Demo - MATSim Population for West London¶

This notebook demonstrates an complex example workflow for creating a sample population for an area in West London. It creates agent plans for people and households using a random process.

Aim¶

Create a bigger and more realistic sample population automatically for the West London area called Londinium. The sample population includes various activities, personal attributes and modes; the population would be used as input for MATSim transport simulation.

Steps:

1. Import geographic data of Londinium;
2. Facility sampling from OpenStreetMap data;
3. Activity generation model with home based tours. Expand agents with different personal attributes, activities and trips;
4. Perform Data Visualization and validation. Plot the activity plan, distance and duration of population;
5. Export intermediate CSV tables of the population

In [1]:

import os

import geopandas as gp
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from pam.activity import Activity, Leg
from pam.core import Household, Person, Population
from pam.plot.stats import plot_activity_times
from pam.read import load_travel_diary
from pam.report.benchmarks import distance_counts, duration_counts
from pam.samplers import facility
from pam.utils import minutes_to_datetime as mtdt
from pam.variables import END_OF_DAY
from pam.write import to_csv, write_matsim, write_od_matrices

%matplotlib inline

import os import geopandas as gp import matplotlib.pyplot as plt import numpy as np import pandas as pd from pam.activity import Activity, Leg from pam.core import Household, Person, Population from pam.plot.stats import plot_activity_times from pam.read import load_travel_diary from pam.report.benchmarks import distance_counts, duration_counts from pam.samplers import facility from pam.utils import minutes_to_datetime as mtdt from pam.variables import END_OF_DAY from pam.write import to_csv, write_matsim, write_od_matrices %matplotlib inline

Import geographic data of Londinium¶

In [2]:

# Import geographic data of west london area
network_bb_path = os.path.join("data", "network_bounding_box.geojson")
lsoas_path = os.path.join("data", "lsoas")  # lsoas: lower layer super output areas

# Import geographic data of west london area network_bb_path = os.path.join("data", "network_bounding_box.geojson") lsoas_path = os.path.join("data", "lsoas") # lsoas: lower layer super output areas

We will start by plotting Londinium boundary

In [3]:

# Read the file and plot the boundary
boundary = gp.read_file(network_bb_path)

# Transform to epsg:27700
boundary = boundary.to_crs("epsg:27700")
boundary.plot()

# Read the file and plot the boundary boundary = gp.read_file(network_bb_path) # Transform to epsg:27700 boundary = boundary.to_crs("epsg:27700") boundary.plot()

Out[3]:

<Axes: >

Next we will plot Londinium outline shown above over a map of London to see where exactly it is located.

In [4]:

# Plot boundary area in lsoas
lsoas = gp.read_file(lsoas_path)
lsoas.crs = "EPSG:27700"
print(lsoas.crs)
lsoas = lsoas.set_index("LSOA_CODE")

fig, ax = plt.subplots(figsize=(10, 10))
lsoas.plot(ax=ax)
boundary.plot(ax=ax, color="red")

# Plot boundary area in lsoas lsoas = gp.read_file(lsoas_path) lsoas.crs = "EPSG:27700" print(lsoas.crs) lsoas = lsoas.set_index("LSOA_CODE") fig, ax = plt.subplots(figsize=(10, 10)) lsoas.plot(ax=ax) boundary.plot(ax=ax, color="red")

EPSG:27700

Out[4]:

<Axes: >

Finally, we will plot Londinium with LSOA boundaries included.

In [5]:

# Overlay the area using geopandas package
lsoas_clipped = gp.overlay(lsoas, boundary, how="intersection")
lsoas_clipped.plot()

# Overlay the area using geopandas package lsoas_clipped = gp.overlay(lsoas, boundary, how="intersection") lsoas_clipped.plot()

/Users/bryn.pickering/mambaforge/envs/pam/lib/python3.11/site-packages/shapely/set_operations.py:133: RuntimeWarning: invalid value encountered in intersection
  return lib.intersection(a, b, **kwargs)

Out[5]:

<Axes: >

In [6]:

lsoas_clipped.head()

lsoas_clipped.head()

Out[6]:

