From 32243528e06a0d5cd3c07f34909c264fde1849a1 Mon Sep 17 00:00:00 2001 From: Hayden Atchley Date: Tue, 31 Oct 2023 18:43:15 -0600 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- 1_land_use.html | 11 +- 2_transit.html | 7 +- 3_wfh.html | 7 +- discussion.html | 7 +- index.html | 7 +- intro.html | 7 +- lit-review.html | 17 +- methods.html | 462 +++++++++++++++--- .../figure-html/fig-inc-groups-map-1.png | Bin 0 -> 524408 bytes methods_files/figure-html/fig-tlfd-comp-1.png | Bin 0 -> 213101 bytes .../figure-html/fig-totpop-comp-1.png | Bin 0 -> 435754 bytes .../aggregate.png | Bin 35863 -> 37117 bytes .../synthetic.png | Bin 47314 -> 48419 bytes output/example_flowchart_comparison/tbm.png | Bin 238872 -> 238862 bytes output/example_flowchart_comparison/tour.png | Bin 46969 -> 47007 bytes output/example_flowchart_comparison/trip.png | Bin 53580 -> 53565 bytes references.html | 11 +- search.json | 29 +- site_libs/bootstrap/bootstrap.min.css | 2 +- site_libs/kePrint-0.0.1/kePrint.js | 8 + site_libs/lightable-0.0.1/lightable.css | 272 +++++++++++ 22 files changed, 745 insertions(+), 104 deletions(-) create mode 100644 methods_files/figure-html/fig-inc-groups-map-1.png create mode 100644 methods_files/figure-html/fig-tlfd-comp-1.png create mode 100644 methods_files/figure-html/fig-totpop-comp-1.png create mode 100644 site_libs/kePrint-0.0.1/kePrint.js create mode 100644 site_libs/lightable-0.0.1/lightable.css diff --git a/.nojekyll b/.nojekyll index 38db0c4..1d694d5 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -52f54378 \ No newline at end of file +0f82fa63 \ No newline at end of file diff --git a/1_land_use.html b/1_land_use.html index 9faae4d..7df3e1a 100644 --- a/1_land_use.html +++ b/1_land_use.html @@ -2,7 +2,7 @@ - + @@ -57,7 +57,8 @@ "search-more-matches-text": "more matches in this document", "search-clear-button-title": "Clear", "search-detached-cancel-button-title": "Cancel", - "search-submit-button-title": "Submit" + "search-submit-button-title": "Submit", + "search-label": "Search" } } @@ -76,7 +77,7 @@ - @@ -187,7 +188,7 @@

4  Figure 4.1). The 600-acre site will be developed into a multi-use residential and commercial area, with an emphasis on walkability and transit. A bus rapid transit (BRT) line is planned to run through the development to the Draper FrontRunner station, and across I-15 to Lehi, Utah. This scenario is to model this development and its effect on transportation demand.

To model this scenario in the trip-based model, the only thing that needed to be done was update the socioeconomic data to reflect the land use changes. Using the information made publicly available by Point of the Mountain State Land Authority (state_land_authority_framework_2021?), the increase in population and employment was estimated as shown in Table 4.1. It is important to note that the development spans five transportation analysis zone (TAZ)s (see ?fig-prison-new-transit), and so the socioeconomic data are spread out across them consistent with the development plan (cite?). To add this information to the model, the csv file containing the socioeconomic data for each zone needed to be adjusted. This involved simply adding the new information to each of the five rows that represented the zones in the new development.

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Bills and Walker (2017) used the 2000 Bay Area Travel Survey to create a synthetic population and compare the effects that certain scenarios had on high income and low income populations. With a 20% reduction in travel cost, they found that high income workers benefited more than low income workers. They did similar comparisons for scenarios involving reduced travel times for different mode choices and saw the effects each scenario had on the high and low income workers. These types of analysis, which are not possible with aggregate population data (this is actually possible; a lot of trip-based models segment high/low income), can be very valuable in transportation planning and policy making.

It is important to note that while many connect them only with ABMs, synthetic populations can be used in running trip-based models as well. Trip-based models using a synthetic population—often called trip-based microsimulation models—do exist (see Walker (2005) and Moeckel et al. (2020)), but these are relatively rare.

Figure 2.1 gives a visualization of an example “information pipeline” for a trip-based model using aggregate data and an ABM using a synthetic population. In the aggregate data model, it is impossible to know which trips are made by e.g. 2-worker, 1-vehicle, low-income households; it only describes which trips are made by households with fewer vehicles than workers. With a synthetic population, however, individuals are being modeled, and so each trip can be traced to a specific person. All information is known at each point in the model regardless of which data is used in previous steps.

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(b) Activity-based
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ABMs, on the other hand, model tours rather than trips. This attempts to create consistency in trip origins/destinations, mode choice, and time of day: since each trip is a part of a “chain” (tour), the trips within a tour are dependent on each other (Rasouli and Timmermans 2014). The open-source ABM ActivitySim (Association of Metropolitan Planning Organizations 2023), for example, has a tour-scheduling model that determines the number of “mandatory” (work, school, etc.) and “discretionary” tours each individual will make, and performs tour-level mode and destination choice for each tour. After the tour-level decisions are made, trip-level mode/destination choice is done for each trip in the tour, including the possible addition of subtours (see Vovsha, Bradley, and Bowman (2005), fig. 18.1).

Figures 2.2 and 2.3 show an example of the trips assigned to a network in the various model forms. Figure 2.2 depicts network assignment in a typical trip-based model where the total number of trips between each zone is given. With these results, the mode and purpose of each trip is known, but, with aggregate data, there is no way of telling who made which trips other than the segmentation in the previous steps (see Figure 2.1 (a)). It is also not possible to construct a coherent daily list of trips for individuals.

Figure 2.3, on the other hand, depicts visual representations of an individual’s travel made possible by the use of a synthetic population. Figure 2.3 (a) depicts the trip list that could be given for an individual in a trip-based microsimulation model. Though each individual’s trips are known, there is no guarantee of consistency between trips. For example, it could predict that the individual takes transit to work but then drives home or that the individual makes two recreational trips without ever making a return trip. The activity-based approach, depicted in Figure 2.3 (b), attempts to add this consistency by modeling tours, and only generating trips consistent with each tour.

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Table of contents

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This section lists our methodology.

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We will compare models…

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We will forecast several scenarios.

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3.1 Models to Use

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We are using the Wasatch Front Regional Council (WFRC) regional travel demand model as a representative trip-based model. This model is implemented in the CUBE software by Bentley (https://www.bentley.com/software/cube/, fix into real citation), and is currently used by WFRC for modeling travel in the Salt Lake City, Utah area. WFRC provided the model directly, including land use forecasts and the current long-range transportation plan. The model is taken mostly as-is, though some changes were made to better accommodate the different scenarios. These modifications are noted in more detail in their respective sections.

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Overview of WFRC model (inputs, outputs, etc)

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The open-source ActivitySim (cite?) is used as our representative activity-based model (ABM). ActivitySim was developed…. Many agencies currently use a travel demand model based on ActivitySim in some form (cite?). another sentence.

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Overview of asim model (inputs, outputs, etc.) (also basic flowchart, tours/trips/etc)

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We used PopulationSim (cite?) to create the synthetic population to use in ActivitySim. PopulationSim requires two main categories of inputs: a seed and controls. The seed comes from XXXXX, and contains a sample of actual (anonymized) individuals and households in the YYYYY region. PopulationSim assigns copies of these seeds to the area in a way that complies with the controls. The controls contain targets for the synthetic population in categories such as number of households, et al. For this we used a combination of the transportation analysis zone (TAZ)-level socioeconomic data present in the WFRC model and Census tract-level data.

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3.2 Initial Model Comparison

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In order to reasonably compare the two models, the initial “baseline” scenarios in each should have relatively similar results. Certainly this applies to the predicted trips by purpose, mode, length, etc., but we also want to ensure the synthetic population generated by PopulationSim matches the socioeconomic data used in the WFRC model. Though many controls such as household size and income group were specified from the Census at the tract level, the WFRC socioeconomic data contains this information as well.

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Besides the different data sources, not all controls were weighted equally. Number of households, for example, was given at the TAZ level from the WFRC data and weighted very heavily, so there is no difference in number of households in each TAZ between the synthetic population and the WFRC data. Household size, on the other hand, is a control from the Census data, and so there is some discrepancy between the synthetic population and the WFRC data (see figure XXX). However, these discrepancies are generally relatively minor, especially in more populated areas.

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Another important comparison to make is of household income. (compare by group because of top-coding)

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We are getting the input zonal SE data form the WFRC model and using that as targets for our synthetic population generation (via PopulationSim).

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Enumerate and discuss model inputs/outputs (diagram?)

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3.2.1 Calibration check

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We want to check if the ABM gives similar results to the WFRC model in the base scenario. If not, then we can’t really compare them. They don’t have to match exactly, but should be close.

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3.2.1.1 synth pop

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Show differences between the actual zonal SE data and the synthetic population data. HH is the same since it was heavily weighted, but check some other things too, like avg income.

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746 zones in the WFRC input data are listed as having no households. Many of these make sense (Utah Lake, etc.). Some in more urban areas may also make sense (commercial zoning, etc), but it’s possible some are erroneous. In any case, the synthetic population matches these targets, as we want the models to perform similarly to be able to make comparisons.

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Many variables such as vehicle ownership were not given at the TAZ level in the input data (were they given anywhere else?). This data is present in the synthetic population, but we can’t compare it (unless we find that data somewhere else).

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Overall we are pretty good; the vast majority of zones are within a doubling of the WFRC data in both persons and income. There are some outliers, but they are fringe zones that are less populated anyway.

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3.2.1.2 TLFD

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Compare TLFD, hopefully similar.

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They are similar! NOTE: The WFRC model multiplies trips by 100 in mode choice because there are a lot of fractional trips that would be lost to rounding otherwise. We don’t need to do that here (R can handle more than 2 decimal places), so we divided the trips by 100 to get the actual amount.

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In order to compare trip-based and activity-based models, this research takes a specific model from both types to be representative. The two models that this research compares are the Wasatch Front Regional Council (WFRC) travel demand model (trip-based) and ActivitySim (activity-based).

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3.1 WFRC Model

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The WFRC model is implemented in the CUBE software by Bentley (Bentley Systems n.d.), and is currently used by WFRC for modeling travel in the Salt Lake City, Utah area. WFRC provided the model directly, including land use forecasts and the current long-range transportation plan. The model is taken mostly as-is, though some changes were made to better accommodate the different scenarios. These modifications are noted in more detail in their respective sections.

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The WFRC model, like many trip-based models, requires the following inputs: - Land use data. This includes information about population, employment, and socioeconomic variables such as income, delineated by transportation analysis zone (TAZ). In our case this was provided by WFRC directly, as an output of their land use forecasting model(s). - Travel skims. This details travel time, cost, etc. between each origin-destination pair of TAZs. The WFRC model uses an iterative process of assigning volumes to the transportation network and recalculating the skims, which are used in the mode and destination choice model steps. - Transportation network. This includes highway, transit, etc. networks which connect the TAZs to each other. These networks contain information such as link speed and capacity. Though the WFRC model assigns travel volumes to the network, this research does not compare the model’s network assignment results. However, the network volumes are still used to calculate the travel skims. - Model constants and coefficients. Many model steps, including mode choice, require calibration constants and coefficients. These are taken directly from the WFRC model without modification.

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The model is comprised of 5 main steps: household disaggregation, trip generation, trip distribution, mode choice, and network assignment. Each of these steps has several sub-steps, and there are additional pre- and post-processing steps, but these five steps are the core of the model. The household disaggregation model uses the land use and socioeconomic data (containing average household sizes, etc.) and estimates the number of households by type, meaning by number of residents and number of workers. An additional part of this step is a vehicle ownership model, which further disaggregates the households by number of owned vehicles. The remaining four steps proceed similarly to any other trip-based model, estimating trips from each household type by purpose, time of day, origin/destination, and mode choice, and assigning these trips to the network(s).

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The final output(s) of the WFRC model include network volumes and performance measures. This includes loaded the network skims, which can then be used in subsequent iterations of the destination and mode choice models. Additionally, the model outputs intermediate results after each main model step. This includes disaggregated household data, trip generation estimates, and origin-destination matrices counting trips by purpose, mode, and time of day.

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3.2 ActivitySim

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3.3 Initial Model Comparison

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In order to reasonably compare the two models, the initial “baseline” scenarios in each should have relatively similar results. Certainly this applies to the predicted trips by purpose, mode, length, etc., but it is also critical to ensure the synthetic population generated by PopulationSim matches the socioeconomic data used in the WFRC/MAG model. Though many controls such as household size and income are specified from the Census at the tract level, the WFRC socioeconomic data contains this information as well. Ideally, then, variables such as number of households per TAZ and TAZ median income should be similar between the two sources.

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The data from both the Census and the WFRC/MAG model become geography-specific control totals which PopulationSim tries to match, with different weights assigned to each control. Number of households, for example, was given at the TAZ level from the WFRC data and weighted very heavily, so there is no difference in number of households in each TAZ between the synthetic population and the WFRC data. Total population, on the other hand, is a control from the Census data, and is in fact specified at the region geography level, and so there is some discrepancy between the synthetic population and the WFRC data (see Figure 3.1). However, these discrepancies are generally relatively minor, especially in more populated areas.

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Figure 3.1: Comparison of population by TAZ between WFRC data and PopulationSim synthetic population.
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Another important comparison to make is of household income, as this can significantly affect travel behavior. Instead of simply comparing average household income by TAZ, it is more useful to compare the number of households in each binned income group. Both the WFRC and ActivitySim models use income groups as a variable in their model steps rather than the actual income. The income groups for the WFRC model are given in Table 3.1.

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Table 3.1: income groups
Income GroupIncome Range
1≤ $45,000
2$45,000–$75,000
3$75,000–$125,000
4≥ $125,000
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Figure 3.2 shows a comparison of the number of households in each income group between the WFRC household disaggregation model and the synthetic population generated by PopulationSim. There is a definite trend of the synthetic population to over-predict low-income households and over-predict high-income households compared to the WFRC disaggregation model. However, especially since this research is focused more on a methodological comparison between the two models rather than a comparison of results, the discrepancies are not too severe to be concerned about.

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Figure 3.2: income groups
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Table 3.2: mode split
PurposeModeActivitySim predicted tripsWFRC/MAG predicted trips% Error% Error (scaled to WFRC/MAG predicted trips)
AllAll74419188737729-14.8%0.0%
AllAuto65153797905927-17.6%-3.2%
AllNon-motorized7684637322504.9%23.2%
AllTransit1580769955158.8%86.4%
Home-based OtherAll36960564641724-20.4%-6.5%
Home-based OtherAuto30927404094887-24.5%-11.3%
Home-based OtherNon-motorized5314975094914.3%22.5%
Home-based OtherTransit718193734692.3%125.8%
Home-based WorkAll175950117115782.8%20.7%
Home-based WorkAuto161744915864132.0%19.7%
Home-based WorkNon-motorized854307641311.8%31.3%
Home-based WorkTransit566224875216.1%36.4%
Non–Home-basedAll19863612384427-16.7%-2.2%
Non–Home-basedAuto18051902224628-18.9%-4.7%
Non–Home-basedNon-motorized1515361463463.5%21.6%
Non–Home-basedTransit2963513453120.3%158.6%
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+kernel, : Selecting bandwidth *not* using 'weights'
+
+
+
+
+

+
Figure 3.3: tlfd comparison
+
+
+
+
+ + + + -
-

3.3 policy scenarios

+
+

3.4 policy scenarios

We will model the following scenarios: - Land use - Transit - WFH

-
-

3.4 Computational resources?

+
+

3.5 Computational resources?

Describe BEAST and the BYU Supercomputer.

