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‎LTS_OSM.py‎

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# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,md,py:light
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# text_representation:
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# extension: .py
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# format_name: light
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# format_version: '1.5'
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# jupytext_version: 1.14.0
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# kernelspec:
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# display_name: Python 3 (ipykernel)
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# language: python
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# name: python3
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# ---
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# # Level of Traffic Stress maps with Open Street Map
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#
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# This notebook calculates Level of Traffic Stress from Open Street Map data. It uses the [osmnx](https://osmnx.readthedocs.io/en/stable/) Python package to download a street network.
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#
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# ### Setup
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#
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# - download a JSON file from Open Street Map that roughly corresponds to the target area. This is used only to get a list of tags to download with the `osmnx` package. The command `wget -nv -O victoriaosm.osm --post-file=victoriaosm.query "http://overpass-api.de/api/interpreter"` downloads a file named victoriaosm.osm using the query in the file victoriaosm.query. This procedure will be simplified in the future.
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import json
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import pandas as pd
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import numpy as np
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import geopandas as gpd
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import osmnx as ox
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from matplotlib import pyplot as plt
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import networkx as nx
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from tqdm import tqdm
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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import matplotlib
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# import lts calculation functions
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from lts_functions import (biking_permitted, is_separated_path, is_bike_lane, parking_present,
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bike_lane_analysis_no_parking, bike_lane_analysis_with_parking, mixed_traffic)
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# ## Extract OSM tags to use in download
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# load the data
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f = open("victoriaosm.osm") # a larger bounding box for the city
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data = json.load(f)
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# +
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# make a dataframe of tags
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dfs = []
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for element in data['elements']:
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if element['type'] != 'way':
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continue
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df = pd.DataFrame.from_dict(element['tags'], orient = 'index')
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dfs.append(df)
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# -
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tags_df = pd.concat(dfs).reset_index()
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tags_df.columns = ["tag", "tagvalue"]
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tag_value_counts = tags_df.value_counts().reset_index() # count all the unique tag and value combinations
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tag_counts = tags_df['tag'].value_counts().reset_index() # count all the unique tags
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# explore the tags that start with 'cycleway'
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tag_counts[tag_counts['index'].str.contains('cycleway')]
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way_tags = list(tag_counts['index']) # all unique tags from the OSM Toronto download
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# add the above list to the global osmnx settings
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ox.settings.useful_tags_way += way_tags
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ox.settings.osm_xml_way_tags = way_tags
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# ### Download data
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# ok this actually looks pretty good. Takes out some of the non-bike options automatically.
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# might want to include 'footway', but could edit the filter manually too.
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osmfilter = ox.downloader._get_osm_filter("bike")
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# keep the footway and construction tags - double the size of the dataset...
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osmfilter2 = '["highway"]["area"!~"yes"]["access"!~"private"]["highway"!~"abandoned|bus_guideway|corridor|elevator|escalator|motor|planned|platform|proposed|raceway|steps"]["bicycle"!~"no"]["service"!~"private"]'
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city = "Victoria"
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province = "British Columbia"
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# get bike network
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G = ox.graph_from_place(
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"%s, %s, Canada" %(city, province),
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retain_all=True,
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truncate_by_edge=True,
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simplify=False,
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custom_filter=osmfilter2,
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)
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# save downloaded graph
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filepath = "./data/%s.graphml" %city
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ox.save_graphml(G, filepath)
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# load graph
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filepath = "./data/%s.graphml" %city
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G = ox.load_graphml(filepath)
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# this is slow for a large area
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fig, ax = ox.plot_graph(G, node_size=0, edge_color="w", edge_linewidth=0.2)
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# you can convert your graph to node and edge GeoPandas GeoDataFrames
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gdf_nodes, gdf_edges = ox.graph_to_gdfs(G)
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# ## Analyze LTS
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#
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# Start with is biking allowed, get edges where biking is not *not* allowed.
