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Nathancoisne authoredNathancoisne authored
3_Synthetic population.gaml 32.14 KiB
/**
* Name: Deplacementdevelos
* Based on the internal empty template.
* Author: nathancoisne
* Tags:
*/
model Deplacementdevelos
/* Génération d'une population synthétique de cyclistes
* Trois classes sociales sont représentées : travailleurs, étudiants et trajets de loisi
* Sources : Mobiliscope, données 'occupation des résidents par quartier'
* INSEE household surveys
*
*
*/
global {
string city <- "Marseille";
bool test_population <- false; // permet de générer une population test 20 fois moins peuplée que la population réelle
file bound <- shape_file("../includes/" + city + "/boundary_" + city + ".shp");
file shape_file_buildings; // building contenant les data osm, ign, pois
file shape_file_quartiers; // shapefile des quartiers de la ville considérée, importée de Mobiliscope
file residents_csv_file; // fichier contenant le nombre de résidents pour chaque quartier, importé de Mobiliscope
geometry shape <- envelope(bound);
// Etalonnage d'un modèle gravitaire pour fixer les trajets des agents (leur destination): + le coeff est grand + les trajets sont courts
float worker_adaptative_coeff <- 0.00083; // distance moyenne : 2000 m
float student_adaptative_coeff <- 0.00095; // distance moyenne : 1600 m
float leisure_adaptative_coeff <- 0.00083; // distance moyenne : 2000 m
float delivery_adaptative_coeff <- 0.0005; // distance moyenne : 3000 m
int hour_early_leisure_start <- 11;
int hour_late_leisure_start <- 15;
int hour_early_leisure_end <- 14;
int hour_late_leisure_end <- 18;
int hour_early_delivery_midi_start <- 11;
int hour_late_delivery_midi_start <- 13;
int hour_early_delivery_soir_start <- 18;
int hour_late_delivery_soir_start <- 20;
list<string> living_places <- ["house", "apartments", "dormitory", "hotel", "residential", "Résidentiel"];
list<string> work_places <- ["industrial", "office", "construction", "garages", "hospital", "service", "Commercial et services", "commercial",
"bakery", "bank", "bicycle_shop", "bookshop", "butcher", "cafe", "car_dealership", "community_centre", "courthouse",
"dentist", "department_store", "doctors", "fire_station", "florist", "furniture_shop", "greengrocer", "guesthouse",
"hairdresser", "kindergarten", "kiosk", "library", "mall", "optician", "pharmacy", "police", "post_office", "prison",
"public_building", "retail", "supermarket", "town_hall", "travel_agent", "warehouse", "wastewater_plant", "water_works"];
list<string> study_places <- ["university", "college", "school", "library"];
list<string> eating_places <- ["fast_food", "restaurant"];
list<string> leisure_places <- ["commercial", "kiosk", "chapel", "church", "service", "Sportif", "Commercial et services", "Religieux", "religious",
"bar", "buddhist", "cafe", "car_wash", "christian", "christian_catholic", "christian_evangelical", "christian_orthodox", "christian_protestant",
"cinema", "clothes", "convenience", "doityourself", "dog_park", "florist", "fort", "fountain", "garden_centre", "gift_shop", "golf_course", "jewish",
"mall", "museum", "muslim_sunni", "newsagent", "nightclub", "park", "picnic_site", "pitch", "playground", "pub", "sports_centre", "stadium",
"supermarket", "swimming_pool", "theatre" ];
list<string> tourism_places <- ["archaeological", "arts_centre", "artwork", "attraction", "castle", "chapel", "church", "Religieux", "religious", "buddhist",
"christian", "christian_catholic", "christian_evangelical", "christian_orthodox", "christian_protestant", "fort", "fountain", "garden_centre",
"gift_shop", "graveyard", "jewish", "memorial", "monument", "museum", "muslim_sunni", "park", "ruins", "stadium", "theatre", "tourist_info", "tower"];
init{
if city = "Marseille"{
shape_file_buildings <- shape_file("../includes/Marseille/buildings.shp"); // building contenant les data osm, ign, pois
shape_file_quartiers <- shape_file("../includes/Marseille/MARSEILLE_secteurs.shp");
residents_csv_file <- csv_file("../includes/Marseille//occ_nb_marseille.csv",","); //infos de population résidente dans chaque quartier
matrix data <- matrix(residents_csv_file);
create building from: shape_file_buildings with: [id::int(read("id"))] {