	LSOA_NAME	MSOA_CODE	MSOA_NAME	STWARDCODE	STWARDNAME	LA_CODE	LA_NAME	geometry
0	Hammersmith and Fulham 010A	E02000381	Hammersmith and Fulham 010	00ANGA	Addison	00AN	Hammersmith and Fulham	POLYGON ((523932.247 179242.842, 523959.439 17...
1	Hammersmith and Fulham 010B	E02000381	Hammersmith and Fulham 010	00ANGA	Addison	00AN	Hammersmith and Fulham	POLYGON ((524171.272 179363.077, 524212.654 17...
2	Hammersmith and Fulham 012A	E02000383	Hammersmith and Fulham 012	00ANGC	Avonmore and Brook Green	00AN	Hammersmith and Fulham	POLYGON ((524167.660 178997.302, 524060.845 17...
3	Hammersmith and Fulham 012B	E02000383	Hammersmith and Fulham 012	00ANGC	Avonmore and Brook Green	00AN	Hammersmith and Fulham	POLYGON ((523774.000 178714.003, 523831.847 17...
4	Hammersmith and Fulham 012C	E02000383	Hammersmith and Fulham 012	00ANGC	Avonmore and Brook Green	00AN	Hammersmith and Fulham	MULTIPOLYGON (((524422.688 178825.081, 524379....

Facility sampler¶

In [7]:

facilities_path = "data/londinium_facilities_sample.geojson"
facilities = gp.read_file(facilities_path)
facilities = facilities.rename({"activities": "activity"}, axis=1)
facilities.crs = "EPSG:27700"
facilities.head()

facilities_path = "data/londinium_facilities_sample.geojson" facilities = gp.read_file(facilities_path) facilities = facilities.rename({"activities": "activity"}, axis=1) facilities.crs = "EPSG:27700" facilities.head()

Out[7]:

	activity	area	distance_to_nearest_education	distance_to_nearest_medical	distance_to_nearest_shop	distance_to_nearest_transit	floor_area	id	levels	units	geometry
0	home	574	617.965594	516.743962	77.712882	466.059745	1148.0	1084822608	2.0	1.0	POINT (524877.659 179721.080)
1	home	66	143.055807	115.674294	125.537224	286.017738	198.0	368319574	3.0	1.0	POINT (527830.357 174758.729)
2	home	103	54.946075	214.532285	41.572871	93.975944	412.0	1640220880	4.0	1.0	POINT (526060.994 178970.515)
3	home	192	164.455318	216.217139	111.674214	180.452314	768.0	1741392588	4.0	1.0	POINT (526698.625 178513.841)
4	home	123	173.648285	249.190465	188.276309	139.258340	246.0	984446626	2.0	1.0	POINT (526369.238 179166.396)

Start by plotting different facility types, e.g. educational and medical facilities

In [8]:

education = facilities[facilities["activity"] == "education"]
medical = facilities[facilities["activity"] == "medical"]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 12))

boundary.plot(ax=ax1, color="steelblue")
education.plot(ax=ax1, color="orange", label="Educational facilities")
ax1.legend()

boundary.plot(ax=ax2, color="steelblue")
medical.plot(ax=ax2, color="red", label="Medical facilities")
ax2.legend()

education = facilities[facilities["activity"] == "education"] medical = facilities[facilities["activity"] == "medical"] fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 12)) boundary.plot(ax=ax1, color="steelblue") education.plot(ax=ax1, color="orange", label="Educational facilities") ax1.legend() boundary.plot(ax=ax2, color="steelblue") medical.plot(ax=ax2, color="red", label="Medical facilities") ax2.legend()

Out[8]:

<matplotlib.legend.Legend at 0x13f575590>

In [9]:

lsoas_clipped.crs = "EPSG:27700"
len(lsoas_clipped)

lsoas_clipped.crs = "EPSG:27700" len(lsoas_clipped)

Out[9]:

306

In [10]:

lsoas_clipped = lsoas_clipped.set_index("LSOA_NAME")

lsoas_clipped = lsoas_clipped.set_index("LSOA_NAME")

In [11]:

# build the sampler
facility_sampler = facility.FacilitySampler(
    facilities=facilities, zones=lsoas_clipped, build_xml=True, fail=False, random_default=True
)

# build the sampler facility_sampler = facility.FacilitySampler( facilities=facilities, zones=lsoas_clipped, build_xml=True, fail=False, random_default=True )

Joining facilities data to zones, this may take a while.
Building sampler, this may take a while.