+
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+95,7 @@ -

@@ -208,6 +209,10 @@

References

Association of Metropolitan Planning Organizations. 2023. “ActivitySim.” ActivitySim. https://activitysim.github.io/.
+
+Bentley Systems. n.d. “CUBE Modeling +Software.” Accessed October 3, 2023. https://www.bentley.com/software/cube/. +
Bick, Alexander, Adam Blandin, and Karel Mertens. 2021. “Work from Home Before and After the diff --git a/search.json b/search.json index 3741c08..fe78aa6 100644 --- a/search.json +++ b/search.json @@ -35,32 +35,39 @@ "text": "2.4 Research Gap\nAlthough there has been much research on ABMs and their theoretical advantages, practical comparisons of the model frameworks have been limited. It is often taken as given that ABMs are unilaterally superior to traditional trip-based models due to their better theoretical foundation, but it is not clear if that better foundation always yields better results in terms of analytical flexibility or policy outcomes. Ferdous et al. (2012) compared the trip- and activity-based model frameworks of the Mid-Ohio Regional Planning Commission and found that the ABM performed slightly better at the region level, but about the same at the project level. This is not an overwhelming victory for ABMs, and so based on this an agency may reasonably decide that the increase in data, computational, training, and potentially financial requirements outweigh the potential gains of switching to an ABM.\nFerdous et al. (2012), however, mainly compared the accuracy of the two frameworks, but did not address the methodological differences between them. What types of data collection/synthesis are needed for each model type? Are there analyses that can only be done through (or that are made easier by) one of the model types? What would an agency need in order to transition from a trip-based model to an ABM? Are certain types of scenarios suited to one model type? Though some of these questions have been discussed (see e.g. Lemp, McWethy, and Kockelman 2007), a holistic methodological comparison is lacking. Additionally, the answers in the current literature are mainly theoretical, with little use to an agency considering the transition.\nThis research aims to answer these questions by providing a side-by-side comparison of a potential trip-based and activity-based modeling methodology. Several “proposed development” scenarios are run in each model, and the strengths and weaknesses of each approach are compared. It is important to note that this paper is not focused on model accuracy, as in any model type this can be adjusted dramatically through calibration efforts. Rather, the focus is on the methodological differences between the approaches, and the types of analyses that can be done with each model type.\n\n\n\n\nAssociation of Metropolitan Planning Organizations. 2023. “ActivitySim.” ActivitySim. https://activitysim.github.io/.\n\n\nBills, Tierra S., and Joan L. Walker. 2017. “Looking Beyond the Mean for Equity Analysis: Examining Distributional Impacts of Transportation Improvements.” Transport Policy 54 (February): 61–69. https://doi.org/10.1016/j.tranpol.2016.08.003.\n\n\nBowman, John L. 1998. “The Day Activity Schedule Approach to Travel Demand Analysis.” Ph.D. Dissertation, Cambridge, MA: Massachusetts Institute of Technology. https://dspace.mit.edu/bitstream/handle/1721.1/16731/44704881-MIT.pdf?sequence=2.\n\n\nCastiglione, Joe, Rachel Hiatt, Tilly Chang, and Billy Charlton. 2006. “Application of Travel Demand Microsimulation Model for Equity Analysis.” Transportation Research Record: Journal of the Transportation Research Board 1977 (1): 35–42. https://doi.org/10.1177/0361198106197700105.\n\n\nChicago Area Transportation Study. 1959. Final Report In Three Parts. Vol. 1–3. Printed by authority of the State of Illinois. http://archive.org/details/chicagoareatrans01chic.\n\n\nDong, Xiaojing, Moshe E. Ben-Akiva, John L. Bowman, and Joan L. Walker. 2006. “Moving from Trip-Based to Activity-Based Measures of Accessibility.” Transportation Research Part A: Policy and Practice 40 (2): 163–80. https://doi.org/10.1016/j.tra.2005.05.002.\n\n\nFerdous, Nazneen, Lakshmi Vana, John L. Bowman, Ram M. Pendyala, Gregory Giaimo, Chandra R. Bhat, David Schmitt, Mark Bradley, and Rebekah Anderson. 2012. “Comparison of Four-Step Versus Tour-Based Models for Prediction of Travel Behavior Before and After Transportation System Changes.” Transportation Research Record 2303 (1): 46–60. https://doi.org/10.3141/2303-06.\n\n\nJones, Peter M. 1979. “New Approaches to Understanding Travel Behaviour: The Human Activity Approach.” In Behavioural Travel Modelling. Routledge.\n\n\nLemp, Jason D., Laura B. McWethy, and Kara M. Kockelman. 2007. “From Aggregate Methods to Microsimulation: Assessing Benefits of Microscopic Activity-Based Models of Travel Demand.” Transportation Research Record: Journal of the Transportation Research Board 1994 (1): 80–88. https://doi.org/10.3141/1994-11.\n\n\nMcNally, Michael G. 2007. “The Four-Step Model.” In Handbook of Transport Modelling, edited by David A. Hensher and Kenneth J. Button, 1:35–53. Emerald Group Publishing Limited. https://doi.org/10.1108/9780857245670-003.\n\n\nMiller, Eric. 2023. “The Current State of Activity-Based Travel Demand Modelling and Some Possible Next Steps.” Transport Reviews 0 (0): 1–6. https://doi.org/10.1080/01441647.2023.2198458.\n\n\nMoeckel, Rolf, Nico Kuehnel, Carlos Llorca, Ana Tsui Moreno, and Hema Rayaprolu. 2020. “Agent-Based Simulation to Improve Policy Sensitivity of Trip-Based Models.” Journal of Advanced Transportation 2020 (February): e1902162. https://doi.org/10.1155/2020/1902162.\n\n\nNational Academies. 2012. Travel Demand Forecasting: Parameters and Techniques. Washington, D.C.: National Academies Press. https://doi.org/10.17226/14665.