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print(gdf_edges.shape)
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gdf_allowed, gdf_not_allowed = biking_permitted(gdf_edges)
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print(gdf_allowed.shape)
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print(gdf_not_allowed.shape)
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# check for separated path
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separated_edges, unseparated_edges = is_separated_path(gdf_allowed)
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# assign separated ways lts = 1
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separated_edges['lts'] = 1
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print(separated_edges.shape)
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print(unseparated_edges.shape)
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to_analyze, no_lane = is_bike_lane(unseparated_edges)
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print(to_analyze.shape)
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print(no_lane.shape)
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parking_detected, parking_not_detected = parking_present(to_analyze)
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print(parking_detected.shape)
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print(parking_not_detected.shape)
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parking_lts = bike_lane_analysis_with_parking(parking_detected)
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no_parking_lts = bike_lane_analysis_no_parking(parking_not_detected)
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# Next, go to the last step - mixed traffic
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lts_no_lane = mixed_traffic(no_lane)
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# final components: lts_no_lane, parking_lts, no_parking_lts, separated_edges
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# these should all add up to gdf_allowed
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print(gdf_allowed.shape)
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lts_no_lane.shape[0] + parking_lts.shape[0] + no_parking_lts.shape[0] + separated_edges.shape[0]
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gdf_not_allowed['lts'] = 0
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all_lts = pd.concat([separated_edges, parking_lts, no_parking_lts, lts_no_lane, gdf_not_allowed])
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# decision rule glossary
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# these are from Bike Ottawa's stressmodel code
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rule_message_dict = {'p2':'Cycling not permitted due to bicycle=\'no\' tag.',
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'p6':'Cycling not permitted due to access=\'no\' tag.',
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'p3':'Cycling not permitted due to highway=\'motorway\' tag.',
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'p4':'Cycling not permitted due to highway=\'motorway_link\' tag.',
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'p7':'Cycling not permitted due to highway=\'proposed\' tag.',
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'p5':'Cycling not permitted. When footway="sidewalk" is present, there must be a bicycle="yes" when the highway is "footway" or "path".',
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's3':'This way is a separated path because highway=\'cycleway\'.',
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's1':'This way is a separated path because highway=\'path\'.',
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's2':'This way is a separated path because highway=\'footway\' but it is not a crossing.',
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's7':'This way is a separated path because cycleway* is defined as \'track\'.',
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's8':'This way is a separated path because cycleway* is defined as \'opposite_track\'.',
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'b1':'LTS is 1 because there is parking present, the maxspeed is less than or equal to 40, highway="residential", and there are 2 lanes or less.',
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'b2':'Increasing LTS to 3 because there are 3 or more lanes and parking present.',
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'b3':'Increasing LTS to 3 because the bike lane width is less than 4.1m and parking present.',
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'b4':'Increasing LTS to 2 because the bike lane width is less than 4.25m and parking present.',
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'b5':'Increasing LTS to 2 because the bike lane width is less than 4.5m, maxspeed is less than 40 on a residential street and parking present.',
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'b6':'Increasing LTS to 2 because the maxspeed is between 41-50 km/h and parking present.',
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'b7':'Increasing LTS to 3 because the maxspeed is between 51-54 km/h and parking present.',
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'b8':'Increasing LTS to 4 because the maxspeed is over 55 km/h and parking present.',
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'b9':'Increasing LTS to 3 because highway is not \'residential\'.',
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'c1':'LTS is 1 because there is no parking, maxspeed is less than or equal to 50, highway=\'residential\', and there are 2 lanes or less.',
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'c3':'Increasing LTS to 3 because there are 3 or more lanes and no parking.',
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'c4':'Increasing LTS to 2 because the bike lane width is less than 1.7 metres and no parking.',
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'c5':'Increasing LTS to 3 because the maxspeed is between 51-64 km/h and no parking.',
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'c6':'Increasing LTS to 4 because the maxspeed is over 65 km/h and no parking.',
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'c7':'Increasing LTS to 3 because highway with bike lane is not \'residential\' and no parking.',
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'm17':'Setting LTS to 1 because motor_vehicle=\'no\'.',
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'm13':'Setting LTS to 1 because highway=\'pedestrian\'.',
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'm14':'Setting LTS to 2 because highway=\'footway\' and footway=\'crossing\'.',
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'm2':'Setting LTS to 2 because highway=\'service\' and service=\'alley\'.',
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'm15':'Setting LTS to 2 because highway=\'track\'.',