types <- types_str split_with ",";
}
create quartier from: shape_file_quartiers with: [ID::int(read("CODE_SEC")), name::read("LIB"), centre::int(read("centre"))]{
if not (self overlaps world) {
do die;
}
active_pop <- int(data[2, ID - 1]); // le nombre de travailleurs logeant dans le quartier = le nombre d'actifs présents dans le quartier à 4am
students_pop <- int(data[3, ID - 1]);
retired_pop <- int(data[5, ID - 1]);
inactive_pop <- int(data[6, ID - 1]);
without_job_pop <- int(data[4, ID - 1]);
float coeff_surface <- (world inter self).area / self.shape.area;
write("id : " + ID + " coeff : " + coeff_surface);
if coeff_surface < 0.98{
active_pop <- int(active_pop * coeff_surface);
students_pop <- int(students_pop * coeff_surface);
retired_pop <- int(retired_pop * coeff_surface);
inactive_pop <- int(inactive_pop * coeff_surface);
without_job_pop <- int(without_job_pop * coeff_surface);
}
if (centre = 1){
centre_ville <- true;
color <- #orange;
}
else{color <- #yellow;}
}
}
if city = "Toulouse"{
shape_file_buildings <- shape_file("../includes/Toulouse/buildings_with_pois.shp"); // building contenant les data osm, ign, pois
shape_file_quartiers <- shape_file("../includes/Toulouse/toulouse_secteurs.shp");
residents_csv_file <- csv_file("../includes/Toulouse/occ_nb_toulouse.csv",",");
matrix data <- matrix(residents_csv_file);
create building from: shape_file_buildings with: [id::int(read("id"))] {
types <- types_str split_with ",";
}
create quartier from: shape_file_quartiers with: [ID::int(read("CODE_SEC")), name::read("LIB"), centre::int(read("centre"))]{
if not (self overlaps world) {
do die;
}
active_pop <- int(data[2, ID - 1]); // le nombre de travailleurs logeant dans le quartier = le nombre d'actifs présents dans le quartier à 4am
students_pop <- int(data[3, ID - 1]); retired_pop <- int(data[5, ID - 1]);
inactive_pop <- int(data[6, ID - 1]);
without_job_pop <- int(data[4, ID - 1]);
float coeff_surface <- (world inter self).area / self.shape.area;
write("id : " + ID + " area : " + self.shape.area);
write("id : " + ID + " coeff : " + coeff_surface);
if (centre = 1){
centre_ville <- true;
color <- #red;
}
else{color <- #blue;}
}
}
list<building> residential_buildings <- building where (not empty(living_places inter each.types));
list<building> university_buildings <- building where (not empty(study_places inter each.types));
list<building> work_buildings <- building where (not empty(work_places inter each.types));
list<building> leisure_buildings <- building where (not empty(leisure_places inter each.types));
list<building> eat_buildings <- building where (not empty(eating_places inter each.types));
list<building> tourism_buildings <- building where (not empty(tourism_places inter each.types));
// placement des agents par quartier
int normalized_delivery_number;
int number_of_delivery;
if city = "Marseille"{number_of_delivery <- 850;}
if city = "Toulouse"{number_of_delivery <- 570;}
string file_path <- "../results/" + city + "/synthetic_population";
if test_population{file_path <- file_path + "_test";}
//
loop quart over: quartier {
write("population quartier " + quart.ID);
float cyclist_proportion;
// définition de la proportion de cyclistes parmi la population totale
if quart.centre_ville{ // proportion de cyclistes en centre-ville deux fois plus élevée (3.9%) qu'en périphérie (2.2%)
cyclist_proportion <- 0.039;
normalized_delivery_number <- 2 * int(number_of_delivery / (length(quartier) + length(quartier where each.centre_ville)));
}
else{
cyclist_proportion <- 0.022;
normalized_delivery_number <- int(number_of_delivery / (length(quartier) + length(quartier where each.centre_ville)));
}
int number_of_workers;
if city = "Marseille"{number_of_workers <- int(quart.active_pop * 0.013);} // source particulière : INSEE
if city = "Toulouse"{number_of_workers <- int(quart.active_pop * cyclist_proportion);}
int number_of_students <- int(quart.students_pop * cyclist_proportion);
int number_of_leisures <- int((quart.retired_pop + quart.without_job_pop) * cyclist_proportion);
if test_population {
number_of_workers <- int(number_of_workers / 20);
number_of_students <- int(number_of_students / 20);
