The visualization of the model looks like this:
(Grey boxes are the obstacles. Yellow triangles are the agents.)
Here is a video showing the simulation process:
There are 16 entrances and 18 exits in the model. An agent is created at an entrance, and will choose one exit as its destination. Agents move towards their destinations using shortest route while avoiding both the fixed obstacles and the other agents. The rule of selecting shortest route is simple: set the patch that one can see with the lowest gradient as target, and move towards it. One can see a patch that is both within vision and not blocked by obstacles. The method of calculating gradients will be explained in the following text.
Diagram of the route-planning algorithm:
Two types of empirical data are used in this model. Firstly, the empirical of probability of choosing each entrance and exit is used when creating agents and assigning their entrance and exits. Secondly, the empirical data of how people have moved on this map on August 25th is used to construct the gradients map, according to which agents select their path towards their destinations. The more frequently being chosen as a path + the closer to destination, the lower the gradient will be. When the empirical gradient maps are not used, the gradients map is constructed purely based on distance to destinations. Four scenarios are designed to compare the simulation results with the empirical result, in order to show how mobility data could help to improve pedestrian models.
Scenario 1: No Realistic Information about Entrance/Exit Probabilities or Heat Maps
In this scenario, entrance and exit locations are considered known, but traffic flow through them is considered unknown. Under such conditions, we run the model to understand its basic functionality without calibrating it with real data about entrance and exit probabilities, nor activity-based heat maps. This will serve as a comparison benchmark, to assess later on how the ABM calibration through such information improves (or reduces) our ability to model movement within our scene.
Scenario 2: Realistic Entrance/Exit Probabilities But Disabled Heat Maps
In this scenario, we explore the effects of introducing realistic entrance and exit probabilities on the model. The heat map models used are distance-based, and not informed by the real datasets. Instead, we use distance-based gradients (i.e., agents choose an exit and walk the shortest route to that exit).
Scenario 3: Realistic Heat Maps but Disabled Entrance/Exit Probabilities
In this scenario we introduce real data-derived heat maps in the model calibration. These activity-based heat map-informed gradients are derived from harvesting the scene activity data, however entrance and exit probabilities are turned off. In a sense one could consider this a very simple form of learning how agents walk on paths more frequently traveled within the scene. It also allows us to compare to extent to which the quality of the results are due to the heat maps versus entrance and exit probability.
Scenario 4: Realistic Entrance/Exit Probabilities and Heat Maps Enabled
In the final scenario we use all available information to calibrate our ABM, namely, the heat map-informed gradients and entrance-exit combinations and see how this knowledge impacts the performance of the ABM.
Please note that there is one gradient map for each pair of entrance and exit, therefore, 16 * 18 = 288 maps are loaded. However, the final result is compared to only one path frequency map which is an empirical data obtained on August 25th. Also please note that, when the entrance/exit probabilities table is used, some entrances are exits have a probability of being chosen equals to zero. While the table is not used, agents just randomly choose any entrances or exits.
Please find the model here: