RoadTrafficModel_step3 - gama-platform/gama GitHub Wiki
This third step presents how to create a road system from GIS data. More precisely, it shows how to build a graph from a list of polylines and to constrain the movement of an agent according to this graph.
- For each
people
agent, definition of 2 new attributes:living_place
(building of type 'Residential') andworking_place
(building of type 'Industrial'). - For each
people
agent: definition ofstart_work
andend_work
hours attributes that respectively represent when the agent leaves its house to go to work and when it leaves its working place to go back home. These hours will be randomly defined between 6 AM (min_work_start
) and 8 AM (max_work_start
) forstart_work
and 4 PM (min_work_end
) and 8 PM (max_work_end
) forend_work
. - For each
people
agent: definition of anobjective
attribute: this one can 'go home' or 'working'. - For each
people
agent: define a speed. The speed will be randomly defined between 1 km/h (min_speed
) and 5 km/h (max_speed
). - The
people
agents move along the road, taking the shortest path. - All the previously introduced values will be defined in global attributes.
First, we have to change the skill of the people
agents: as we want to use an action of the moving
skill (goto
), we will provide the people
agents with this skill. A skill is a built-in module that provide the modeler a self-contain and relevant set of actions and variables.
species people skills: [moving] {
...
}
Then, we have to add new attributes to the people agents: living_place
, working_place
, start_work
, end_work
and objective
. In addition, we will add a the_target
variable that will represent the point toward which the agent will be currently moving.
species people skills: [moving]{
rgb color <- #yellow ;
building living_place <- nil ;
building working_place <- nil ;
int start_work ;
int end_work ;
string objective ;
point the_target <- nil ;
...
}
We define two reflexes that allow to change the objective
(and the_target
) of the agent at the start_work
and end_work
hours. Concerning the_target
value, we choose a random point in the objective building (working_place
or living_place
) by using the any_location_in
operator. The attribute current_date
is a built-in global attribute of type date
that is automatically incremented from the starting_date
of the simulation by step
value.
species people skills: [moving]{
...
reflex time_to_work when: current_date.hour = start_work and objective = "resting" {
objective <- "working" ;
the_target <- any_location_in (working_place);
}
reflex time_to_go_home when: current_date.hour = end_work and objective = "working" {
objective <- "resting" ;
the_target <- any_location_in (living_place);
}
...
}
At last, we define a reflex that allows the agent to move. If a target point is defined (the_target != nil
), the agent moves toward its target using the goto
action (provided by the moving
skill). Note that we specified a graph to constraint the movement of the agents on the road network with the facet on
. We will see later how this graph is built. The agent uses the shortest path (according to the graph) to go to the target point. When the agent arrives at its destination (the_target = self.location
), the target is set to nil (the agent will stop moving).
species people skills: [moving]{
...
reflex move when: the_target != nil {
do goto target: the_target on: the_graph ;
if the_target = location {
the_target <- nil ;
}
}
}
We add several parameters (and thus global variables) (min_work_start
, max_work_start
, min_work_end
, max_work_end
, min_speed
and max_speed
) and two global variables: the_graph
(graph computed from the road network). In addition, we set the starting date in order that the simulation starts at midnight.
In the global section:
global {
...
date starting_date <- date("2019-09-01-00-00-00");
int min_work_start <- 6;
int max_work_start <- 8;
int min_work_end <- 16;
int max_work_end <- 20;
float min_speed <- 1.0 #km / #h;
float max_speed <- 5.0 #km / #h;
graph the_graph;
...
}
In the experiment
section, we add a parameter
statement for each global variable we want to have as a parameter:
experiment road_traffic type: gui {
...
parameter "Earliest hour to start work" var: min_work_start category: "People" min: 2 max: 8;
parameter "Latest hour to start work" var: max_work_start category: "People" min: 8 max: 12;
parameter "Earliest hour to end work" var: min_work_end category: "People" min: 12 max: 16;
parameter "Latest hour to end work" var: max_work_end category: "People" min: 16 max: 23;
parameter "minimal speed" var: min_speed category: "People" min: 0.1 #km/#h ;
parameter "maximal speed" var: max_speed category: "People" max: 10 #km/#h;
...
}
First, we need to compute from the road
agents, a graph for the moving of the people
agents. The operator as_edge_graph
allows doing that. It automatically builds from a set of agents or geometries a graph where the agents are the edges of the graph, a node represent the extremities of the agent geometry.
global {
init {
...
create road from: shape_file_roads ;
the_graph <- as_edge_graph(road);
...
}
We randomly assign one working place and one house to each people
agent. To simplify the GAML code, we define two temporary variables: the list of buildings of type 'Residential' and the list of buildings of type 'Industrial' (by using the where
command). At the creation of each people
agent, we initialize its speed
(built-in attribute coming from the moving
skill), start_work
and end_work
to each people
agent (according to the min and max values defined in the global). We define as well an initial objective ("resting"). Concerning the definition of the living_place
and working_place
, these ones are randomly chosen among the residential_buildings
and industrial_buildings
lists.
init {
...
list<building> residential_buildings <- building where (each.type="Residential");
list<building> industrial_buildings <- building where (each.type="Industrial") ;
create people number: nb_people {
speed <- rnd(min_speed, max_speed);
start_work <- rnd (min_work_start, max_work_start);
end_work <- rnd(min_work_end, max_work_end);
living_place <- one_of(residential_buildings) ;
working_place <- one_of(industrial_buildings) ;
objective <- "resting";
location <- any_location_in (living_place);
}
...
}
https://github.com/gama-platform/gama.old/blob/GAMA_1.9.2/msi.gama.models/models/Tutorials/Road%20Traffic/models/Model%2003.gaml