Make a microscopic simulation¶
In the two following models, individuals are seen as spherical particles. They move at a desired velocity to reach a goal (typically a door). Doing nothing more would lead to many overlaps between individuals. Hence the two families of models below can prevent these overlaps through two different approaches. For the Social Force models, and, for some forces are added to act against overlaps, and for the Granular model the velocities are projected into a permissible velocity space which ensures the absence of overlaps.
Granular model¶
The Granular model comes from crowd motion models of the granular type : each individual is identified to a hard disk of a prescribed size, subject to a non-overlapping constraint with their neighbors. The approach relies on a desired velocity for each individual (the velocity they would take if they were alone), and the global velocity field shall be defined as the closest to the desired one among all those feasible fields (fields which do not lead to overlapping of disks).
Reference : [MF2018] Chapter 4.
An example can be find in the directory
cromosim/examples/micro/granular
and can be launched with
python micro_granular.py --json input_room.json
python micro_granular.py --json input_event.json
python micro_granular.py --json input_stadium.json
python micro_granular.py --json input_shibuya_crossing.json
python micro_granular.py --json input_stairs.json
|
|
|---|
|
|
|
|
|---|
|
|
|
|
|
|---|---|
|
|
|
|
|
|---|
|
|
|
Floor 1 on the left and 0 on the right |
|---|
|
|
micro_granular.py¶
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 | # Authors:
# Sylvain Faure <sylvain.faure@universite-paris-saclay.fr>
# Bertrand Maury <bertrand.maury@universite-paris-saclay.fr>
#
# cromosim/examples/micro/granular/micro_granular.py
# python micro_granular.py --json input.json
#
# License: GPL
import sys, os
from cromosim import *
from cromosim.micro import *
from optparse import OptionParser
import json
import matplotlib
plt.ion()
"""
python3 micro_granular.py --json input.json
"""
parser = OptionParser(usage="usage: %prog [options] filename",
version="%prog 1.0")
parser.add_option('--json',dest="jsonfilename",default="input.json",
type="string", action="store",help="Input json filename")
opt, remainder = parser.parse_args()
print("===> JSON filename = ",opt.jsonfilename)
with open(opt.jsonfilename) as json_file:
try:
input = json.load(json_file)
except json.JSONDecodeError as msg:
print(msg)
print("Failed to load json file ",opt.jsonfilename)
print("Check its content \
(https://fr.wikipedia.org/wiki/JavaScript_Object_Notation)")
sys.exit()
"""
Get parameters from json file :
prefix: string
Folder name to store the results
with_graphes: bool
true if all the graphes are shown and saved in png files,
false otherwise
seed: integer
Random seed which can be used to reproduce a random selection
if >0
For each domain :
| name: string
| Domain name
| background: string
| Image file used as background
| px: float
| Pixel size in meters (also called space step)
| width: integer
| Domain width (equal to the width of the background image)
| height: integer
| Domain height (equal to the height of the background image)
| wall_colors: list
| rgb colors for walls
| [ [r,g,b],[r,g,b],... ]
| shape_lines: list
| Used to define the Matplotlib Polyline shapes,
| [
| {
| "xx": [x0,x1,x2,...],
| "yy": [y0,y1,y2,...],
| "linewidth": float,
| "outline_color": [r,g,b],
| "fill_color": [r,g,b]
| },...
| ]
| shape_circles: list
| Used to define the Matplotlib Circle shapes,
| [
| {
| "center_x": float,
| "center_y": float,
| "radius": float,
| "outline_color": [r,g,b],
| "fill_color": [r,g,b]
| },...
| ]
| shape_ellipses: list
| Used to define the Matplotlib Ellipse shapes,
| [
| {
| "center_x": float,
| "center_y": float,
| "width": float,
| "height": float,
| "angle_in_degrees_anti-clockwise": float (degre),
| "outline_color": [r,g,b],
| "fill_color": [r,g,b]
| },...
| ]
| shape_rectangles: list
| Used to define the Matplotlib Rectangle shapes,
| [
| {
| "bottom_left_x": float,
| "bottom_left_y": float,
| "width": float,
| "height": float,
| "angle_in_degrees_anti-clockwise": float (degre),
| "outline_color": [r,g,b],
| "fill_color": [r,g,b]
| },...