Activity generation model¶

In [12]:

# Create random area sample


def random_area_sampler():
    indexes = list(lsoas_clipped.index)
    return np.random.choice(indexes)


random_area_sampler()  # test

# Create random area sample def random_area_sampler(): indexes = list(lsoas_clipped.index) return np.random.choice(indexes) random_area_sampler() # test

Out[12]:

'Hammersmith and Fulham 020A'

• It is a simple home based tours within 24 hours.
• We create different activity types: work, leisure, education, shopping, etc. Different transport model types: car, bus, subway, etc.
• Random number is assigned to the duration for each activity and transport mode

In [13]:

# mapping the MSOA and LAD with index
mapping_dict = dict(zip(lsoas_clipped.index, lsoas_clipped.MSOA_CODE))
mapping_dict1 = dict(zip(lsoas_clipped.index, lsoas_clipped.LA_NAME))

# mapping the MSOA and LAD with index mapping_dict = dict(zip(lsoas_clipped.index, lsoas_clipped.MSOA_CODE)) mapping_dict1 = dict(zip(lsoas_clipped.index, lsoas_clipped.LA_NAME))

In [14]:

# Generate agents in west london area


def generate_agents(no_of_agents):
    """
    Randomly create agents with simple home-based tours.
    The trip starts from home, has a random number of various acitivites, tranport modes would be added.
    The trip finally ends at home.

    """
    population = Population()  # Initialise an empty population

    # Create simple personal attributes
    income = ["low", "medium", "high"]
    gender = ["male", "female"]
    sort_age = [
        "0 to 4",
        "5 to 10",
        "11 to 15",
        "16 to 20",
        "21 to 25",
        "26 to 29",
        "30 to 39",
        "40 to 49",
        "50 to 59",
        "60 to 64",
        "65 to 69",
        "70 to 74",
        "75 to 79",
        "80 to 84",
        "85  and over",
    ]

    # Create mode and activities
    transport = ["car", "bus", "ferry", "rail", "subway", "bike", "walk"]
    # Removed gym and park due to osmox problem
    activity = [
        "leisure",
        "work",
        "shop",
        "medical",
        "education",
        "park",
        "pub",
        "gym",
    ]  # Primary activity
    sub_activity = [
        "shop",
        "medical",
        "pub",
        "gym",
    ]  # People usually spend less time on sub activity

    # Add activity plan for each person
    for i in range(no_of_agents):
        # Create different agents and household
        agent_id = f"agent_{i}"
        hh_id = f"hh_{i}"
        hh = Household(hh_id, freq=1)

        # Adding Activities and Legs alternately to different agents
        # Activity 1 - home
        leaves_home = (np.random.randint(6, 8) * 60) + np.random.randint(0, 100)  # minutes
        location1 = random_area_sampler()
        location1_loc = facility_sampler.sample(location1, "home")
        lsoa_name = mapping_dict.get(location1)
        lad_name = mapping_dict1.get(location1)

        agent = Person(
            agent_id,
            freq=1,
            attributes={
                "subpopulation": np.random.choice(income) + " income",
                "gender": np.random.choice(gender),
                "age": np.random.choice(sort_age),
                "household_zone": location1,
                "household_LSOA": lsoa_name,
                "household_LAD": lad_name,
            },
        )

        hh.add(agent)
        population.add(hh)

        # Trip duration
        trip_duration_main_activity = np.random.randint(3, 6) * 60
        trip_duration_sub_activity = np.random.randint(1, 3) * 60

        agent.add(
            Activity(
                seq=1,
                act="home",
                area=location1,
                loc=location1_loc,
                start_time=mtdt(0),
                end_time=mtdt(leaves_home),
            )
        )

        # Initiated parameters
        location_prev = location1
        location_prev_loc = location1_loc
        leave_time = leaves_home

        # Add random numbers of activities
        no_of_activities = np.random.randint(1, 5)

        for i in range(no_of_activities):
            arrives_primary = leave_time + np.random.randint(10, 90)  # minutes

            # Activity 2.
            if i < 2:  # Start with main activity
                random_act = np.random.choice(activity)
            else:
                random_act = np.random.choice(sub_activity)

            if random_act == ("work"):
                leaves_primary = arrives_primary + trip_duration_main_activity
            else:
                leaves_primary = arrives_primary + trip_duration_sub_activity