\n\n\nOrtúzar, Juan De Dios, and Luis G. Willumsen. 2011. Modelling Transport. 1st ed. Wiley. https://doi.org/10.1002/9781119993308.\n\n\nPark, Keunhyun, Sadegh Sabouri, Torrey Lyons, Guang Tian, and Reid Ewing. 2020. “Intrazonal or Interzonal? Improving Intrazonal Travel Forecast in a Four-Step Travel Demand Model.” Transportation 47 (5): 2087–2108. https://doi.org/10.1007/s11116-019-10002-0.\n\n\nPinjari, Abdul Rawoof, and Chandra R. Bhat. 2011. “Activity-Based Travel Demand Analysis.” In A Handbook of Transport Economics, edited by André de Palma, Robin Lindsey, Emile Quinet, and Roger Vickerman. Edward Elgar Publishing.\n\n\nRasouli, Soora, and Harry Timmermans. 2014. “Activity-Based Models of Travel Demand: Promises, Progress and Prospects.” International Journal of Urban Sciences 18 (1): 31–60. https://doi.org/10.1080/12265934.2013.835118.\n\n\nVovsha, Peter, Mark Bradley, and John L. Bowman. 2005. “Activity-Based Travel Forecasting Models in the United States: Progress Since 1995 and Prospects for the Future.” In Progress In Activity-Based Analysis, edited by Harry Timmermans, 389–415. https://trid.trb.org/view/759300.\n\n\nWalker, Joan L. 2005. “Making Household Microsimulation of Travel and Activities Accessible to Planners.” Transportation Research Record: Journal of the Transportation Research Board 1931 (1): 38–48. https://doi.org/10.1177/0361198105193100105.\n\n\nWeiner, Edward. 1997. “Urban Transportation Planning in the United States. An Historical Overview.” DOT-T-97-24. https://doi.org/10.21949/1526591." }, { - "objectID": "methods.html#models-to-use", - "href": "methods.html#models-to-use", + "objectID": "methods.html#wfrc-model", + "href": "methods.html#wfrc-model", "title": "3  Methodology", - "section": "3.1 Models to Use", - "text": "3.1 Models to Use\nWe are using the Wasatch Front Regional Council (WFRC) regional travel demand model as a representative trip-based model. This model is implemented in the CUBE software by Bentley (https://www.bentley.com/software/cube/, fix into real citation), and is currently used by WFRC for modeling travel in the Salt Lake City, Utah area. WFRC provided the model directly, including land use forecasts and the current long-range transportation plan. The model is taken mostly as-is, though some changes were made to better accommodate the different scenarios. These modifications are noted in more detail in their respective sections.\nOverview of WFRC model (inputs, outputs, etc)\nThe open-source ActivitySim (cite?) is used as our representative activity-based model (ABM). ActivitySim was developed…. Many agencies currently use a travel demand model based on ActivitySim in some form (cite?). another sentence.\nOverview of asim model (inputs, outputs, etc.) (also basic flowchart, tours/trips/etc)\nWe used PopulationSim (cite?) to create the synthetic population to use in ActivitySim. PopulationSim requires two main categories of inputs: a seed and controls. The seed comes from XXXXX, and contains a sample of actual (anonymized) individuals and households in the YYYYY region. PopulationSim assigns copies of these seeds to the area in a way that complies with the controls. The controls contain targets for the synthetic population in categories such as number of households, et al. For this we used a combination of the transportation analysis zone (TAZ)-level socioeconomic data present in the WFRC model and Census tract-level data." + "section": "3.1 WFRC Model", + "text": "3.1 WFRC Model\nThe WFRC model is implemented in the CUBE software by Bentley (Bentley Systems n.d.), and is currently used by WFRC for modeling travel in the Salt Lake City, Utah area. WFRC provided the model directly, including land use forecasts and the current long-range transportation plan. The model is taken mostly as-is, though some changes were made to better accommodate the different scenarios. These modifications are noted in more detail in their respective sections.\nThe WFRC model, like many trip-based models, requires the following inputs: - Land use data. This includes information about population, employment, and socioeconomic variables such as income, delineated by transportation analysis zone (TAZ). In our case this was provided by WFRC directly, as an output of their land use forecasting model(s). - Travel skims. This details travel time, cost, etc. between each origin-destination pair of TAZs. The WFRC model uses an iterative process of assigning volumes to the transportation network and recalculating the skims, which are used in the mode and destination choice model steps. - Transportation network. This includes highway, transit, etc. networks which connect the TAZs to each other. These networks contain information such as link speed and capacity. Though the WFRC model assigns travel volumes to the network, this research does not compare the model’s network assignment results. However, the network volumes are still used to calculate the travel skims. - Model constants and coefficients. Many model steps, including mode choice, require calibration constants and coefficients. These are taken directly from the WFRC model without modification.\nThe model is comprised of 5 main steps: household disaggregation, trip generation, trip distribution, mode choice, and network assignment. Each of these steps has several sub-steps, and there are additional pre- and post-processing steps, but these five steps are the core of the model. The household disaggregation model uses the land use and socioeconomic data (containing average household sizes, etc.) and estimates the number of households by type, meaning by number of residents and number of workers. An additional part of this step is a vehicle ownership model, which further disaggregates the households by number of owned vehicles. The remaining four steps proceed similarly to any other trip-based model, estimating trips from each household type by purpose, time of day, origin/destination, and mode choice, and assigning these trips to the network(s).