| ]
| shape_polygons: list
| Used to define the Matplotlib Polygon shapes,
| [
| {
| "xy": float,
| "outline_color": [r,g,b],
| "fill_color": [r,g,b]
| },...
| ]
| destinations: list
| Used to define the Destination objects,
| [
| {
| "name": string,
| "colors": [[r,g,b],...],
| "excluded_colors": [[r,g,b],...],
| "desired_velocity_from_color": [] or
| [
| {
| "color": [r,g,b],
| "desired_velocity": [ex,ey]
| },...
| ],
| "velocity_scale": float,
| "next_destination": null or string,
| "next_domain": null or string,
| "next_transit_box": null or [[x0,y0],...,[x3,y3]]
| },...
| ]
|--------------------
For each group of persons, required for the initialization process:
| nb:
| Number of people in the group
| domain:
| Name of the domain where people are located
| radius_distribution:
| Radius distribution used to create people
| ["uniform",min,max] or ["normal",mean,sigma]
| velocity_distribution:
| Velocity distribution used to create people
| ["uniform",min,max] or ["normal",mean,sigma]
| box:
| Boxe to randomly position people at initialization
| [ [x0,y0],[x1,y1],...]
| destination:
| Initial destination for the group
|--------------------
For each sensor:
| domain:
| Name of the domain where the sensor is located
| line:
| Segment through which incoming and outgoing flows are measured
| [x0,y0,x1,y1]
|--------------------
Tf: float
Final time
dt: float
Time step
drawper: integer
The results will be displayed every "drawper" iterations
projection_method: string
Name of the projection method : cvxopt(default),
mosek(a licence is needed) or uzawa
dmax: float
Maximum distance used to detect neighbors
dmin_people: float
Minimum distance allowed between individuals
dmin_walls: float
Minimum distance allowed between an individual and a wall
plot_people: boolean
If true, people are drawn
plot_contacts: boolean
If true, active contacts between people are drawn
plot_desired_velocities: boolean
If true, people desired velocities are drawn
plot_velocities: boolean
If true, people velocities are drawn
plot_sensors: boolean
If true, plot sensor lines on people graph and sensor data graph
plot_paths: boolean
If true, plot people paths
"""
prefix = input["prefix"]
if not os.path.exists(prefix):
os.makedirs(prefix)
seed = input["seed"]
with_graphes = input["with_graphes"]
json_domains = input["domains"]
#print("===> JSON data used to build the domains : ",json_domains)
json_people_init = input["people_init"]
#print("===> JSON data used to create groups : ",json_people_init)
json_sensors = input["sensors"]
#print("===> JSON data used to create sensors : ",json_sensors)
Tf = input["Tf"]
dt = input["dt"]
drawper = input["drawper"]
projection_method = input["projection_method"]
dmax = input["dmax"]
dmin_people = input["dmin_people"]
dmin_walls = input["dmin_walls"]
plot_p = input["plot_people"]
plot_c = input["plot_contacts"]
plot_v = input["plot_velocities"]
plot_vd = input["plot_desired_velocities"]
plot_s = input["plot_sensors"]
plot_pa = input["plot_paths"]
print("===> Final time, Tf = ",Tf)
print("===> Time step, dt = ",dt)
print("===> To draw the results each drawper iterations, drawper = ",
drawper)
print("===> Maximal distance to find neighbors, dmax = ",dmax,
", example : 2*dt")
print("===> Minimal distance between persons, dmin_people = ",
dmin_people)
print("===> Minimal distance between a person and a wall, dmin_walls = ",
dmin_walls)
"""
Build the Domain objects
"""
domains = {}
for i,jdom in enumerate(json_domains):
jname = jdom["name"]
print("===> Build domain number ",i," : ",jname)
jbg = jdom["background"]
jpx = jdom["px"]
jwidth = jdom["width"]
jheight = jdom["height"]
jwall_colors = jdom["wall_colors"]