number_of_leisures <- int(number_of_leisures / 20);
normalized_delivery_number <- int(normalized_delivery_number / 20);
}
create worker number: number_of_workers{
living_place <- one_of (residential_buildings where (each overlaps quart));
location <- any_location_in (living_place);
destination_place <- rnd_choice(gravitational_model(living_place, work_buildings where (each.location distance_to living_place < 15000 #m), worker_adaptative_coeff));
int_go <- int(gauss(120, 50)); // distribution gaussienne de l'heure de départ entre 6am et 10am
go_out_hour <- int(int_go / 60) + 6;
go_out_min <- int((int_go - (go_out_hour - 6) * 60) / 5) * 5;
int_home <- int(gauss(120, 50));
go_home_hour <- int(int_home / 60) + 15;
go_home_min <- int((int_home - (go_home_hour - 15) * 60) / 5) * 5;
save [go_out_hour, go_out_min, go_home_hour, go_home_min, living_place.id, destination_place.id] to: file_path + "/worker.csv" type:"csv" rewrite: false;
}
create student number: number_of_students {
living_place <- one_of (residential_buildings where (each overlaps quart));
location <- any_location_in (living_place);
destination_place <- rnd_choice(gravitational_model(living_place, university_buildings where (each.location distance_to living_place < 15000 #m), student_adaptative_coeff));
int_go <- int(gauss(90, 40));
go_out_hour <- int(int_go / 60) + 6;
go_out_min <- int((int_go - (go_out_hour - 6) * 60) / 5) * 5;
int_home <- int(gauss(90, 40));
go_home_hour <- int(int_home / 60) + 15;
go_home_min <- int((int_home - (go_home_hour - 15) * 60) / 5) * 5;
save [go_out_hour, go_out_min, go_home_hour, go_home_min, living_place.id, destination_place.id] to: file_path + "/student.csv" type:"csv" rewrite: false;
}
create leisure number: number_of_leisures {
living_place <- one_of (residential_buildings where (each overlaps quart));
location <- any_location_in (living_place);
destination_place <- rnd_choice(gravitational_model(living_place, leisure_buildings where (each.location distance_to living_place < 15000 #m), leisure_adaptative_coeff));
go_out_hour <- rnd(9, 12);
go_out_min <- rnd(0, 59, 5);
go_home_hour <- rnd(go_out_hour + 2, 21);
go_home_min <- rnd(0, 59, 5);
save [go_out_hour, go_out_min, go_home_hour, go_home_min, living_place.id, destination_place.id] to: file_path + "/leisure.csv" type:"csv" rewrite: false;
}
create delivery number: normalized_delivery_number {
living_place <- one_of (residential_buildings where (each overlaps quart));
location <- any_location_in (living_place);
if(rnd(1.0) <= 0.40){
midi <- true;
go_out_hour_midi <- rnd(hour_early_delivery_midi_start, hour_late_delivery_midi_start - 1);
go_out_min_midi <- rnd(0, 59, 5);
restaurant_1 <- rnd_choice(gravitational_model(living_place, eat_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
residential_place_1 <- rnd_choice(gravitational_model(restaurant_1, residential_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
restaurant_2 <- rnd_choice(gravitational_model(residential_place_1, eat_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
residential_place_2 <- rnd_choice(gravitational_model(restaurant_2, residential_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
restaurant_3 <- rnd_choice(gravitational_model(residential_place_2, eat_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
residential_place_3 <- rnd_choice(gravitational_model(restaurant_3, residential_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
save [go_out_hour_midi, go_out_min_midi, living_place.id, restaurant_1.id, residential_place_1.id, restaurant_2.id, residential_place_2.id, restaurant_3.id,
residential_place_3.id]
to: file_path + "/delivery_midi.csv" type:"csv" rewrite: false;
}
else {
midi <- false;
go_out_hour_soir <- rnd(hour_early_delivery_soir_start, hour_late_delivery_soir_start - 1);
go_out_min_soir <- rnd(0, 59, 5);
restaurant_1 <- rnd_choice(gravitational_model(living_place, eat_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
residential_place_1 <- rnd_choice(gravitational_model(restaurant_1, residential_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
restaurant_2 <- rnd_choice(gravitational_model(residential_place_1, eat_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