\nThe final output(s) of the WFRC model include network volumes and performance measures. This includes loaded the network skims, which can then be used in subsequent iterations of the destination and mode choice models. Additionally, the model outputs intermediate results after each main model step. This includes disaggregated household data, trip generation estimates, and origin-destination matrices counting trips by purpose, mode, and time of day." + }, + { + "objectID": "methods.html#activitysim", + "href": "methods.html#activitysim", + "title": "3  Methodology", + "section": "3.2 ActivitySim", + "text": "3.2 ActivitySim" }, { "objectID": "methods.html#initial-model-comparison", "href": "methods.html#initial-model-comparison", "title": "3  Methodology", - "section": "3.2 Initial Model Comparison", - "text": "3.2 Initial Model Comparison\nIn order to reasonably compare the two models, the initial “baseline” scenarios in each should have relatively similar results. Certainly this applies to the predicted trips by purpose, mode, length, etc., but we also want to ensure the synthetic population generated by PopulationSim matches the socioeconomic data used in the WFRC model. Though many controls such as household size and income group were specified from the Census at the tract level, the WFRC socioeconomic data contains this information as well.\nBesides the different data sources, not all controls were weighted equally. Number of households, for example, was given at the TAZ level from the WFRC data and weighted very heavily, so there is no difference in number of households in each TAZ between the synthetic population and the WFRC data. Household size, on the other hand, is a control from the Census data, and so there is some discrepancy between the synthetic population and the WFRC data (see figure XXX). However, these discrepancies are generally relatively minor, especially in more populated areas.\nAnother important comparison to make is of household income. (compare by group because of top-coding)\nWe are getting the input zonal SE data form the WFRC model and using that as targets for our synthetic population generation (via PopulationSim).\nEnumerate and discuss model inputs/outputs (diagram?)\n\n3.2.1 Calibration check\nWe want to check if the ABM gives similar results to the WFRC model in the base scenario. If not, then we can’t really compare them. They don’t have to match exactly, but should be close.\n\n3.2.1.1 synth pop\nShow differences between the actual zonal SE data and the synthetic population data. HH is the same since it was heavily weighted, but check some other things too, like avg income.\n746 zones in the WFRC input data are listed as having no households. Many of these make sense (Utah Lake, etc.). Some in more urban areas may also make sense (commercial zoning, etc), but it’s possible some are erroneous. In any case, the synthetic population matches these targets, as we want the models to perform similarly to be able to make comparisons.\nMany variables such as vehicle ownership were not given at the TAZ level in the input data (were they given anywhere else?). This data is present in the synthetic population, but we can’t compare it (unless we find that data somewhere else).\nOverall we are pretty good; the vast majority of zones are within a doubling of the WFRC data in both persons and income. There are some outliers, but they are fringe zones that are less populated anyway.\n\n\n3.2.1.2 TLFD\nCompare TLFD, hopefully similar.\nThey are similar! NOTE: The WFRC model multiplies trips by 100 in mode choice because there are a lot of fractional trips that would be lost to rounding otherwise. We don’t need to do that here (R can handle more than 2 decimal places), so we divided the trips by 100 to get the actual amount." + "section": "3.3 Initial Model Comparison", + "text": "3.3 Initial Model Comparison\nIn order to reasonably compare the two models, the initial “baseline” scenarios in each should have relatively similar results. Certainly this applies to the predicted trips by purpose, mode, length, etc., but it is also critical to ensure the synthetic population generated by PopulationSim matches the socioeconomic data used in the WFRC/MAG model. Though many controls such as household size and income are specified from the Census at the tract level, the WFRC socioeconomic data contains this information as well. Ideally, then, variables such as number of households per TAZ and TAZ median income should be similar between the two sources.\nThe data from both the Census and the WFRC/MAG model become geography-specific control totals which PopulationSim tries to match, with different weights assigned to each control. Number of households, for example, was given at the TAZ level from the WFRC data and weighted very heavily, so there is no difference in number of households in each TAZ between the synthetic population and the WFRC data. Total population, on the other hand, is a control from the Census data, and is in fact specified at the region geography level, and so there is some discrepancy between the synthetic population and the WFRC data (see Figure 3.1). However, these discrepancies are generally relatively minor, especially in more populated areas.\n\n\nLoading required namespace: raster\n\n\nZoom: 9\n\n\n\n\n\nFigure 3.1: Comparison of population by TAZ between WFRC data and PopulationSim synthetic population.