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'm3':'Setting LTS to 2 because maxspeed is 50 km/h or less and service is \'parking_aisle\'.',
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'm4':'Setting LTS to 2 because maxspeed is 50 km/h or less and service is \'driveway\'.',
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'm16':'Setting LTS to 2 because maxspeed is less than 35 km/h and highway=\'service\'.',
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'm5':'Setting LTS to 2 because maxspeed is up to 40 km/h, 3 or fewer lanes and highway=\'residential\'.',
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'm6':'Setting LTS to 3 because maxspeed is up to 40 km/h and 3 or fewer lanes on non-residential highway.',
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'm7':'Setting LTS to 3 because maxspeed is up to 40 km/h and 4 or 5 lanes.',
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'm8':'Setting LTS to 4 because maxspeed is up to 40 km/h and the number of lanes is greater than 5.',
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'm9':'Setting LTS to 2 because maxspeed is up to 50 km/h and lanes are 2 or less and highway=\'residential\'.',
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'm10':'Setting LTS to 3 because maxspeed is up to 50 km/h and lanes are 3 or less on non-residential highway.',
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'm11':'Setting LTS to 4 because the number of lanes is greater than 3.',
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'm12':'Setting LTS to 4 because maxspeed is greater than 50 km/h.'}
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simplified_message_dict = {'p2':r'bicycle $=$ "no"',
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'p6':r'access $=$ "no"',
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'p3':r'highway $=$ "motorway"',
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'p4':r'highway $=$ "motorway_link"',
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'p7':r'highway $=$ "proposed"',
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'p5':r'footway $=$ "sidewalk", bicycle$\neq$"yes"',
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's3':r'highway $=$ "cycleway"',
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's1':r'highway $=$" path"',
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's2':r'separated, highway $=$" footway", not a crossing',
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's7':r'cycleway* $=$ "track"',
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's8':r'cycleway* $=$ "opposite_track"',
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'b1':r'bike lane w/ parking, $\leq$ 40 km/h, highway $=$ "residential", $\leq$ 2 lanes',
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'b2':r'bike lane w/ parking, 3 or more lanes',
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'b3':r'bike lane width $<$ 4.1m, parking',
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'b4':r'bike lane width $<$ 4.25m, parking',
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'b5':r'bike lane width $<$ 4.5m, $\leq$ 40 km/h, residential, parking',
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'b6':r'bike lane w/ parking, speed 41-50 km/h',
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'b7':r'bike lane w/ parking, speed 51-54 km/h',
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'b8':r'bike lane w/ parking, speed $>$ 55 km/h',
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'b9':r'bike lane w/ parking, highway $\neq$ "residential"',
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'c1':r'bike lane no parking, $\leq$ 50 km/h, highway $=$ "residential", $\leq$ 2 lanes',
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'c3':r'bike lane no parking, $\leq$ 65 km/h, $\geq$ 3 lanes',
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'c4':r'bike lane width $<$ 1.7m, no parking',
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'c5':r'bike lane no parking, speed 51-64 km/h',
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'c6':r'bike lane no parking, speed $>$ 65 km/h',
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'c7':r'bike lane no parking, highway $\neq$ "residential"',
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'm17':r'mixed traffic, motor_vehicle $=$ "no"',
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'm13':r'mixed traffic, highway $=$ "pedestrian"',
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'm14':r'mixed traffic, highway $=$ "footway", footway $=$ "crossing"',
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'm2':r'mixed traffic, highway $=$ "service", service $=$ "alley"',
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'm15':r'mixed traffic, highway $=$ "track"',
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'm3':r'mixed traffic, speed $\leq$ 50 km/h, service $=$ "parking_aisle"',
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'm4':r'mixed traffic, speed $\leq$ 50 km/h, service $=$ "driveway"',
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'm16':r'mixed traffic, speed $\leq$ 35 km/h, highway $=$ "service"',
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'm5':r'mixed traffic, speed $\leq$ 40 km/h, highway $=$ "residential", $\leq$ 3 lanes',
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'm6':r'mixed traffic, speed $\leq$ 40 km/h, highway $\neq$ "residential", $\leq$ 3 lanes',
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'm7':r'mixed traffic, speed $\leq$ 40 km/h, 4 or 5 lanes',
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'm8':r'mixed traffic, speed $\leq$ 40 km/h, lanes $>$ 5',
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'm9':r'mixed traffic, speed $\leq$ 50 km/h, highway $=$ "residential",$\leq$ 2 lanes',
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'm10':r'mixed traffic, speed $\leq$ 50 km/h, highway $\neq$ "residential", $\leq$ 3 lanes',
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'm11':r'mixed traffic, speed $\leq$ 50 km/h, lanes $>$ 3',
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'm12':r'mixed traffic, speed $>$ 50 km/h'}
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all_lts['message'] = all_lts['rule'].map(rule_message_dict)
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all_lts['short_message'] = all_lts['rule'].map(simplified_message_dict)
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# ## Node LTS
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#
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# Calculate node LTS.
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#
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# - An intersection without either was assigned the highest LTS of its intersecting roads.
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# - Stop signs reduced an otherwise LTS2 intersection to LTS1.
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# - A signalized intersection of two lowstress links was assigned LTS1.
