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archive/SRS_modeling_2025-11-10.py
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349
archive/SRS_modeling_2025-11-10.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Nov 10 01:02:31 2025
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@author: ethan
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"""
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# SPDX-License-Identifier: LGPL-3.0-or-later
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import numpy as np
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import pandas as pd
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import pathlib
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import jupedsim as jps
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from shapely import Polygon
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from shapely.plotting import plot_polygon
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from collections import deque
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def main():
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grades = {
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# mean and standard deviation for
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# average population size
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# and average speed by grade levels
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"Kindergarden":np.array([31.43,5.65,1.21,0.24]),
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"Grade 1":np.array([32.57,6.27,1.35,0.26]),
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"Grade 2":np.array([34.43,6.80,1.42,0.28]),
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"Grade 3":np.array([35.43,5.19,1.48,0.23]),
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"Grade 4":np.array([34.86,6.77,1.58,0.26]),
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"Grade 5":np.array([36.71,7.09,1.59,0.24]),
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"Grade 6":np.array([37.71,6.99,1.65,0.24]),
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"Grade 7":np.array([40.43,6.02,1.61,0.25]),
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"Grade 8":np.array([40.43,5.50,1.66,0.24]),
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"Grade 9":np.array([44.14,4.85,1.60,0.24]),
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"Grade 10":np.array([46.29,6.29,1.57,0.23]),
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"Grade 11":np.array([48.29,3.30,1.51,0.22]),
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"Grade 12":np.array([43.71,6.02,1.54,0.23])
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}
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df=pd.DataFrame({
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"Grade Level":(
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list(grades.keys())),
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"Pop Mean":[
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grades[j][0] for j in grades],
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"Pop Std Dev":[
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grades[j][1] for j in grades],
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"Speed Mean":[
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grades[j][2] for j in grades],
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"Speed Std Dev":[
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grades[j][3] for j in grades]})
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rng = np.random.default_rng(seed=42)
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A_crosswalk=Polygon([
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(-1,1.499),(-1,3.327),
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(0,3.327),(11.214, 3.327),
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(11.214,1.499),(0,1.499)])
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B_queue=Polygon([
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(11.214, 0),(22.163, 0),
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(22.163, 4.826),(11.214, 4.826)])
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C_road_adj_path=Polygon([
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(21.787,4.826),(24.214,4.826),
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(24.214,40.431),(29.179,40.431),
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(29.179,42.511),(24.214,42.511),
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(21.787,42.511)])
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D_path_k_3=Polygon([
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(26.45,42.511),(26.45,52.84),
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(26.45,53.84),(29.179,53.84),
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(29.179,52.84),(29.179,42.511)])
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E_path_4_6=Polygon([
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(29.179,42.511),(54.351,42.511),
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(60.406,48.842),(60.406,51.22),
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(60.406,52.22),(63.49,52.22),
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(63.49,51.22),(63.49,47.866),
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(56.381,40.431),(29.179,40.431)])
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F_path_7_12=Polygon([
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(22.163, 0),(39.227, 5.516),
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(39.631, 4.267),(39.939,3.315),
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(45.099,4.983),(44.792,5.935),
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(43.169,10.954),(24.214,4.826),
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(22.163,4.826)])
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G_extended_queue=Polygon([
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(11.214,0),(12.924,0),
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(12.924,-4.569),(11.214,-4.569)])
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H_angled_path=Polygon([
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(21.787,13.192),(21.787,10.527),
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(17,4.826),(14.767,4.826)])
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enter_k_3=Polygon([
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(26.45,52.84),(29.179,52.84),
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(29.179,53.84),(26.45,53.84)])
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enter_4_6=Polygon([
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(60.406,51.22),(60.406,52.22),
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(63.49,52.22),(63.49,51.22)])
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enter_7_12=Polygon([
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(39.631, 4.267),(39.939,3.315),
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(45.099,4.983),(44.792,5.935)])
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exit_polygon=Polygon([
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(0,1.499),(0,3.327),
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(-1,3.327),(-1,1.499)])
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plot_polygon(
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A_crosswalk,color="black",add_points=False)
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plot_polygon(
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B_queue,color="black",add_points=False)
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plot_polygon(
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C_road_adj_path, color="blue",add_points=False)
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plot_polygon(
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D_path_k_3, color="blue",add_points=False)
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plot_polygon(
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E_path_4_6, color="blue",add_points=False)
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plot_polygon(
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F_path_7_12, color="blue",add_points=False)
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plot_polygon(
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G_extended_queue, color="black",add_points=False)
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plot_polygon(
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H_angled_path, color="black",add_points=False)
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plot_polygon(
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enter_k_3, color="darkgreen",add_points=False)
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plot_polygon(
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enter_4_6, color="darkgreen",add_points=False)
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plot_polygon(
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enter_7_12, color="darkgreen",add_points=False)
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plot_polygon(