            # Outbound leg
            location_next = random_area_sampler()
            location_next_loc = facility_sampler.sample(location_next, random_act)

            agent.add(
                Leg(
                    seq=i + 1,
                    mode=np.random.choice(transport),
                    start_area=location_prev,
                    start_loc=location_prev_loc,
                    end_area=location_next,
                    end_loc=location_next_loc,
                    start_time=mtdt(leave_time),
                    end_time=mtdt(arrives_primary),
                )
            )

            agent.add(
                Activity(
                    seq=i + 2,
                    act=random_act,
                    area=location_next,
                    loc=location_next_loc,
                    start_time=mtdt(arrives_primary),
                    end_time=mtdt(leaves_primary),
                )
            )

            # Update parameters
            leave_time = leaves_primary
            location_prev = location_next
            location_prev_loc = location_next_loc

        # Inbound leg
        arrives_home = leave_time + np.random.randint(10, 90)  # minutes
        agent.add(
            Leg(
                seq=no_of_activities + 1,
                mode=np.random.choice(transport),
                start_area=location_next,
                start_loc=location_next_loc,
                end_area=location1,
                end_loc=location1_loc,
                start_time=mtdt(leave_time),
                end_time=mtdt(arrives_home),
            )
        )

        # Activity
        agent.add(
            Activity(
                seq=no_of_activities + 2,
                act="home",
                area=location1,
                loc=location1_loc,
                start_time=mtdt(arrives_home),
                end_time=END_OF_DAY,
            )
        )

    return population

# Generate agents in west london area def generate_agents(no_of_agents): """ Randomly create agents with simple home-based tours. The trip starts from home, has a random number of various acitivites, tranport modes would be added. The trip finally ends at home. """ population = Population() # Initialise an empty population # Create simple personal attributes income = ["low", "medium", "high"] gender = ["male", "female"] sort_age = [ "0 to 4", "5 to 10", "11 to 15", "16 to 20", "21 to 25", "26 to 29", "30 to 39", "40 to 49", "50 to 59", "60 to 64", "65 to 69", "70 to 74", "75 to 79", "80 to 84", "85 and over", ] # Create mode and activities transport = ["car", "bus", "ferry", "rail", "subway", "bike", "walk"] # Removed gym and park due to osmox problem activity = [ "leisure", "work", "shop", "medical", "education", "park", "pub", "gym", ] # Primary activity sub_activity = [ "shop", "medical", "pub", "gym", ] # People usually spend less time on sub activity # Add activity plan for each person for i in range(no_of_agents): # Create different agents and household agent_id = f"agent_{i}" hh_id = f"hh_{i}" hh = Household(hh_id, freq=1) # Adding Activities and Legs alternately to different agents # Activity 1 - home leaves_home = (np.random.randint(6, 8) * 60) + np.random.randint(0, 100) # minutes location1 = random_area_sampler() location1_loc = facility_sampler.sample(location1, "home") lsoa_name = mapping_dict.get(location1) lad_name = mapping_dict1.get(location1) agent = Person( agent_id, freq=1, attributes={ "subpopulation": np.random.choice(income) + " income", "gender": np.random.choice(gender), "age": np.random.choice(sort_age), "household_zone": location1, "household_LSOA": lsoa_name, "household_LAD": lad_name, }, ) hh.add(agent) population.add(hh) # Trip duration trip_duration_main_activity = np.random.randint(3, 6) * 60 trip_duration_sub_activity = np.random.randint(1, 3) * 60 agent.add( Activity( seq=1, act="home", area=location1, loc=location1_loc, start_time=mtdt(0), end_time=mtdt(leaves_home), ) ) # Initiated parameters location_prev = location1 location_prev_loc = location1_loc leave_time = leaves_home # Add random numbers of activities no_of_activities = np.random.randint(1, 5) for i in range(no_of_activities): arrives_primary = leave_time + np.random.randint(10, 90) # minutes # Activity 2. if i < 2: # Start with main activity random_act = np.random.choice(activity) else: random_act = np.random.choice(sub_activity) if random_act == ("work"): leaves_primary = arrives_primary + trip_duration_main_activity else: leaves_primary = arrives_primary + trip_duration_sub_activity # Outbound leg location_next = random_area_sampler() location_next_loc = facility_sampler.sample(location_next, random_act) agent.add( Leg( seq=i + 1, mode=np.random.choice(transport), start_area=location_prev, start_loc=location_prev_loc, end_area=location_next, end_loc=location_next_loc, start_time=mtdt(leave_time), end_time=mtdt(arrives_primary), ) ) agent.add( Activity( seq=i + 2, act=random_act, area=location_next, loc=location_next_loc, start_time=mtdt(arrives_primary), end_time=mtdt(leaves_primary), ) ) # Update parameters leave_time = leaves_primary location_prev = location_next location_prev_loc = location_next_loc # Inbound leg arrives_home = leave_time + np.random.randint(10, 90) # minutes agent.add( Leg( seq=no_of_activities + 1, mode=np.random.choice(transport), start_area=location_next, start_loc=location_next_loc, end_area=location1, end_loc=location1_loc, start_time=mtdt(leave_time), end_time=mtdt(arrives_home), ) ) # Activity agent.add( Activity( seq=no_of_activities + 2, act="home", area=location1, loc=location1_loc, start_time=mtdt(arrives_home), end_time=END_OF_DAY, ) ) return population

