stplanpy.dist module

The functions in this module can be used to apply different distributions to flow data.

stplanpy.dist.ebike(fd: pandas.core.frame.DataFrame, column_names=['all', 'distance', 'gradient']) → pandas.core.series.Series

Apply the “e-bike” scenario.

This function returns the total number of expected bicyclists under the “e-bike” scenario. Under this scenario all people aquire e-bikes and ride them as much as people in Netherlands do, taking into account both hilliness and trip distance. The total number of expected bicyclists is equal to: \(\text{fd["all"]} \cdot p_{\text{cycle}}\), where \(\text{fd["all"]}\) is the total number of people traveling between an origin and a destination using all modes of transportation, and \(p_{\text{cycle}}\) is the proportion of expected cyclists 1:

\[\text{logit}(p_{\text{cycle}}) = - 1.436 + 0.05901 \sqrt{d} - 6.1546 \cdot 10^{-4} d + 7.9413 \cdot 10^{-9} d^2 - 8.98 \nabla - 0.1624 \sqrt{d} \; \nabla + 9.394 \cdot 10^{-4} d \; \nabla\]

Here \(d\) is the distance in meters and \(\nabla\) is the gradient \(h/d\), with \(h\) the elevation difference in meters between the origin and the destination. Only distances smaller than 30 km are considered.

Parameters

column_names (list of str, defaults to ["all", "distance", "gradient"]) – Names of the input columns that this function operates on. The “all” column contains the total number of people traveling between an origin and a destination using all modes of transportation, the “distance” column contains the distance between the origin and the destination, and the “gradient” column contains the gradient as defined above.

Returns

Series with the total number of expected bicyclists under the “e-bike” scenario.

Return type

pandas.Series

See also

go_dutch, od_lines, distances, gradient

Examples

import pandas as pd
import geopandas as gpd
from shapely import wkt
from stplanpy import od
from stplanpy import dist

# Define two origin-destination lines
df = pd.DataFrame({
    "all": [5, 10],
    "geometry": [
    "LINESTRING(-11785727.431 4453819.337, -11782276.436 4448452.023)",
    "LINESTRING(-11785787.392 4450797.573, -11787086.209 4449884.472)"]})

# Convert to WTK
df["geometry"] = gpd.GeoSeries.from_wkt(df["geometry"])

# Create GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:6933")

# compute distances and set gradient to zero
gdf["distance"] = gdf.distances()
gdf["gradient"] = 0.0

# Compute e-bike scenario
gdf["ebike"] = gdf.ebike()

stplanpy.dist.go_dutch(fd: pandas.core.frame.DataFrame, column_names=['all', 'distance', 'gradient']) → pandas.core.series.Series

Apply the “go Dutch” scenario.

This function returns the total number of expected bicyclists under the “go Dutch” scenario. Under this scenario people cycle as much as people in Netherlands do, taking into account both hilliness and trip distance. The total number of expected bicyclists is equal to: \(\text{fd["all"]} \cdot p_{\text{cycle}}\), where \(\text{fd["all"]}\) is the total number of people traveling between an origin and a destination using all modes of transportation, and \(p_{\text{cycle}}\) is the proportion of expected cyclists 1:

\[\text{logit}(p_{\text{cycle}}) = - 1.436 + 0.05901 \sqrt{d} - 6.7256 \cdot 10^{-4} d + 8.05 \cdot 10^{-9} d^2 - 27.10 \nabla - 0.1624 \sqrt{d} \; \nabla + 9.394 \cdot 10^{-4} d \; \nabla\]

Here \(d\) is the distance in meters and \(\nabla\) is the gradient \(h/d\), with \(h\) the elevation difference in meters between the origin and the destination. Only distances smaller than 30 km are considered.

Parameters

column_names (list of str, defaults to ["all", "distance", "gradient"]) – Names of the input columns that this function operates on. The “all” column contains the total number of people traveling between an origin and a destination using all modes of transportation, the “distance” column contains the distance between the origin and the destination, and the “gradient” column contains the gradient as defined above.

Returns

Series with the total number of expected bicyclists under the “go Dutch” scenario.

Return type

pandas.Series

See also

ebike, od_lines, distances, gradient

Examples

import pandas as pd
import geopandas as gpd
from shapely import wkt
from stplanpy import od
from stplanpy import dist

# Define two origin-destination lines
df = pd.DataFrame({
    "all": [5, 10],
    "geometry": [
    "LINESTRING(-11785727.431 4453819.337, -11782276.436 4448452.023)",
    "LINESTRING(-11785787.392 4450797.573, -11787086.209 4449884.472)"]})

# Convert to WTK
df["geometry"] = gpd.GeoSeries.from_wkt(df["geometry"])

# Create GeoDataFrame
gdf = gpd.GeoDataFrame(df, geometry='geometry', crs="EPSG:6933")

# compute distances and set gradient to zero
gdf["distance"] = gdf.distances()
gdf["gradient"] = 0.0

# Compute go_dutch scenario
gdf["go_dutch"] = gdf.go_dutch()

References

1(1,2)

Robin Lovelace, Anna Goodman, Rachel Aldred, et al., “The Propensity to Cycle Tool: An open source online system for sustainable transport planning”, Journal of transport and land use, vol 10, no 1, pp 505-528, 2017, url:https://www.jstor.org/stable/26211742