if (jbg==""):
dom = Domain(name=jname, pixel_size=jpx, width=jwidth,
height=jheight, wall_colors=jwall_colors)
else:
dom = Domain(name=jname, background=jbg, pixel_size=jpx,
wall_colors=jwall_colors)
## To add lines : Line2D(xdata, ydata, linewidth)
for sl in jdom["shape_lines"]:
line = Line2D(sl["xx"],sl["yy"],linewidth=sl["linewidth"])
dom.add_shape(line,outline_color=sl["outline_color"],
fill_color=sl["fill_color"])
## To add circles : Circle( (center_x,center_y), radius )
for sc in jdom["shape_circles"]:
circle = Circle( (sc["center_x"], sc["center_y"]), sc["radius"] )
dom.add_shape(circle,outline_color=sc["outline_color"],
fill_color=sc["fill_color"])
## To add ellipses : Ellipse( (center_x,center_y), width, height,
## angle_in_degrees_anti-clockwise )
for se in jdom["shape_ellipses"]:
ellipse = Ellipse( (se["center_x"], se["center_y"]),
se["width"], se["height"],
se["angle_in_degrees_anti-clockwise"])
dom.add_shape(ellipse,outline_color=se["outline_color"],
fill_color=se["fill_color"])
## To add rectangles : Rectangle( (bottom_left_x,bottom_left_y),
## width, height, angle_in_degrees_anti-clockwise )
for sr in jdom["shape_rectangles"]:
rectangle = Rectangle( (sr["bottom_left_x"],sr["bottom_left_y"]),
sr["width"], sr["height"],
sr["angle_in_degrees_anti-clockwise"])
dom.add_shape(rectangle,outline_color=sr["outline_color"],
fill_color=sr["fill_color"])
## To add polygons : Polygon( [[x0,y0],[x1,y1],...] )
for spo in jdom["shape_polygons"]:
polygon = Polygon(spo["xy"])
dom.add_shape(polygon,outline_color=spo["outline_color"],
fill_color=spo["fill_color"])
## To build the domain : background + shapes
dom.build_domain()
## To add all the available destinations
for j,dd in enumerate(jdom["destinations"]):
desired_velocity_from_color=[]
for gg in dd["desired_velocity_from_color"]:
desired_velocity_from_color.append(
np.concatenate((gg["color"],gg["desired_velocity"])))
dest = Destination(name=dd["name"],colors=dd["colors"],
excluded_colors=dd["excluded_colors"],
desired_velocity_from_color=desired_velocity_from_color,
velocity_scale=dd["velocity_scale"],
next_destination=dd["next_destination"],
next_domain=dd["next_domain"],
next_transit_box=dd["next_transit_box"])
print("===> Destination : ",dest)
dom.add_destination(dest)
if (with_graphes):
dom.plot_desired_velocity(dd["name"],id=100*i+10+j,step=20)
print("===> Domain : ",dom)
if (with_graphes):
dom.plot(id=100*i)
dom.plot_wall_dist(id=100*i+1,step=20)
domains[dom.name] = dom
print("===> All domains = ",domains)
"""
To create the sensors to measure the pedestrian flows
"""
all_sensors = {}
for domain_name in domains:
all_sensors[domain_name] = []
for s in json_sensors:
s["id"] = []
s["times"] = []
s["xy"] = []
s["dir"] = []
all_sensors[s["domain"]].append(s)
#print("===> All sensors = ",all_sensors)
"""
Initialization
"""
## Current time
t = 0.0
counter = 0
## Initialize people
all_people = {}
for i,peopledom in enumerate(json_people_init):
dom = domains[peopledom["domain"]]
groups = peopledom["groups"]
print("===> Group number ",i,", domain = ",peopledom["domain"])
people = people_initialization(dom, groups, dt,
dmin_people=dmin_people, dmin_walls=dmin_walls, seed=seed,
itermax=10,projection_method=projection_method, verbose=True)
I, J, Vd = dom.people_desired_velocity(people["xyrv"],
people["destinations"])
people["Vd"] = Vd
for ip,pid in enumerate(people["id"]):
people["paths"][pid] = people["xyrv"][ip,:2]
contacts = None
if (with_graphes):
colors = people["xyrv"][:,2]
plot_people(100*i+20, dom, people, contacts, colors, time=t,
plot_people=plot_p, plot_contacts=plot_c,
plot_velocities=plot_v, plot_desired_velocities=plot_vd,