In [15]:

# Create 100 agents and check the population statistics
population = generate_agents(20)
print(population.stats)

# Create 100 agents and check the population statistics population = generate_agents(20) print(population.stats)

Using random sample for zone:Lambeth 008B:work
Using random sample for zone:Hammersmith and Fulham 022C:pub
Using random sample for zone:Lambeth 013B:shop
Using random sample for zone:Wandsworth 017A:gym
Using random sample for zone:Wandsworth 015E:pub
Using random sample for zone:Wandsworth 003A:leisure
Using random sample for zone:Wandsworth 002C:medical
Using random sample for zone:Hammersmith and Fulham 013C:work
Using random sample for zone:Lambeth 004D:education
Using random sample for zone:Wandsworth 018B:gym
Using random sample for zone:Hammersmith and Fulham 020E:gym
Using random sample for zone:Kensington and Chelsea 015B:gym
Using random sample for zone:Wandsworth 015D:pub
Using random sample for zone:Kensington and Chelsea 008C:medical
Using random sample for zone:Wandsworth 007D:pub
Using random sample for zone:Wandsworth 001D:gym
Using random sample for zone:Kensington and Chelsea 017E:medical
Using random sample for zone:Kensington and Chelsea 017B:shop
Using random sample for zone:Wandsworth 001C:park
Using random sample for zone:Lambeth 017A:education
Using random sample for zone:Hammersmith and Fulham 017C:shop
Using random sample for zone:Wandsworth 009D:leisure
Using random sample for zone:Westminster 024C:pub
Using random sample for zone:Kensington and Chelsea 015B:pub
Using random sample for zone:Wandsworth 006A:gym
Using random sample for zone:Kensington and Chelsea 021A:gym
Using random sample for zone:Lambeth 013B:work
Using random sample for zone:Lambeth 012D:medical
Using random sample for zone:Kensington and Chelsea 014E:pub
Using random sample for zone:Lambeth 007A:work
Using random sample for zone:Hammersmith and Fulham 016D:park
Using random sample for zone:Hammersmith and Fulham 020B:gym
Using random sample for zone:Kensington and Chelsea 009B:shop
Using random sample for zone:Hammersmith and Fulham 023B:pub
Using random sample for zone:Wandsworth 007B:shop
Using random sample for zone:Kensington and Chelsea 015C:pub
Using random sample for zone:Westminster 021A:gym
Using random sample for zone:Hammersmith and Fulham 020C:park
Using random sample for zone:Wandsworth 006C:shop
Using random sample for zone:Kensington and Chelsea 012E:shop
Using random sample for zone:Kensington and Chelsea 015B:shop
Using random sample for zone:Westminster 023A:gym
Using random sample for zone:Kensington and Chelsea 013A:leisure
Using random sample for zone:Wandsworth 019C:park
Using random sample for zone:Hammersmith and Fulham 016D:pub

{'num_households': 20, 'num_people': 20, 'num_activities': 92, 'num_legs': 72}

In [16]:

population.random_person().print()

population.random_person().print()