residential_place_2 <- rnd_choice(gravitational_model(restaurant_2, residential_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
restaurant_3 <- rnd_choice(gravitational_model(residential_place_2, eat_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
residential_place_3 <- rnd_choice(gravitational_model(restaurant_3, residential_buildings where (each.location distance_to living_place < 5000 #m), delivery_adaptative_coeff));
save [go_out_hour_soir, go_out_min_soir, living_place.id, restaurant_1.id, residential_place_1.id, restaurant_2.id, residential_place_2.id, restaurant_3.id,
residential_place_3.id]
to: file_path + "/delivery_soir.csv" type:"csv" rewrite: false;
}
}
}
write("Population de cyclistes à " + city + " :");
write("nombre de travailleurs : " + length(worker));
write("nombre d'étudiants : " + length(student));
write("nombre d'agents loisir : " + length(leisure));
write("nombre d'agents livreurs : " + length(delivery));
}
}
species building {
int id;
string type;
string types_str;
list<string> types;
float height;
rgb color <- #gray;
aspect base {
draw shape color: color ;
}
}
species quartier {
int ID;
string name;
int active_pop;
int students_pop;
int retired_pop;
int inactive_pop;
int without_job_pop;
int centre;
bool centre_ville; // si true : proportion de vélos 2x plus importante
rgb color;
aspect base {
draw shape width: 6;
}
}
species people skills: [moving] {
rgb color;
building living_place <- nil;
building destination_place <- nil;
int go_out_hour;
int go_out_min;
int go_home_hour;
int go_home_min;
map<building, float> gravitational_model(building starting_place, list<building> destination_to_choose, float adaptative_coeff){
// À partir d'une location de départ starting_place, choisit une destination parmi la liste destination_to_choose, selon un modèle gravitaire étalonné par adaptative_coeff
list<float> proba_list;
loop build over:destination_to_choose{
proba_list << exp(- adaptative_coeff * (starting_place distance_to build));
}
return create_map(destination_to_choose, proba_list); // retourne une map avec en clé la liste de buildings candidats, et en valeur la probabilité d'affectation du building
}
aspect base{
draw circle(6) color: color border: #black;
}
}
species worker parent: people {
rgb color <- #black;
int int_go min: 0 max: 240;
int int_home min: 0 max: 240;
}
species student parent: people{
rgb color <- #lightgreen;
int int_go min: 0 max: 180;
int int_home min: 0 max: 180;
}
species leisure parent: people{
rgb color <- #pink;
}
species delivery parent: people{
building restaurant_1;
building restaurant_2;
building restaurant_3;
building residential_place_1;
building residential_place_2;
building residential_place_3;
bool midi;
int go_out_hour_midi;
int go_out_min_midi;
int go_out_hour_soir;
int go_out_min_soir;
rgb color <- #red;
}
experiment population_generation type: gui {
parameter "Earliest hour to start leisure" var: hour_early_leisure_start category: "Leisure" min: 8 max: 13;
parameter "Latest hour to start leisure" var: hour_late_leisure_start category: "Leisure" min: 12 max: 18;
parameter "Earliest hour to end leisure" var: hour_early_leisure_end category: "Leisure" min: 11 max: 17;
parameter "Latest hour to end leisure" var: hour_late_leisure_end category: "Leisure" min: 15 max: 21;
output{
display quartiers{
species quartier;
//species building;
}
display travel_distance {
chart "Distribution de la distance de trajet des travailleurs" type: histogram background: #lightgrey size: {0.5, 0.5} position: {0, 0}{
data "0 - 1 km" value: worker count (each.living_place distance_to each.destination_place < 1000 #m) color: #blue;
data "1 - 2 km" value: worker count (each.living_place distance_to each.destination_place >= 1000 #m and each.living_place distance_to each.destination_place < 2000 #m) color: #blue;
data "2 - 3 km" value: worker count (each.living_place distance_to each.destination_place >= 2000 #m and each.living_place distance_to each.destination_place < 3000 #m) color: #blue;
data "3 - 4 km" value: worker count (each.living_place distance_to each.destination_place >= 3000 #m and each.living_place distance_to each.destination_place < 4000 #m) color: #blue;
