Merge branch 'main' into develop

.github/workflows/docs.yml

@@ -0,0 +1,22 @@
+name: Publish documents
+on:
+  push:
+    branches: [main, develop]
+jobs:
+  build:
+    name: Deploy docs to GitHub Pages
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout 
+        uses: actions/checkout@v2
+      - name: Build
+        uses: Tiryoh/actions-mkdocs@v0
+        with:
+          mkdocs_version: 'latest' 
+          requirements: 'docs_requirements.txt'
+          configfile: 'mkdocs.yml'
+      - name: Deploy
+        uses: peaceiris/actions-gh-pages@v3
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./site

.gitignore

@@ -6,4 +6,9 @@ rsm.egg-info
 _version.py
 .DS_Store
 test/data/*
-*.pyc
+notebooks/**/*.omx
+visualizer/simwrapper/data/processed/pipeline.log
+visualizer/simwrapper/data/external
+*.pyc
+sandag_rsm.egg-info/*
+site/*

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "visualizer/Data-Pipeline-Tool"]
+	path = visualizer/Data-Pipeline-Tool
+	url = https://github.com/SANDAG/Data-Pipeline-Tool.git
+        branch = rsm-visualizer

docs/README.md

@@ -0,0 +1,23 @@
+# Instructions for Editing/Managing Documentation
+
+TODO: update/refine as needed
+
+## Getting Started
+
+To get started using Anaconda, do the following:
+
+```
+conda create -n rsm_docs python=3.10
+conda activate rsm_docs
+pip install -r docs_requirements.txt
+```
+
+To create the website locally:
+
+```
+mkdocs serve
+```
+
+See the `mkdocs.yml` file for the configuration.
+
+To create the website on GitHub pages, see the .github/workflows/docs.yml

docs/api.md

@@ -0,0 +1,7 @@
+# Application Programming Interface
+
+::: rsm.zone_agg
+::: rsm.input_agg
+::: rsm.translate
+::: rsm.sampler
+::: rsm.assembler

docs/assessment.md

@@ -0,0 +1,106 @@
+## RSM Configuration
+The team conducted tests using different combinations for the RSM parameters, including the number of RSM zones (1000, 2000), default sampling rates (15%, 25%, 100%), enabling or disabling the intelligent sampler, and choosing the number of global iterations (2 or 3), among other factors. The most significant influence of the number of RSM zones was observed on the runtime of the highway assignment process. Since the highway assignment runtime was already low with 1000 RSM zones, there was no motivation to explore lower RSM zone number. Altering the sampling rate had a greater impact on the runtime of the demand model (CT-RAMP) compared to changing the number of RSM zones. These test runs exhibited varying runtimes depending on the specific configuration. Key metrics at the regional level were analyzed across these different test runs to comprehend the trade-off between improved runtime for RSM and achieving RSM results that are similar to ABM. Based on this, the team collectively determined that for the MVP (Minimum Viable Product) version of the RSM, the "optimal" configuration would be to use 2000 RSM zones, a 25% default sampling rate, the intelligent sampler turned off, and 2 global iterations and this RSM configuration was used to move forward with the overall assessment of the RSM.  
+
+## Calibration
+Aggregating the ABM zones to RSM zones, distorts the walk trips share coming out of the model. With the model configuration (Rapid Zones, Global Iterations, Sample Rate, etc.) for RSM as identified above, tour mode choice calibration was performed to match the RSM mode share to ABM2+ mode share, primarily to match the walk trips. A calibration constant was applied to the tour mode choice UEC to School, Maintenance, Discretionary tour purpose. The mode share for Work and University purpsoe were reasonable, therefore the calibration wasn't applied to those purposes. 
+
+RSM specific constants were added to the Tour Mode Choice UEC (TourModeChoice.xls) to some of the tour purposes. The Walk mode share for the `Maintenance` and `Discretionary` purposes was first adjusted by calibrating and applying n RSM specific constant row to the UEC. Furthermore, in cases where the tour involved escorting for Maintenance or Discretionary purposes, an additional calibration constant was introduced to further adjust the walk mode share for such escort tours. Similarly, a differeent set of constants were added to calibrate the `School` tour purpose. There was no need to calibrate mode choice for any other tour purpose as those were reasonable from RSM. 
+
+Note that a minor calibration will be required for RSM when number of rapid zones are changed.
+
+Here is how the mode share and VMT compares before and after the calibration for RSM. Donor model in the charts below refers to the ABM2+ run. 
+
+![](images\assessment\mode_share_calibrated.PNG)
+
+![](images\assessment\vmt_by_class_calibrated.PNG)
+
+![](images\assessment\total_vmt_calibrated.PNG)
+
+## Base Year Validation
+Here is the table of ABM2+ and RSM outcome comparison after the RSM calibration. The metrics used are some of the regional level key metrics. Volume comparison for the roadway segment on I-5 and I-8 were chosen at random. 
+
+![](images\assessment\validation_performance.PNG)
+
+
+## Runtime Comparison 
+For base year 2016 simulation, below is the runtime comparison of ABM2+ vs RSM. 
+
+![](images\assessment\runtime_performance.PNG)
+
+## Sensitivity Testing
+After validating the RSM for base year with the chosen design configuration, RSM was used to carry out hypothetical planning studies related to some broader use-cases. Model results from both RSM and ABM2+ were compared for each of the sensitivity test to assess the performance of RSM and evaluate if RSM could be a viable tool for such policy planning. 
+
+For each test, a few key metrics from ABM2+ No Action, ABM2+ Action, RSM No Action and RSM Action scenario runs were compared. The goal was to have RSM and ABM2+ show similar sensitivities for action vs no-action. 
+
+### Regional Highway Changes
+
+####Auto Operating Cost - 50% Increase
+
+![](images\assessment\elasticity_aoc_plus_50%.PNG)
+
+
+
+####Auto Operating Cost - 50% Decrease
+
+![](images\assessment\elasticity_aoc_minus_50%.PNG)
+
+
+
+#### Ride Hailing Cost - 50% decrease
+
+![](images\assessment\elasticity_CMPR_RHC_minus_50%.PNG)
+
+
+
+#### Automated Vehicles - 100% Adoption
+
+In SANDAG model, the AV adoption is analyzed by capturing the zero occupancy vehicle movement as simulated in the Household AV Allocation module. For RSM, this AV allocation module is skipped, which is why RSM is not a viable tool for evaluating policies related to automated vehicles. 
+
+![](images\assessment\elasticity_comparison_AV_100%.PNG)
+
+
+
+### Land Use Changes
+
+RSM and ABM2+ shows similar sensitivities for the two tested scenarios with land use change. 
+
+#### Change in land use - Job Housing Balance
+
+![](images\assessment\elasticity_comparison_JOB_HH.PNG)
+
+
+
+####Change in land use - Mixed Land Use 
+
+![](images\assessment\elasticity_comparison_Mixed_LU.PNG)
+
+
+
+### Regional Transit Changes
+
+#### Transit Fare
+
+TODO: Add some text to explain how this test was performed using the study area parameter
+TODO: Add outcome screenshot
+
+#### Transit Frequency
+
+TODO: Add some text to explain how this test was performed using the study area parameter
+TODO: Add outcome screenshot
+
+
+### Local Highway Changes
+
+#### Managed Lane Conversion
+
+TODO: Add some text to explain how this test was performed using the study area parameter
+TODO: Add outcome screenshot
+
+
+
+### Local Transit Changes
+
+#### Rapid 637 BRT
+
+TODO: Add some text to explain how this test was performed using the study area parameter
+TODO: Add outcome screenshot