Person: agent_14
{'subpopulation': 'high income', 'gender': 'male', 'age': '5 to 10', 'household_zone': 'Westminster 023A', 'household_LSOA': 'E02000982', 'household_LAD': 'Westminster'}
0:	Activity(act:home, location:POINT (528674.754749049 178085.52007832748), time:00:00:00 --> 08:24:00, duration:8:24:00)
1:	Leg(mode:car, area:POINT (528674.754749049 178085.52007832748) --> POINT (524366.7278900618 179488.58531248264), time:08:24:00 --> 09:46:00, duration:1:22:00)
2:	Activity(act:shop, location:POINT (524366.7278900618 179488.58531248264), time:09:46:00 --> 11:46:00, duration:2:00:00)
3:	Leg(mode:bike, area:POINT (524366.7278900618 179488.58531248264) --> POINT (527123.1724066776 177645.15136836463), time:11:46:00 --> 13:01:00, duration:1:15:00)
4:	Activity(act:leisure, location:POINT (527123.1724066776 177645.15136836463), time:13:01:00 --> 15:01:00, duration:2:00:00)
5:	Leg(mode:bus, area:POINT (527123.1724066776 177645.15136836463) --> POINT (526077.4308400992 175824.41748798476), time:15:01:00 --> 16:04:00, duration:1:03:00)
6:	Activity(act:pub, location:POINT (526077.4308400992 175824.41748798476), time:16:04:00 --> 18:04:00, duration:2:00:00)
7:	Leg(mode:rail, area:POINT (526077.4308400992 175824.41748798476) --> POINT (526679.9410896822 175589.59066244372), time:18:04:00 --> 19:31:00, duration:1:27:00)
8:	Activity(act:shop, location:POINT (526679.9410896822 175589.59066244372), time:19:31:00 --> 21:31:00, duration:2:00:00)
9:	Leg(mode:car, area:POINT (526679.9410896822 175589.59066244372) --> POINT (528674.754749049 178085.52007832748), time:21:31:00 --> 22:33:00, duration:1:02:00)
10:	Activity(act:home, location:POINT (528674.754749049 178085.52007832748), time:22:33:00 --> 00:00:00, duration:1:27:00)

In [17]:

population.random_person().attributes

population.random_person().attributes

Out[17]:

{'subpopulation': 'high income',
 'gender': 'male',
 'age': '50 to 59',
 'household_zone': 'Hammersmith and Fulham 010D',
 'household_LSOA': 'E02000381',
 'household_LAD': 'Hammersmith and Fulham'}

Data Visulazation and validation¶

In [18]:

# Validation if it works
population.validate()

# Validation if it works population.validate()

In [19]:

# Print random person activity plan
population.random_person().print()

# Print random person activity plan population.random_person().print()

Person: agent_7
{'subpopulation': 'high income', 'gender': 'female', 'age': '65 to 69', 'household_zone': 'Lambeth 015C', 'household_LSOA': 'E02000632', 'household_LAD': 'Lambeth'}
0:	Activity(act:home, location:POINT (530210.4950919976 175462.92665453235), time:00:00:00 --> 06:59:00, duration:6:59:00)
1:	Leg(mode:ferry, area:POINT (530210.4950919976 175462.92665453235) --> POINT (531028.1389694853 177371.31543672664), time:06:59:00 --> 08:19:00, duration:1:20:00)
2:	Activity(act:shop, location:POINT (531028.1389694853 177371.31543672664), time:08:19:00 --> 09:19:00, duration:1:00:00)
3:	Leg(mode:rail, area:POINT (531028.1389694853 177371.31543672664) --> POINT (530210.4950919976 175462.92665453235), time:09:19:00 --> 10:07:00, duration:0:48:00)
4:	Activity(act:home, location:POINT (530210.4950919976 175462.92665453235), time:10:07:00 --> 00:00:00, duration:13:53:00)

Plot the activities as a 24-hour diary schedules for 5 randomly chosen agents

In [20]:

for _i in range(5):
    p = population.random_person()
    p.plot()

for _i in range(5): p = population.random_person() p.plot()

Plot the frequency with which each of the activity types happens throughout the 24-hour period.

In [21]:

fig = plot_activity_times(population)

fig = plot_activity_times(population)

In [22]:

# Check the duration of trips
durations = duration_counts(population)
durations

# Check the duration of trips durations = duration_counts(population) durations

/Users/bryn.pickering/Repos/arup-group/pam/pam/report/benchmarks.py:128: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = df.groupby(dimensions)[data_fields].agg(aggfunc).fillna(0)
/Users/bryn.pickering/Repos/arup-group/pam/pam/report/benchmarks.py:128: FutureWarning: The provided callable <built-in function sum> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'sum' instead.
  df = df.groupby(dimensions)[data_fields].agg(aggfunc).fillna(0)

Out[22]:

	duration	trips
0	0 to 5 min	0
1	5 to 10 min	0
2	10 to 15 min	3
3	15 to 30 min	5
4	30 to 45 min	11
5	45 to 60 min	10
6	60 to 90 min	43
7	90 to 120 min	0
8	120+ min	0

Now plot a histogram for duration of the trips.