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exit_polygon, color="orangered",add_points=False)
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geometry = (A_crosswalk.union(
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B_queue).union(
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C_road_adj_path).union(
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D_path_k_3).union(
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E_path_4_6).union(
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F_path_7_12).union(
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G_extended_queue).union(
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H_angled_path))
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trajectory_file = "SRS_evac.sqlite"
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simulation = jps.Simulation(
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model=jps.AnticipationVelocityModel(),
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geometry=geometry,
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trajectory_writer=jps.SqliteTrajectoryWriter(
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output_file=pathlib.Path(trajectory_file)))
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exit_id = simulation.add_exit_stage(exit_polygon)
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journey = jps.JourneyDescription([exit_id])
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journey_id = simulation.add_journey(journey)
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grade_pop = {}
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door = {}
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door_polygon = {
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"K-3":enter_k_3,
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"4-6":enter_4_6,
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"7-12":enter_7_12,
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}
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platoon_agents = {}
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for i, grade in enumerate(df["Grade Level"]):
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grade_sample=rng.normal(
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loc=df["Pop Mean"][i],
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scale=df["Pop Std Dev"][i],size=1)
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grade_pop[grade] = int(np.ceil(grade_sample[0]))
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x = grade_pop[grade]
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if i < 4:
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door[grade] = "K-3"
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elif i <7:
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door[grade] = "4-6"
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else:
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door[grade] = "7-12"
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platoon_a_size = int(x/2)
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platoon_b_size = x - platoon_a_size
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platoon_a_id = (2*(i+1))-1
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platoon_b_id = (2*(i+1))
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platoon_agents[platoon_a_id] ={
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"Grade Level": grade,
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"Platoon Size": platoon_a_size,
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"Entry Door":door_polygon[door[grade]]
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}
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platoon_agents[platoon_b_id] ={
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"Grade Level": grade,
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"Platoon Size": platoon_b_size,
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"Entry Door":door_polygon[door[grade]]
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}
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#================================================
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#================================================
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#================================================
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#================================================
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agent_set = []
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for platoon_id, platoon_data in platoon_agents.items():
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spawn_time=float(
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rng.uniform(5,15)+rng.uniform(0,120))
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spawn_time=min(spawn_time,120)
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remaining=int(platoon_data["Platoon Size"])
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attempts = 0
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max_attempts = 10
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time_delay = 1.0
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batch_size=max(1,min(10,remaining))
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while remaining>0 and attempts<max_attempts:
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n_try = min(batch_size,remaining)
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try:
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positions = jps.distribute_by_number(
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polygon=platoon_data["Entry Door"],
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number_of_agents=n_try,
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distance_to_agents=0.45,
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distance_to_polygon=0.3,
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max_iterations=1500)
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placed_count = len(positions)
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if placed_count ==0:
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attempts +=1
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spawn_time+=time_delay
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batch_size=max(1,batch_size//2)
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continue
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offset=0.1
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for k, pos in enumerate(positions):
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speed=float(rng.normal(
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loc=df["Speed Mean"][i],
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scale=df["Speed Std Dev"][i],
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size=1)[0])
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agent = {
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"Grade Level":
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platoon_data["Grade Level"],
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"Entry Point":
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(door[platoon_data["Grade Level"]]),
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"Platoon":platoon_id,
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"Position":(
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float(pos[0]),float(pos[1])),
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"Speed":speed,
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"Spawn Time":float(
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spawn_time+(k*offset))}
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agent_set.append(agent)
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remaining-=placed_count
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if remaining>0:
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attempts+=1
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spawn_time+=time_delay
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attempts=0
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batch_size=max(1,min(10,remaining))
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except Exception as e:
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print(
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f"Error placing platoon {platoon_id}: {e}")
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print("Reducing batch and retrying")
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attempts+=1
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batch_size=max(1,batch_size//2)
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spawn_time+=time_delay
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#================================================
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#================================================
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#================================================
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#================================================