data "4 - 5 km" value: worker count (each.living_place distance_to each.destination_place >= 4000 #m and each.living_place distance_to each.destination_place < 5000 #m) color: #blue;
data "5 - 6 km" value: worker count (each.living_place distance_to each.destination_place >= 5000 #m and each.living_place distance_to each.destination_place < 6000 #m) color: #blue;
data "6 - 7 km" value: worker count (each.living_place distance_to each.destination_place >= 6000 #m and each.living_place distance_to each.destination_place < 7000 #m) color: #blue;
data "7 - 8 km" value: worker count (each.living_place distance_to each.destination_place >= 7000 #m and each.living_place distance_to each.destination_place < 8000 #m) color: #blue;
data "8 - 9 km" value: worker count (each.living_place distance_to each.destination_place >= 8000 #m and each.living_place distance_to each.destination_place < 9000 #m) color: #blue;
data "9 - 10 km" value: worker count (each.living_place distance_to each.destination_place >= 9000 #m and each.living_place distance_to each.destination_place < 10000 #m) color: #blue;
data "10 - 11 km" value: worker count (each.living_place distance_to each.destination_place >= 10000 #m and each.living_place distance_to each.destination_place < 11000 #m) color: #blue;
data "11 - 12 km" value: worker count (each.living_place distance_to each.destination_place >= 11000 #m and each.living_place distance_to each.destination_place < 12000 #m) color: #blue;
data "12 - 13 km" value: worker count (each.living_place distance_to each.destination_place >= 12000 #m and each.living_place distance_to each.destination_place < 13000 #m) color: #blue;
data "13 - 14 km" value: worker count (each.living_place distance_to each.destination_place >= 13000 #m and each.living_place distance_to each.destination_place < 14000 #m) color: #blue;
data "14 - 15 km" value: worker count (each.living_place distance_to each.destination_place >= 14000 #m and each.living_place distance_to each.destination_place < 15000 #m) color: #blue;
data "15 - 16 km" value: worker count (each.living_place distance_to each.destination_place >= 15000 #m and each.living_place distance_to each.destination_place < 16000 #m) color: #blue;
data "16 - 17 km" value: worker count (each.living_place distance_to each.destination_place >= 16000 #m and each.living_place distance_to each.destination_place < 17000 #m) color: #blue;
data "17 - 18 km" value: worker count (each.living_place distance_to each.destination_place >= 17000 #m and each.living_place distance_to each.destination_place < 18000 #m) color: #blue;
data "18 - 19 km" value: worker count (each.living_place distance_to each.destination_place >= 18000 #m and each.living_place distance_to each.destination_place < 19000 #m) color: #blue;
data "19 - 20 km" value: worker count (each.living_place distance_to each.destination_place >= 19000 #m and each.living_place distance_to each.destination_place < 20000 #m) color: #blue;
}
chart "Distribution de la distance de trajet des étudiants" type: histogram background: #lightgrey size: {0.5, 0.5} position: {0, 0.5}{
data "0 - 1 km" value: student count (each.living_place distance_to each.destination_place < 1000 #m) color: #blue;
data "1 - 2 km" value: student count (each.living_place distance_to each.destination_place >= 1000 #m and each.living_place distance_to each.destination_place < 2000 #m) color: #blue;
data "2 - 3 km" value: student count (each.living_place distance_to each.destination_place >= 2000 #m and each.living_place distance_to each.destination_place < 3000 #m) color: #blue;
data "3 - 4 km" value: student count (each.living_place distance_to each.destination_place >= 3000 #m and each.living_place distance_to each.destination_place < 4000 #m) color: #blue;
data "4 - 5 km" value: student count (each.living_place distance_to each.destination_place >= 4000 #m and each.living_place distance_to each.destination_place < 5000 #m) color: #blue;
data "5 - 6 km" value: student count (each.living_place distance_to each.destination_place >= 5000 #m and each.living_place distance_to each.destination_place < 6000 #m) color: #blue;
