interact with each other. usually, it’s the issue of using swarm intelligence in modeling such problems. the unique thing is the people, us, we are not animals. we don’t run like kettles, and we don’t just bounce away from each other like balls. Human is much smarter and responsible creatures than cows and birds. we move steadily, and we intervene with manners. in other words; when a man is confronted with another, we either turn around him or giving him a passage. but in the current swarm intelligence algorithms, it does the typical animal herding and fish schooling. In real life we don’t change speed and direction suddenly like what we see in current crowd simulation software, we don’t group or cohese in a flock, we take our own path, and follow our own way. and this is my first hypothesis.
Hypothesis 2/fields of movements
The second Hypothesis that people walk independently, but they follow fields of movements. in other words, if an individual is taking a path, it’s influenced by others taking the same path, but it doesn’t get affected by the ones taking the opposite direction, and it doesn’t influence them either.
So I started with modeling the environment using Rhino, it’s a compositional representation which can be generalized either to architectural space or urban environment. Space include the origins, destinations and the connections. I needed also to build narrow passages which represent either door in buildings or crossings in streets.
As I mentioned at the beginning, the crowd simulation is an agent-based modeling which uses swarm intelligence to represent users behaviors. so I used Grasshopper and Quelea plugin to develop flocking system. the flocking system is a kind of an interactive algorithms consists of moving agents, each agent is randomly embedded with unique behavior settings. these behaviors are varying between wandering in random paths, eliminating collisions and following routes constraints. by adding time interval , each member does update itself incrementally to create a kind of uncontrolled collective behavior, and that was exactly what swarm meant to be about.
According to the first hypothesis, where I stated that the crowd algorithms need to be not similar to animals herding, so I tried to eliminate the bouncing behavior(by decreasing the weighting) because the human doesn’t jump off each other like balls. I also tried to create controlled speed and acceleration bounding between 2.5 and 3.1 m/s. that does help to give the simulation a realistic feeling. finally, I tried to develop a human-based wandering technique, that was processed by creating networked proximity and shortest path algorithms. this is almost the same how we perceptually build our invisible paths according to sensed patterns and perceived routes.
I reached a status of working algorithms which can help me in many different ways, that include the ability to measure the time required to evacuate a space, the practicality of monitoring the capacity of a sidewalk, and finally the ability to calculate the area needed to host a specific mass of crowds. The algorithms also helped me to understand the impact of angles and turns in slowing flows and delaying modes of egress. however, its still under developments and need more investigations.
In the future, I want to develop a new mechanism which follows my hypothesis 2. that mean I need to build two kinds of particles which can smartly follow pedestrian patterns. that mean to get into fields of forces rather than flocking like birds. so when a particle confronted by a mass of agents in the opposite direction it keeps piercing like professional handball player rather than cohesing like a fish. The other thing is I need to give particles ability to momentarily scan the environment and looking for routes and exits while navigating to its final destination. Finally, I need to add a new behavior which allows user to maneuver around others or to wait for bigger masses to move.