In [23]:

plt.barh(durations["duration"], durations["trips"])
plt.xlabel("Counts")
plt.ylabel("Duration for trips")
plt.title("Duration for different trips")
plt.ylim(ymax="90 to 120 min")

plt.barh(durations["duration"], durations["trips"]) plt.xlabel("Counts") plt.ylabel("Duration for trips") plt.title("Duration for different trips") plt.ylim(ymax="90 to 120 min")

Out[23]:

(-0.8400000000000001, 7.0)

In [24]:

# Check the distance of trips
distances = distance_counts(population)
distances

# Check the distance of trips distances = distance_counts(population) distances

/Users/bryn.pickering/Repos/arup-group/pam/pam/report/benchmarks.py:128: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  df = df.groupby(dimensions)[data_fields].agg(aggfunc).fillna(0)
/Users/bryn.pickering/Repos/arup-group/pam/pam/report/benchmarks.py:128: FutureWarning: The provided callable <built-in function sum> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'sum' instead.
  df = df.groupby(dimensions)[data_fields].agg(aggfunc).fillna(0)

Out[24]:

	distance	trips
0	0 to 1 km	3
1	1 to 5 km	59
2	5 to 10 km	10
3	10 to 25 km	0
4	25 to 50 km	0
5	50 to 100 km	0
6	100 to 200 km	0
7	200+ km	0

Next we plot the distribution of trip distances.

In [25]:

plt.barh(distances["distance"], distances["trips"])
plt.xlabel("Counts")
plt.ylabel("distance, km")
plt.title("distance for different trips")
plt.ylim(ymax="25 to 50 km")

plt.barh(distances["distance"], distances["trips"]) plt.xlabel("Counts") plt.ylabel("distance, km") plt.title("distance for different trips") plt.ylim(ymax="25 to 50 km")

Out[25]:

(-0.79, 4.0)

Read/write data¶

Export intermediate CSV tables of population¶

In [26]:

to_csv(population, dir="outputs", crs="epsg:27700")

to_csv(population, dir="outputs", crs="epsg:27700")

Plot the distribution of activities by type

In [27]:

df_activity = pd.read_csv(os.path.join("outputs", "activities.csv"))
totals = df_activity.activity.value_counts()
plt.barh(totals.index, totals)
plt.title("activities count")

df_activity = pd.read_csv(os.path.join("outputs", "activities.csv")) totals = df_activity.activity.value_counts() plt.barh(totals.index, totals) plt.title("activities count")

Out[27]:

Text(0.5, 1.0, 'activities count')

In [28]:

write_od_matrices(population, path="outputs")
od_matrices = pd.read_csv(
    os.path.join("outputs", "total_od.csv")
)  # we should change this method to be consistent with other - ie return a dataframe
od_matrices["total origins"] = od_matrices.drop("Origin", axis=1).sum(axis=1)
od_matrices

write_od_matrices(population, path="outputs") od_matrices = pd.read_csv( os.path.join("outputs", "total_od.csv") ) # we should change this method to be consistent with other - ie return a dataframe od_matrices["total origins"] = od_matrices.drop("Origin", axis=1).sum(axis=1) od_matrices

Out[28]:

	Origin	Hammersmith and Fulham 010D	Hammersmith and Fulham 013C	Hammersmith and Fulham 015A	Hammersmith and Fulham 016A	Hammersmith and Fulham 016D	Hammersmith and Fulham 017C	Hammersmith and Fulham 020B	Hammersmith and Fulham 020C	Hammersmith and Fulham 020E	...	Wandsworth 017A	Wandsworth 018B	Wandsworth 018D	Wandsworth 019C	Westminster 019A	Westminster 020D	Westminster 021A	Westminster 023A	Westminster 024C	total origins
0	Hammersmith and Fulham 010D	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
1	Hammersmith and Fulham 013C	0	0	0	0	0	0	0	0	0	...	0	0	1	0	0	0	0	0	0	1
2	Hammersmith and Fulham 015A	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	1
3	Hammersmith and Fulham 016A	0	0	0	0	0	0	0	1	0	...	0	0	0	0	0	0	0	0	0	1
4	Hammersmith and Fulham 016D	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	1	0	0	0	2
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
59	Westminster 019A	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1
60	Westminster 020D	0	0	0	0	0	0	1	0	0	...	0	0	0	0	0	0	0	0	0	1
61	Westminster 021A	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	2
62	Westminster 023A	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	2
63	Westminster 024C	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	1