\n\n\n\n\nAnother important comparison to make is of household income, as this can significantly affect travel behavior. Instead of simply comparing average household income by TAZ, it is more useful to compare the number of households in each binned income group. Both the WFRC and ActivitySim models use income groups as a variable in their model steps rather than the actual income. The income groups for the WFRC model are given in Table 3.1.\n\n\n\n\nTable 3.1: income groups\n\n\nIncome Group\nIncome Range\n\n\n\n\n1\n≤ $45,000\n\n\n2\n$45,000–$75,000\n\n\n3\n$75,000–$125,000\n\n\n4\n≥ $125,000\n\n\n\n\n\n\n\n\nFigure 3.2 shows a comparison of the number of households in each income group between the WFRC household disaggregation model and the synthetic population generated by PopulationSim. There is a definite trend of the synthetic population to over-predict low-income households and over-predict high-income households compared to the WFRC disaggregation model. However, especially since this research is focused more on a methodological comparison between the two models rather than a comparison of results, the discrepancies are not too severe to be concerned about.\n\n\nZoom: 9\nZoom: 9\nZoom: 9\nZoom: 9\n\n\n\n\n\nFigure 3.2: income groups\n\n\n\n\n\n\n\n\nTable 3.2: mode split\n\n\nPurpose\nMode\nActivitySim predicted trips\nWFRC/MAG predicted trips\n% Error\n% Error (scaled to WFRC/MAG predicted trips)\n\n\n\n\nAll\nAll\n7441918\n8737729\n-14.8%\n0.0%\n\n\nAll\nAuto\n6515379\n7905927\n-17.6%\n-3.2%\n\n\nAll\nNon-motorized\n768463\n732250\n4.9%\n23.2%\n\n\nAll\nTransit\n158076\n99551\n58.8%\n86.4%\n\n\nHome-based Other\nAll\n3696056\n4641724\n-20.4%\n-6.5%\n\n\nHome-based Other\nAuto\n3092740\n4094887\n-24.5%\n-11.3%\n\n\nHome-based Other\nNon-motorized\n531497\n509491\n4.3%\n22.5%\n\n\nHome-based Other\nTransit\n71819\n37346\n92.3%\n125.8%\n\n\nHome-based Work\nAll\n1759501\n1711578\n2.8%\n20.7%\n\n\nHome-based Work\nAuto\n1617449\n1586413\n2.0%\n19.7%\n\n\nHome-based Work\nNon-motorized\n85430\n76413\n11.8%\n31.3%\n\n\nHome-based Work\nTransit\n56622\n48752\n16.1%\n36.4%\n\n\nNon–Home-based\nAll\n1986361\n2384427\n-16.7%\n-2.2%\n\n\nNon–Home-based\nAuto\n1805190\n2224628\n-18.9%\n-4.7%\n\n\nNon–Home-based\nNon-motorized\n151536\n146346\n3.5%\n21.6%\n\n\nNon–Home-based\nTransit\n29635\n13453\n120.3%\n158.6%\n\n\n\n\n\n\n\n\n\n\nWarning: Removed 19136083 rows containing non-finite values (`stat_density()`).\n\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : 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kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\nWarning in density.default(x, weights = w, bw = bw, adjust = adjust, kernel =\nkernel, : Selecting bandwidth *not* using 'weights'\n\n\n\n\n\nFigure 3.3: tlfd comparison" }, { "objectID": "methods.html#policy-scenarios", "href": "methods.html#policy-scenarios", "title": "3  Methodology", - "section": "3.3 policy scenarios", - "text": "3.3 policy scenarios\nWe will model the following scenarios: - Land use - Transit - WFH" + "section": "3.4 policy scenarios", + "text": "3.4 policy scenarios\nWe will model the following scenarios: - Land use - Transit - WFH" }, { "objectID": "methods.html#computational-resources", "href": "methods.html#computational-resources", "title": "3  Methodology", - "section": "3.4 Computational resources?", - "text": "3.4 Computational resources?\nDescribe BEAST and the BYU Supercomputer." + "section": "3.5 Computational resources?", + "text": "3.5 Computational resources?\nDescribe BEAST and the BYU Supercomputer.\n\n\n\n\nBentley Systems. n.d. “CUBE Modeling Software.” Accessed October 3, 2023. https://www.bentley.com/software/cube/." }, { "objectID": "1_land_use.html", @@ -109,6 +116,6 @@ "href": "references.html", "title": "References", "section": "", - "text": "Association of Metropolitan Planning Organizations. 2023.\n“ActivitySim.” ActivitySim. 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Walker. 2017. “Looking Beyond the\nMean for Equity Analysis: Examining Distributional Impacts\nof Transportation Improvements.” Transport Policy 54\n(February): 61–69. https://doi.org/10.1016/j.tranpol.2016.08.003.\n\n\nBowman, John L. 1998. “The Day Activity\nSchedule Approach to Travel\nDemand Analysis.” Ph.D. Dissertation,\nCambridge, MA: Massachusetts Institute of Technology. https://dspace.mit.edu/bitstream/handle/1721.1/16731/44704881-MIT.pdf?sequence=2.\n\n\nBramberga, Katrina Aleksandra. 2023. “Teleworking in\nFour-Step Transport\nDemand Models.” PhD thesis, KTH Royal\nInstitute of Technology.\n\n\nCastiglione, Joe, Rachel Hiatt, Tilly Chang, and Billy Charlton. 2006.\n“Application of Travel Demand\nMicrosimulation Model for Equity\nAnalysis.” Transportation Research Record:\nJournal of the Transportation Research Board 1977 (1): 35–42. https://doi.org/10.1177/0361198106197700105.\n\n\nChicago Area Transportation Study. 1959. Final Report\nIn Three Parts. Vol. 1–3.\nPrinted by authority of the State of Illinois. http://archive.org/details/chicagoareatrans01chic.\n\n\nDong, Xiaojing, Moshe E. Ben-Akiva, John L. Bowman, and Joan L. Walker.\n2006. “Moving from Trip-Based to Activity-Based Measures of\nAccessibility.” Transportation Research Part A: Policy and\nPractice 40 (2): 163–80. https://doi.org/10.1016/j.tra.2005.05.002.\n\n\nElldér, Erik. 2020. “Telework and Daily Travel: New\nEvidence from Sweden.” Journal of Transport\nGeography 86 (June): 102777. https://doi.org/10.1016/j.jtrangeo.2020.102777.\n\n\nFerdous, Nazneen, Lakshmi Vana, John L. Bowman, Ram M. Pendyala, Gregory\nGiaimo, Chandra R. Bhat, David Schmitt, Mark Bradley, and Rebekah\nAnderson. 2012. “Comparison of Four-Step\nVersus Tour-Based Models for\nPrediction of Travel Behavior\nBefore and After Transportation System\nChanges.” Transportation Research Record\n2303 (1): 46–60. https://doi.org/10.3141/2303-06.\n\n\nJones, Peter M. 1979. “New Approaches to\nUnderstanding Travel Behaviour:\nThe Human Activity\nApproach.” In Behavioural Travel\nModelling. Routledge.