data "6 - 7 km" value: student count (each.living_place distance_to each.destination_place >= 6000 #m and each.living_place distance_to each.destination_place < 7000 #m) color: #blue;
data "7 - 8 km" value: student count (each.living_place distance_to each.destination_place >= 7000 #m and each.living_place distance_to each.destination_place < 8000 #m) color: #blue;
data "8 - 9 km" value: student count (each.living_place distance_to each.destination_place >= 8000 #m and each.living_place distance_to each.destination_place < 9000 #m) color: #blue;
data "9 - 10 km" value: student count (each.living_place distance_to each.destination_place >= 9000 #m and each.living_place distance_to each.destination_place < 10000 #m) color: #blue;
data "10 - 11 km" value: student count (each.living_place distance_to each.destination_place >= 10000 #m and each.living_place distance_to each.destination_place < 11000 #m) color: #blue;
data "11 - 12 km" value: student count (each.living_place distance_to each.destination_place >= 11000 #m and each.living_place distance_to each.destination_place < 12000 #m) color: #blue;
data "12 - 13 km" value: student count (each.living_place distance_to each.destination_place >= 12000 #m and each.living_place distance_to each.destination_place < 13000 #m) color: #blue;
data "13 - 14 km" value: student count (each.living_place distance_to each.destination_place >= 13000 #m and each.living_place distance_to each.destination_place < 14000 #m) color: #blue;
data "14 - 15 km" value: student count (each.living_place distance_to each.destination_place >= 14000 #m and each.living_place distance_to each.destination_place < 15000 #m) color: #blue;
data "15 - 16 km" value: student count (each.living_place distance_to each.destination_place >= 15000 #m and each.living_place distance_to each.destination_place < 16000 #m) color: #blue;
data "16 - 17 km" value: student count (each.living_place distance_to each.destination_place >= 16000 #m and each.living_place distance_to each.destination_place < 17000 #m) color: #blue;
data "17 - 18 km" value: student count (each.living_place distance_to each.destination_place >= 17000 #m and each.living_place distance_to each.destination_place < 18000 #m) color: #blue;
data "18 - 19 km" value: student count (each.living_place distance_to each.destination_place >= 18000 #m and each.living_place distance_to each.destination_place < 19000 #m) color: #blue;
data "19 - 20 km" value: student count (each.living_place distance_to each.destination_place >= 19000 #m and each.living_place distance_to each.destination_place < 20000 #m) color: #blue;
}
chart "Distribution de la distance de trajet des leisure" type: histogram background: #lightgrey size: {0.5, 0.5} position: {0.5, 0}{
data "0 - 1 km" value: leisure count (each.living_place distance_to each.destination_place < 1000 #m) color: #blue;
data "1 - 2 km" value: leisure count (each.living_place distance_to each.destination_place >= 1000 #m and each.living_place distance_to each.destination_place < 2000 #m) color: #blue;
data "2 - 3 km" value: leisure count (each.living_place distance_to each.destination_place >= 2000 #m and each.living_place distance_to each.destination_place < 3000 #m) color: #blue;
data "3 - 4 km" value: leisure count (each.living_place distance_to each.destination_place >= 3000 #m and each.living_place distance_to each.destination_place < 4000 #m) color: #blue;
data "4 - 5 km" value: leisure count (each.living_place distance_to each.destination_place >= 4000 #m and each.living_place distance_to each.destination_place < 5000 #m) color: #blue;
data "5 - 6 km" value: leisure count (each.living_place distance_to each.destination_place >= 5000 #m and each.living_place distance_to each.destination_place < 6000 #m) color: #blue;
data "6 - 7 km" value: leisure count (each.living_place distance_to each.destination_place >= 6000 #m and each.living_place distance_to each.destination_place < 7000 #m) color: #blue;
data "7 - 8 km" value: leisure count (each.living_place distance_to each.destination_place >= 7000 #m and each.living_place distance_to each.destination_place < 8000 #m) color: #blue;