plot_sensors=plot_s, sensors=all_sensors[dom.name],
savefig=True, filename=prefix+dom.name+'_fig_'+ \
str(counter).zfill(6)+'.png')
all_people[peopledom["domain"]] = people
#print("===> All people = ",all_people)
"""
Main loop
"""
cc = 0
draw = True
while (t<Tf):
print("\n===> Time = "+str(t))
## Compute people desired velocity
for idom,name in enumerate(domains):
print("===> Compute desired velocity for domain ",name)
dom = domains[name]
people = all_people[name]
I, J, Vd = dom.people_desired_velocity(people["xyrv"],
people["destinations"])
people["Vd"] = Vd
people["I"] = I
people["J"] = J
## Look at if there are people in the transit boxes
print("===> Find people who have to be duplicated")
virtual_people = find_duplicate_people(all_people, domains)
#print(" virtual_people : ",virtual_people)
## Projection
for idom,name in enumerate(domains):
print("===> Projection step for domain ",name)
dom = domains[name]
people = all_people[name]
try:
xyrv = np.concatenate((people["xyrv"],
virtual_people[name]["xyrv"]))
Vd = np.concatenate((people["Vd"],
virtual_people[name]["Vd"]))
except:
xyrv = people["xyrv"]
Vd = people["Vd"]
if (xyrv.shape[0]>0):
if (np.unique(xyrv, axis=0).shape[0] != xyrv.shape[0]):
print("===> ERROR : There are two identical lines \
in the")
print(" array xyrv used to determine the \
contacts between")
print(" individuals and this is not normal.")
sys.exit()
contacts = compute_contacts(dom, xyrv, dmax)
print(" Number of contacts: ",contacts.shape[0])
info, B, U, L, P = projection(dt, xyrv,
contacts, Vd,
dmin_people = dmin_people,
dmin_walls = dmin_walls,
method=projection_method,
log=True)
nn = people["xyrv"].shape[0]
all_people[name]["U"] = U[:nn,:]
virtual_people[name]["U"] = U[nn:,:]
all_people[name], all_sensors[name] = move_people(t, dt,
all_people[name],
all_sensors[name])
if (draw and with_graphes):
## coloring people according to their radius
colors = all_people[name]["xyrv"][:,2]
## coloring people according to their destinations
# colors = np.zeros(all_people[name]["xyrv"].shape[0])
# for i,dest_name in enumerate(list(dom.destinations.keys())):
# ind = np.where(all_people[name]["destinations"]==dest_name)[0]
# if (ind.shape[0]>0):
# colors[ind]=i
plot_people(100*idom+20, dom, all_people[name], contacts,
colors, virtual_people=virtual_people[name], time=t,
plot_people=plot_p, plot_contacts=plot_c,
plot_paths=plot_pa, plot_velocities=plot_v,
plot_desired_velocities=plot_vd, plot_sensors=plot_s,
sensors=all_sensors[dom.name], savefig=True,
filename=prefix+dom.name+'_fig_'
+ str(counter).zfill(6)+'.png')
plt.pause(0.01)
## Update people destinations
all_people = people_update_destination(all_people,domains,dom.pixel_size)
## Print the number of persons for each domain
for idom,name in enumerate(domains):
print("===> Domain ",name," nb of persons = ",
all_people[name]["xyrv"].shape[0])
t += dt
cc += 1
counter += 1
if (cc>=drawper):
draw = True
cc = 0
else:
draw = False
for idom,domain_name in enumerate(all_sensors):
print("===> Plot sensors of domain ",domain_name)
plot_sensors(100*idom+40, all_sensors[domain_name], t, savefig=True,
filename=prefix+'sensor_'+str(i)+'_'+str(counter)+'.png')
plt.pause(0.01)
plt.ioff()
plt.show()
sys.exit()
|
Social force model¶
The Social Force model has been introduced in the 90’s. Pedestrians are identified with inertial particles submitted to a forcing term which implements the individual tendencies and extra forces which account for interactions with other pedestrians (typically the tendency to preserve a certain distance with neighbors).
Reference : [MF2018] Chapter 3.
Some examples can be found in the directory
and can be launched with
Floor 1 on the left and 0 on the right