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2 changes: 1 addition & 1 deletion .nojekyll
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Binary file modified A-Comparative-Illustration-of-Trip--and-Activity-Based-Modeling-Techniques.pdf
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300 changes: 190 additions & 110 deletions qmd/2_transit.html
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Expand Up @@ -224,124 +224,204 @@ <h1 class="title"><span id="sec-transit" class="quarto-section-identifier"><span
</div>
<section id="scenario-creation" class="level2" data-number="5.1">
<h2 data-number="5.1" class="anchored" data-anchor-id="scenario-creation"><span class="header-section-number">5.1</span> Scenario Creation</h2>
<p>In the WFRC model, this change is relatively easy to implement. The headways are stored directly in the input data and are easily modified, and a year-2050 network with increased speeds and additional stations is already built into the model for future-year analysis. The only additional change needed was to turn on the “park and ride” flag in the highway network at each new station. Wasatch Front Regional Council (WFRC)</p>
<p>In the WFRC model, this change is relatively easy to implement. The headways are stored directly in the input data and are easily modified, and a year-2050 network with increased speeds and additional stations is already built into the model for future-year analysis. The only additional change needed was to turn on the “park and ride” flag in the highway network at the node of each new station. Wasatch Front Regional Council (WFRC)</p>
<!-- The WFRC/MAG model contains data on transit headways (including FrontRunner). The BYU team will change this data from 30-minute to 15-minute peak headways and 60-minute to 30-minute off-peak headways. To increase the speed, the team will modify the model to use the predicted speeds after electrification instead of the base year speeds. The 2050 transit networks from the WFRC/MAG model appear to have these changes made, and so the team will use these speed values for the FrontRunner. These networks will be paired with the 2019 socioeconomic scenario for comparison with the base scenario. -->
<p>To implement this scenario in ActivitySim, only updated travel skims are needed. As in the baseline scenario, the transit skims output from the WFRC model’s network assignment in this model scenario are taken directly as inputs to ActivitySim. Because the mode share of transit is relatively low, it is not expected that the highway travel times will be affected very much by this change, and so the highway skims from the baseline scenario are used directly and not updated for this scenario. No other changes to ActivitySim are necessary to model this scenario. <!-- No other changes are needed to ActivitySim for this model scenario. --></p>
<p>To implement this scenario in ActivitySim, only updated travel skims are needed. As in the baseline scenario, the transit skims output from the WFRC model’s network assignment in this model scenario are taken directly as inputs to ActivitySim. Because the mode share of transit is relatively low, it is not expected that the highway travel times will be affected very much by this change, and so the highway skims are taken from the baseline scenario in the WFRC model and not updated for this scenario. No other changes to ActivitySim are necessary to model this scenario. <!-- No other changes are needed to ActivitySim for this model scenario. --></p>
</section>
<section id="scenario-analysis" class="level2" data-number="5.2">
<h2 data-number="5.2" class="anchored" data-anchor-id="scenario-analysis"><span class="header-section-number">5.2</span> Scenario Analysis</h2>
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<td style="text-align: right;">-0.001</td>
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<td style="text-align: right;">48752</td>
<td style="text-align: right;">0.074</td>
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<td style="text-align: left;">auto</td>
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<td style="text-align: right;">0.000</td>
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<td style="text-align: right;">0.000</td>
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<td style="text-align: right;">162979</td>
<td style="text-align: right;">0.000</td>
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<td style="text-align: left;">nhb</td>
<td style="text-align: left;">transit</td>
<td style="text-align: right;">13870</td>
<td style="text-align: right;">13453</td>
<td style="text-align: right;">0.031</td>
<td style="text-align: right;">30688</td>
<td style="text-align: right;">30513</td>
<td style="text-align: right;">0.006</td>
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4 changes: 2 additions & 2 deletions qmd/conclusions.html
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Expand Up @@ -211,8 +211,8 @@ <h1 class="title"><span id="sec-conclusions" class="quarto-section-identifier"><
<section id="computational-resources" class="level2" data-number="7.1">
<h2 data-number="7.1" class="anchored" data-anchor-id="computational-resources"><span class="header-section-number">7.1</span> Computational resources</h2>
<p>As part of our recommendations, we include a discussion of the computational resources used for each model.</p>
<p>All runs of the WFRC model were done on a Windows 10 computer with 2 Intel Xeon Silver 4114 CPUs. The CPUs have a base frequency of 2.2 GHz with a maximum turbo frequency of 3.0 GHz, and 10 cores/20 threads each. The WFRC model is configured for multiprocessing in its destination and mode choice steps, <strong>waiting to head back from Chad on how many processes it actually uses though</strong>. This machine also has 128 GB of RAM installed, <strong>and we need to check how much it actually uses</strong>. There were not significant differences in runtimes between each model scenario, and each scenario had a runtime of 16–17 hours.</p>
<p>Most runs of ActivitySim were done on nodes of the BYU supercomputer. Each node runs Red Hat Enterprise Linux 7.9, and uses a 14-core Intel Broadwell CPU at 2.4 GHz. Each ActivitySim run requested 12 CPU cores and 360 GB of RAM. Running in single-threaded mode (i.e.&nbsp;only one CPU core was utilized), each run took roughly 5 hours to complete, and used nearly all of the 360 GB of RAM available. With multi-threading enabled, however, the runtimes decreased to around an hour per scenario, using 72% of the available CPU time across all 12 cores and 88% of the available RAM.</p>
<p>All runs of the WFRC model were done on a Windows 10 computer with 2 Intel Xeon Silver 4114 CPUs. The CPUs have a base frequency of 2.2 GHz with a maximum turbo frequency of 3.0 GHz, and 10 cores/20 threads each. The WFRC model is configured for multiprocessing in its destination and mode choice steps, and was configured to use 16 threads for our scenario runs. This machine also has 128 GB of RAM installed, <strong>and we need to check how much it actually uses</strong>. There were not significant differences in runtimes between each model scenario, and each scenario had a runtime of 16–17 hours.</p>
<p>Most runs of ActivitySim were done on nodes of the BYU supercomputer. Each node runs Red Hat Enterprise Linux 7.9, and uses an AMD EPYC 7763 CPU at 2.45 GHz. Each ActivitySim run requested 12 CPU cores and 360 GB of RAM. Running in single-threaded mode (i.e.&nbsp;only one CPU core was utilized), each run took roughly 5 hours to complete, and used nearly all of the 360 GB of RAM available. With multi-threading enabled, however, the runtimes decreased to around an hour per scenario, using 72% of the available CPU time across all 12 cores and 88% of the available RAM.</p>
<p>This is a huge difference in runtime between the two models, though crucially ActivitySim had 3 times as much RAM available for use. ActivitySim offers “chunking” options <span class="citation" data-cites="asim-chunking">(<a href="references.html#ref-asim-chunking" role="doc-biblioref"><strong>asim-chunking?</strong></a>)</span>, where large tables are loaded into RAM in chunks rather than all at once. This can dramatically reduce the amount of RAM required to run an ActivitySim scenario, at the expense of increased runtimes. For comparison, we ran the baseline scenario in ActivitySim on the same computer used for the WFRC model scenarios, with chunking enabled to account for the amount of RAM available. With multi-threading set to use <strong>36 of the 40 available (should probably match what the WFRC model actually uses if not the max amount</strong> threads, the baseline ActivitySim scenario ran in XXX hours <strong>(still need to do this)</strong>.</p>
</section>
<section id="time-spent" class="level2" data-number="7.2">
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