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# - Assigned LTS2 to signalized intersections where a low-stress (LTS1/ 2) link crosses a high-stress (LTS3/4) link.
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gdf_nodes['highway'].value_counts()
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# +
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gdf_nodes['lts'] = np.nan # make lts column
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gdf_nodes['message'] = np.nan # make message column
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for node in tqdm(gdf_nodes.index):
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try:
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edges = all_lts.loc[node]
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except:
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#print("Node not found in edges: %s" %node)
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gdf_nodes.loc[node, 'message'] = "Node not found in edges"
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continue
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control = gdf_nodes.loc[node,'highway'] # if there is a traffic control
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max_lts = edges['lts'].max()
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node_lts = int(max_lts) # set to max of intersecting roads
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message = "Node LTS is max intersecting LTS"
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if node_lts > 2:
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if control == 'traffic_signals':
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node_lts = 2
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message = "LTS 3-4 with traffic signals"
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elif node_lts <= 2:
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if control == 'traffic_signals' or control == 'stop':
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node_lts = 1
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message = "LTS 1-2 with traffic signals or stop"
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gdf_nodes.loc[node,'message'] = message
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gdf_nodes.loc[node,'lts'] = node_lts # assign node lts
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# -
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# ### Save data for plotting
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gdf_nodes.to_csv("data/gdf_nodes_%s.csv" %city)
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all_lts_small = all_lts[['osmid', 'lanes', 'name', 'highway', 'maxspeed', 'geometry', 'length', 'rule', 'lts',
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'lanes_assumed', 'maxspeed_assumed', 'message', 'short_message']]
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all_lts_small.to_csv("data/all_lts_%s.csv" %city)
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# make graph with LTS information
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G_lts = ox.graph_from_gdfs(gdf_nodes, all_lts_small)
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# save LTS graph
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filepath = "data/%s_lts.graphml" %city
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ox.save_graphml(G_lts, filepath)
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‎LTS_plot.py‎

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# ---
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# jupyter:
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# jupytext:
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# formats: ipynb,py:light,md
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# text_representation:
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# extension: .py
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# format_name: light
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# format_version: '1.5'
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# jupytext_version: 1.14.0
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# kernelspec:
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# display_name: Python 3 (ipykernel)
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# language: python
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# name: python3
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# ---
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# # Plotting Level of Traffic Stress
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#
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# This notebook plots the Level of Traffic Stress map calculated in `LTS_OSM'.
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import numpy as np
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import pandas as pd
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import geopandas as gpd
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from matplotlib import pyplot as plt
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city = "Victoria"
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all_lts_df = pd.read_csv("data/all_lts_%s.csv" %city)
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# convert to a geodataframe for plotting
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all_lts = gpd.GeoDataFrame(
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all_lts_df.loc[:, [c for c in all_lts_df.columns if c != "geometry"]],
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geometry=gpd.GeoSeries.from_wkt(all_lts_df["geometry"]),
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crs='wgs84') # projection from graph
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gdf_nodes = pd.read_csv("data/gdf_nodes_%s.csv" %city, index_col=0)
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# +
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# define lts colours for plotting
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conditions = [
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(all_lts['lts'] == 1),
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(all_lts['lts'] == 2),
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(all_lts['lts'] == 3),
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(all_lts['lts'] == 4),
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]
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# create a list of the values we want to assign for each condition
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values = ['g', 'b', 'y', 'r']
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# create a new column and use np.select to assign values to it using our lists as arguments
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all_lts['color'] = np.select(conditions, values)
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# -
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fig, ax = plt.subplots()
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all_lts[all_lts['lts'] > 0].plot(ax = ax, linewidth = 0.1, color = all_lts[all_lts['lts'] > 0]['color'])
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plt.savefig("lts_%s.pdf" %city)
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plt.savefig("lts_%s.png" %city, dpi = 300)
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# ## Plot segments that aren't missing speed and lane info
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has_speed_lanes = all_lts[(~all_lts['maxspeed'].isna())
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& (~all_lts['lanes'].isna())]
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# +
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fig, ax = plt.subplots(figsize = (8,8))
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has_speed_lanes.plot(ax = ax, linewidth = 0.5, color = has_speed_lanes['color'])
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ax.set_xlim(-79.43, -79.37)
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ax.set_ylim(43.645, 43.675)
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ax.set_yticks([])
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ax.set_xticks([])
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plt.savefig("LTS_%s_has_speed_has_lanes.pdf" %city)
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plt.savefig("LTS_%s_has_speed_has_lanes.png" %city, dpi = 300)
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# -
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