Overview

Green Paths 2.0 finds environmental exposure optimized paths with flexibility for spatial and temporal context and data sources. It can be used for single trip one-to-one path finding, or large mass calculations of one-to-many or many-to-many path findings. Its usage requires at least moderate expertise in filling the configurations correctly, and being able to interpret the results.

Background

Green Paths 2 is the next iteration, and heavily influenced on the previous version of Green Paths developed by Joose Helle for his Geography masters thesis and for Digital Geography Lab, University of Helsinki. See also the Green Paths Web GUI.

Where in Green Paths (1.0) worked in Helsinki Metropolitan Region with air quality, greenery and noise data, Green Paths 2.0 can be transfered to different cities, it supports multiple different data sets and formats and is relatively computationally powerful due to using the R5 software package as its routing engine.

Green Paths 2.0 Framework

Green Paths 2.0 uses spatial data in multiple formats (vector, raster) to add exposure costs to the OSM street network segments.

GP2 consists of three main modules (or pipelines):

  • preprocessing

  • routing

  • exposure analysis

These modules separate the logic of calculating environmental exposure for OSM segments, routing with environmental weights, and analysing environmental exposures from the selected routes.

Hint

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Module results

preprocessing module

Preprocessing module will populate the sqlite3 db with values: raw exposure, normalized exposure, lenght, geometry. Where each row describes a single segment. The OSM PBF is segmented to create more representative segments vs. OSM ways (which can span across multiple intersections / nodes) Preprocessing module handles conversion of CRS to the project CRS. Preprocessing uses point segmentation for segments and overlay analysis to see what is the exposure values and then averages the mean value from all the points and assigns that to the segment.

Optionally preprocessing also saves the exposure rasters according to the user configuration value.

routing module

Routing module uses the normalized exposures from the sqlite3 segment_store to input them to r5py and forward to r5. The values in addition to the weigth / sensitivity is then used to weight the segments in the network. The routing saves the paths and the actual travel times to sqlite3 to tables routing_results and travel_times.

analysing module

Analysing module then combines the results from routing paths and travel times with segment store values by using the (osm segment newly created) OSM_ID’s to fetch the exposure values. The module calculates time weighted averages and sum, min, max, and optionally configured cumulative values (seconds spend in each range of exposure value, exposure range is key and seconds is value).

The module then produces results as gpkg if keep_geometries is defined as true in user configurations, otherwise it produces csv.

User configurations

Green Paths 2.0 relies heavily on user configurations in order to work. All of the necessary tool configurations are filled in the YAML.

The user configurations: config.yaml can befound in /user/ directory in GP2 root directory. In case a custom path for user configuration needs to be used, it can be passed in as argument, see cli user interface

Attention

When running without -c (config path) argument, the config used will always be user/config.yaml

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Routing engine

The segment exposure values are then used to route healthier paths with the by using Conveyal’s R5: Rapid Realistic Routing on Real-world and Reimagined networks via python interface of r5py: Rapid Realistic Routing with R5 in Python. The routing returns lists of OSM ID’s taken during routing, and uses those OSM ID’s to combine and calculate segment exposure values (combined with Preprocessing raw exposure values).

GP2 is using patched forks from Digital Geography Lab’s r5 R5 (branch GP2) and r5py modified fork R5PY_GP2.

Output Results

The output results can befound in /results_output directory in the GP2 root directory. Also the resulting exposure rasters will be located in this folder.

Geography Masters Thesis (completed June 2024)

Green Paths 2.0 was part of the Master’s thesis by Roope Heinonen made for the Digital Geography Lab in the University of Helsinki.

See the detailed documentation for the software:

Heinonen, R., 2024. Green Paths 2.0: Developing a Transferable Multi-Objective Environmental Exposure Optimizing Route Planning tool for Active Travel (MSc thesis). University of Helsinki, Helsinki. (https://helda.helsinki.fi/items/5b77f6c3-2d2c-455f-bb8c-528b0ac136d8)

The Green Paths 2.0 affiliated projects are: Urban Air Quality 2.0 (UAQ2.0) and GREENTRAVEL. GP2 was funded by UAQ2.0.