\n\n\nKim, Seung-Nam. 2017. “Is Telecommuting Sustainable?\nAn Alternative Approach to Estimating the Impact of\nHome-Based Telecommuting on Household Travel.” International\nJournal of Sustainable Transportation 11 (2): 72–85. https://doi.org/10.1080/15568318.2016.1193779.\n\n\nLemp, Jason D., Laura B. McWethy, and Kara M. 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Edward Elgar Publishing.\n\n\nRasouli, Soora, and Harry Timmermans. 2014. “Activity-Based Models\nof Travel Demand: Promises, Progress and Prospects.”\nInternational Journal of Urban Sciences 18 (1): 31–60. https://doi.org/10.1080/12265934.2013.835118.\n\n\nSener, Ipek N, and Chandra R Bhat. 2011. “A\nCopula-Based Sample\nSelection Model of Telecommuting\nChoice and Frequency.” Environment\nand Planning A: Economy and Space 43 (1): 126–45. https://doi.org/10.1068/a43133.\n\n\nVovsha, Peter, Mark Bradley, and John L. Bowman. 2005.\n“Activity-Based Travel\nForecasting Models in the United\nStates: Progress Since 1995 and\nProspects for the Future.” In\nProgress In Activity-Based\nAnalysis, edited by Harry Timmermans, 389–415. https://trid.trb.org/view/759300.\n\n\nWalker, Joan L. 2005. “Making Household\nMicrosimulation of Travel and\nActivities Accessible to\nPlanners.” Transportation Research Record:\nJournal of the Transportation Research Board 1931 (1): 38–48. https://doi.org/10.1177/0361198105193100105.\n\n\nWeiner, Edward. 1997. “Urban Transportation\nPlanning in the United States.\nAn Historical Overview.”\nDOT-T-97-24. https://doi.org/10.21949/1526591.\n\n\nYasenov, Vasil I. 2020. “Who Can Work\nfrom Home?” {SSRN} {Scholarly} {Paper}. Rochester,\nNY. https://doi.org/10.2139/ssrn.3590895.\n\n\nZhu, P., and S. G. Mason. 2014. “The Impact of Telecommuting on\nPersonal Vehicle Usage and Environmental Sustainability.”\nInternational Journal of Environmental Science and Technology\n11 (8): 2185–2200. https://doi.org/10.1007/s13762-014-0556-5." + "text": "Association of Metropolitan Planning Organizations. 2023.\n“ActivitySim.” ActivitySim. https://activitysim.github.io/.\n\n\nBentley Systems. n.d. “CUBE Modeling\nSoftware.” Accessed October 3, 2023. https://www.bentley.com/software/cube/.\n\n\nBick, Alexander, Adam Blandin, and Karel Mertens. 2021. “Work from\nHome Before and After the\nCOVID-19 Outbreak.” https://doi.org/https://doi.org/10.24149/wp2017r2.\n\n\nBills, Tierra S., and Joan L. Walker. 2017. “Looking Beyond the\nMean for Equity Analysis: Examining Distributional Impacts\nof Transportation Improvements.” Transport Policy 54\n(February): 61–69. https://doi.org/10.1016/j.tranpol.2016.08.003.\n\n\nBowman, John L. 1998. “The Day Activity\nSchedule Approach to Travel\nDemand Analysis.” Ph.D. Dissertation,\nCambridge, MA: Massachusetts Institute of Technology. https://dspace.mit.edu/bitstream/handle/1721.1/16731/44704881-MIT.pdf?sequence=2.\n\n\nBramberga, Katrina Aleksandra. 2023. “Teleworking in\nFour-Step Transport\nDemand Models.” PhD thesis, KTH Royal\nInstitute of Technology.\n\n\nCastiglione, Joe, Rachel Hiatt, Tilly Chang, and Billy Charlton. 2006.\n“Application of Travel Demand\nMicrosimulation Model for Equity\nAnalysis.” Transportation Research Record:\nJournal of the Transportation Research Board 1977 (1): 35–42. https://doi.org/10.1177/0361198106197700105.\n\n\nChicago Area Transportation Study. 1959. Final Report\nIn Three Parts. Vol. 1–3.\nPrinted by authority of the State of Illinois. http://archive.org/details/chicagoareatrans01chic.\n\n\nDong, Xiaojing, Moshe E. Ben-Akiva, John L. Bowman, and Joan L. Walker.\n2006. “Moving from Trip-Based to Activity-Based Measures of\nAccessibility.” Transportation Research Part A: Policy and\nPractice 40 (2): 163–80. https://doi.org/10.1016/j.tra.2005.05.002.\n\n\nElldér, Erik. 2020. “Telework and Daily Travel: New\nEvidence from Sweden.” Journal of Transport\nGeography 86 (June): 102777. https://doi.org/10.1016/j.jtrangeo.2020.102777.\n\n\nFerdous, Nazneen, Lakshmi Vana, John L. Bowman, Ram M. Pendyala, Gregory\nGiaimo, Chandra R. Bhat, David Schmitt, Mark Bradley, and Rebekah\nAnderson. 2012. “Comparison of Four-Step\nVersus Tour-Based Models for\nPrediction of Travel Behavior\nBefore and After Transportation System\nChanges.” Transportation Research Record\n2303 (1): 46–60. https://doi.org/10.3141/2303-06.\n\n\nJones, Peter M. 1979. “New Approaches to\nUnderstanding Travel Behaviour:\nThe Human Activity\nApproach.” In Behavioural Travel\nModelling. Routledge.\n\n\nKim, Seung-Nam. 2017. “Is Telecommuting Sustainable?\nAn Alternative Approach to Estimating the Impact of\nHome-Based Telecommuting on Household Travel.” International\nJournal of Sustainable Transportation 11 (2): 72–85. https://doi.org/10.1080/15568318.2016.1193779.\n\n\nLemp, Jason D., Laura B. McWethy, and Kara M. Kockelman. 2007.\n“From Aggregate Methods to\nMicrosimulation: Assessing\nBenefits of Microscopic\nActivity-Based Models of\nTravel Demand.” Transportation\nResearch Record: Journal of the Transportation Research Board 1994\n(1): 80–88. https://doi.org/10.3141/1994-11.\n\n\nMcNally, Michael G. 2007. “The Four-Step\nModel.” In Handbook of Transport\nModelling, edited by David A. Hensher and Kenneth J.\nButton, 1:35–53. 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Mason. 2014. “The Impact of Telecommuting on\nPersonal Vehicle Usage and Environmental Sustainability.”\nInternational Journal of Environmental Science and Technology\n11 (8): 2185–2200. https://doi.org/10.1007/s13762-014-0556-5." } ] \ No newline at end of file diff --git a/site_libs/bootstrap/bootstrap.min.css b/site_libs/bootstrap/bootstrap.min.css index 49fe930..d3c0f95 100644 --- a/site_libs/bootstrap/bootstrap.min.css +++ b/site_libs/bootstrap/bootstrap.min.css @@ -7,4 +7,4 @@ * * ansi colors from IPython notebook's * 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