data "8 - 9 km" value: leisure count (each.living_place distance_to each.destination_place >= 8000 #m and each.living_place distance_to each.destination_place < 9000 #m) color: #blue;
data "9 - 10 km" value: leisure count (each.living_place distance_to each.destination_place >= 9000 #m and each.living_place distance_to each.destination_place < 10000 #m) color: #blue;
data "10 - 11 km" value: leisure count (each.living_place distance_to each.destination_place >= 10000 #m and each.living_place distance_to each.destination_place < 11000 #m) color: #blue;
data "11 - 12 km" value: leisure count (each.living_place distance_to each.destination_place >= 11000 #m and each.living_place distance_to each.destination_place < 12000 #m) color: #blue;
data "12 - 13 km" value: leisure count (each.living_place distance_to each.destination_place >= 12000 #m and each.living_place distance_to each.destination_place < 13000 #m) color: #blue;
data "13 - 14 km" value: leisure count (each.living_place distance_to each.destination_place >= 13000 #m and each.living_place distance_to each.destination_place < 14000 #m) color: #blue;
data "14 - 15 km" value: leisure count (each.living_place distance_to each.destination_place >= 14000 #m and each.living_place distance_to each.destination_place < 15000 #m) color: #blue;
data "15 - 16 km" value: leisure count (each.living_place distance_to each.destination_place >= 15000 #m and each.living_place distance_to each.destination_place < 16000 #m) color: #blue;
data "16 - 17 km" value: leisure count (each.living_place distance_to each.destination_place >= 16000 #m and each.living_place distance_to each.destination_place < 17000 #m) color: #blue;
data "17 - 18 km" value: leisure count (each.living_place distance_to each.destination_place >= 17000 #m and each.living_place distance_to each.destination_place < 18000 #m) color: #blue;
data "18 - 19 km" value: leisure count (each.living_place distance_to each.destination_place >= 18000 #m and each.living_place distance_to each.destination_place < 19000 #m) color: #blue;
data "19 - 20 km" value: leisure count (each.living_place distance_to each.destination_place >= 19000 #m and each.living_place distance_to each.destination_place < 20000 #m) color: #blue;
}
chart "Distance moyenne des trajets en fonction du motif" type: histogram background: #lightgrey size: {0.5, 0.5} position: {0.5, 0.5} {
list<float> travel_distance;
if(length(worker) != 0){
loop work over: worker{
travel_distance << work.living_place distance_to work.destination_place;
}
data "Travail" value: sum(travel_distance) / length(worker) color: #blue; }
if(length(student) != 0){
travel_distance <- [];
loop stud over: student{
travel_distance << stud.living_place distance_to stud.destination_place;
}
data "Etude" value: sum(travel_distance) / length(student) color: #blue;
}
if(length(leisure) != 0){
travel_distance <- [];
loop leis over: leisure{
travel_distance << leis.living_place distance_to leis.destination_place;
}
data "Loisir" value: sum(travel_distance) / length(leisure) color: #blue;
}
if (length(delivery) != 0){
travel_distance <- [];
loop deli over: delivery {
travel_distance << deli.living_place distance_to deli.restaurant_1;
}
data "Distance domicile / restaurant 1" value: sum(travel_distance) / length(delivery) color: #blue;
travel_distance <- [];
loop deli over: delivery {
travel_distance << deli.restaurant_1 distance_to deli.residential_place_1;
}
data "Distance resto1 / resi1" value: sum(travel_distance) / length(delivery) color: #blue;
}
}
}
display hour_travel {
chart "Distribution de l'heure de départ des agents worker" type: histogram background: #lightgrey size: {0.5, 0.5} position: {0, 0}{
loop i from: 0 to: 240 step: 5{
int hour_go <- int(i / 60);
data string(i) value: worker count (each.go_out_hour = int(i / 60) + 6 and each.go_out_min = int((i - (each.go_out_hour - 6) * 60) / 5) * 5) color: #blue;
}
}
chart "Distribution de l'heure de départ des agents student" type: histogram background: #lightgrey size: {0.5, 0.5} position: {0.5, 0.5}{
loop i from: 0 to: 180 step: 5{
int hour_go <- int(i / 60);
data string(i) value: student count (each.go_out_hour = int(i / 60) + 6 and each.go_out_min = int((i - (each.go_out_hour - 6) * 60) / 5) * 5) color: #blue;
}
}
}
}
}