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# Group platoons by entry door first
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door_platoons = {}
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for platoon_id, platoon_data in platoon_agents.items():
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door = platoon_data["Entry Door"]
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if door not in door_platoons:
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door_platoons[door] = []
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door_platoons[door].append((platoon_id, platoon_data))
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# Process each door sequentially
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agent_set = []
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for door_poly, platoon_list in door_platoons.items():
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# Calculate total agents for this door
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total_agents = sum(data["Platoon Size"] for _, data in platoon_list)
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# Generate all positions for this door at once
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try:
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all_positions = jps.distribute_by_number(
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polygon=door_poly,
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number_of_agents=total_agents,
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distance_to_agents=0.6, # Increased for safety
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distance_to_polygon=0.3,
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max_iterations=1500
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)
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# Distribute positions to platoons
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position_index = 0
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for platoon_id, platoon_data in platoon_list:
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platoon_size = platoon_data["Platoon Size"]
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platoon_positions = all_positions[position_index:position_index + platoon_size]
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position_index += platoon_size
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# Create agents for this platoon
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spawn_time = float(rng.uniform(5,15) + rng.uniform(0,120))
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spawn_time = min(spawn_time, 120)
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offset = 0.1
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for k, pos in enumerate(platoon_positions):
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speed = float(rng.normal(
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loc=df["Speed Mean"][platoon_data["Grade Index"]], # You'll need to store grade index
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scale=df["Speed Std Dev"][platoon_data["Grade Index"]],
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size=1
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)[0])
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agent = {
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"Grade Level": platoon_data["Grade Level"],
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"Entry Point": door_poly, # Or door name if you prefer
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"Platoon": platoon_id,
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"Position": (float(pos[0]), float(pos[1])),
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"Speed": speed,
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"Spawn Time": float(spawn_time + (k * offset))
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}
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agent_set.append(agent)
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except Exception as e:
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print(f"Error generating positions for door: {e}")
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# Fallback: use your original per-platoon approach for this door
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#================================================
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#================================================
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#================================================
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#================================================
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pending=sorted(agent_set,key=lambda a:a["Spawn Time"])
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pending=deque(pending)
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max_iterations=1500
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spawned_total=0
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max_agents_per_step=25
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spawn_tolerance=1e-8
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while ((simulation.agent_count()>0 or len(pending)>0)
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and simulation.iteration_count()<max_iterations):
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current_time=simulation.elapsed_time()
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agents_this_step=0
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while (pending and (pending[0]["Spawn Time"]<=(
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current_time+spawn_tolerance))
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and agents_this_step<max_agents_per_step):
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a = pending.popleft()
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pos=tuple(a["Position"])
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v0 = float(a["Speed"])
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v0 = float(np.clip(v0,0.2,2.5))
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agent_params = (
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jps.AnticipationVelocityModelAgentParameters(
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journey_id=journey_id,
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stage_id=exit_id,
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position=pos,
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radius=0.25,
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desired_speed=v0,
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anticipation_time=0.5,
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reaction_time=0.3,
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wall_buffer_distance=0.08))
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retry=0
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max_retry=10
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while retry < max_retry:
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try:
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simulation.add_agent(agent_params)
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spawned_total +=1
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agents_this_step+=1
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break
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except Exception as e:
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print("Failed: add_agent")
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print(f"For: pos={pos}, speed={v0}")
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print(f"{e}: Rescheduling...")
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retry +=1
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if retry >= max_retry:
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print(f"\n\nMax Retries:{max_retry}")
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break
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adj_pos=(pos[0]+rng.uniform(-0.1,0.1),
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pos[1]+rng.uniform(-0.1,0.1))
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agent_params.position =adj_pos
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simulation.iterate()
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iter_count = simulation.iteration_count()
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print(f"Iteration Complete: {iter_count}")
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print("Simulation Completed!")
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print(f"Iterations: {simulation.iteration_count()}")
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print(f"Elapsed Time: {simulation.elapsed_time()}")
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print(f"{trajectory_file = }")
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if __name__ == "__main__":
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main()
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Reference in New Issue
Block a user