64 rows × 66 columns

Plot the number of trips originating from each LSOA

In [29]:

lsoas_clipped = lsoas_clipped.reset_index()
origins_heat_map = lsoas_clipped.join(od_matrices["total origins"])

fig, ax = plt.subplots(figsize=(18, 10))
origins_heat_map.plot("total origins", legend=True, ax=ax)
ax.set_title("Total Origins")

lsoas_clipped = lsoas_clipped.reset_index() origins_heat_map = lsoas_clipped.join(od_matrices["total origins"]) fig, ax = plt.subplots(figsize=(18, 10)) origins_heat_map.plot("total origins", legend=True, ax=ax) ax.set_title("Total Origins")

/Users/bryn.pickering/mambaforge/envs/pam/lib/python3.11/site-packages/geopandas/plotting.py:732: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(values.dtype):

Out[29]:

Text(0.5, 1.0, 'Total Origins')

Reload Tabular Data¶

We load in the csv files we previously wrote to disk. This replicates a simple synthesis process we might typically use for travel diary survey data.

In [30]:

people = pd.read_csv(os.path.join("outputs", "people.csv")).set_index("pid")
hhs = pd.read_csv(os.path.join("outputs", "households.csv")).set_index("hid")
trips = pd.read_csv(os.path.join("outputs", "legs.csv")).drop(["Unnamed: 0"], axis=1)

trips = trips.rename(columns={"origin activity": "oact", "destination activity": "dact"})
trips.head()

people = pd.read_csv(os.path.join("outputs", "people.csv")).set_index("pid") hhs = pd.read_csv(os.path.join("outputs", "households.csv")).set_index("hid") trips = pd.read_csv(os.path.join("outputs", "legs.csv")).drop(["Unnamed: 0"], axis=1) trips = trips.rename(columns={"origin activity": "oact", "destination activity": "dact"}) trips.head()

Out[30]:

	pid	hid	freq	ozone	dzone	purp	oact	dact	mode	seq	tst	tet	duration
0	agent_0	hh_0	NaN	Lambeth 010B	Lambeth 008B	NaN	home	work	subway	1	1900-01-01 07:45:00	1900-01-01 09:01:00	1:16:00
1	agent_0	hh_0	NaN	Lambeth 008B	Hammersmith and Fulham 022C	NaN	work	pub	bus	2	1900-01-01 13:01:00	1900-01-01 14:27:00	1:26:00
2	agent_0	hh_0	NaN	Hammersmith and Fulham 022C	Lambeth 013B	NaN	pub	shop	car	3	1900-01-01 15:27:00	1900-01-01 16:37:00	1:10:00
3	agent_0	hh_0	NaN	Lambeth 013B	Wandsworth 017A	NaN	shop	gym	car	4	1900-01-01 17:37:00	1900-01-01 18:38:00	1:01:00
4	agent_0	hh_0	NaN	Wandsworth 017A	Lambeth 010B	NaN	gym	home	walk	5	1900-01-01 19:38:00	1900-01-01 20:47:00	1:09:00

In [31]:

population_reloaded = load_travel_diary(trips=trips, persons_attributes=people, hhs_attributes=hhs)

population_reloaded = load_travel_diary(trips=trips, persons_attributes=people, hhs_attributes=hhs)

Using from-to activity parser using 'oact' and 'dact' columns

Plot the activities as a 24-hour diary schedules

In [32]:

population["hh_0"]["agent_0"].plot()

population["hh_0"]["agent_0"].plot()

In [33]:

population_reloaded["hh_0"]["agent_0"].plot()

population_reloaded["hh_0"]["agent_0"].plot()

In [34]:

population == population_reloaded

population == population_reloaded

Out[34]:

False

The populations are not the same because the csv files did not preserve the coordinates that we previously sampled, so we will sample them again. But the reloaded population will be different as for each location a new coordinate is sampled.

Write output to MATSim xml¶

In [35]:

write_matsim(population=population, plans_path=os.path.join("outputs", "population.xml"))

write_matsim(population=population, plans_path=os.path.join("outputs", "population.xml"))