This page provides summary comparisons of six different scenarios analyzed for the West Station Area using the accessibility-based travel model. The summary data and visualizations shown provide examples of the potential comparative analyses supported by the model rather than an exhaustive menu of metrics.

The scenarios analyzed include:

  1. Base - Approximates current land uses and travel networks

  2. LRTP - Reflects adopted long term (2045) land use forecasts and cost feasible travel networks.

  3. RailVision - Reflects modified long term forecasts in the focus area and expanded/enhanced regional rail services.

  4. BRT-FEIR - Reflects modified long term forecasts in the focus area and potential bus rapid transit services.

  5. BRT-Alt A - Reflects modified long term forecasts in the focus area and potential bus rapid transit services.

  6. BRT-Alt B - Reflects modified long term forecasts in the focus area and potential bus rapid transit services.

Use the tabs below to compare scenario outputs across key model steps.

Explore scenario results

Trip generation

In the trip generation step, households, jobs, school enrollments, etc. are used to estimate how many trips begin or end in different zones. The charts below present example summary outputs for four alternative land use configurations. Base, LRTP, and FEIR are operative in the scenarios defined above. FEIR MAX is a potential alternative land use forecast in the focus area used in early model sensitivity testing, but it is not reflected in any reported scenarios.

The first chart shows trips productions and attractions generated in the window area under each scenario, stratified by purpose. Generally, productions reflect households and their trip-making trends based on various characteristics, like income and vehicle ownership; attractions reflect jobs and school enrollments. Both productions and attractions are forecasted to increase over base year conditions in all scenarios. Differences between the alternative forecasts are subtle since they are isolated to the focus area, which is just a small subset of the window area. The share of trips by each purpose is roughly consistent accross all scenarios as well, with the most common travel purpose for productions and attractions being home-based other trips.

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Trip productions are sensitive to different household characteristics. The chart below shows the breakdown of households by vehicle ownership in the window area in each alternative land use configuration. All future year scenarios show a dramatic expected increase in one-car households throughout the window area. Vehicle ownership is also an important factor in mode choice decisions. Having high numbers of zero-car or one-car households will result in more non-auto trips, all else being equal.

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Accessibility scores

Accessibility scores describe the number of activities (jobs, school enrollments, households, etc.) that can reach or be reached from a given zone. In addition to informing mode choice models, the scores can be used to assess how well the transportation system connects various activities to one another. The charts below summarize average access to jobs scores for households by income group for non-auto travel modes (transit, walking, and biking). The charts show that households in the highest income bracket ($125,000 and higher) have the highest access scores in the window area in all scenarios. Middle-income households ($35,000 - $125,000) have the lowest non-auto access scores.

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Mode Choice

Mode choice estimates are a pivotal output of the sketch accessibility model. Access scores and demographic data combine to estimate what proportions of trips are likely to be made by each travel mode.

The chart below shows the breakdown of trips by mode and purpose for each scenario for productions in the window area. Future year scenarios indicate increased walking and biking trips, especially for non-home-based trips. Walk- access transit (WAT) is much more common than drive-access transit (DAT), as expected for the urban context of the window area. All scenarios indicate an expected increase in WAT trips for the home-based work (HBW), but there is substantial variety in transit utilization for home-based other (HBO) trips, with shares highest in the RailVision scenario and lowest in the BRT-FEIR and BRT-Scen B runs.

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The next chart shows transit ridership estimates by mode of access for productions and attractions separately. In all scenarios, there are more transit attractions than productions, reflecting the large number of destinations in the window area. These destinations can be reached by both WAT and DAT modes, whereas DAT is less relevant to productions in the window area. Total expected transit utilization is highest in the RailVision scenario.

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The chart below presents similar information for non-motorized trips (walking and biking). All future scenarios anticipate fewer non-motorized attractions but more non-motorized productions, reflecting increased residential development and more travel to the window area by transit and auto modes from the remainder area. The RailVision and BRT-Scen A scenarios have the greatest amount of non-motorized trip-making.

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Distribution

The distribution phase estimates where trips are likely to travel to and from. Outputs from this portion of the model can be summarized in terms of trip lenghts, durations, and costs. The chart below compares vehicle miles of travel (VMT) generated purpose across each scenario. This reflects the total number of trips made by driving and the length of each trip. VMT is expected to increase over the base year in all future year scenarios. This increase is more pronounced for trips produced in the window area than for those attracted there. VMT estimates are highest in the BRT-FEIR and BRT-Scen B scenarios.

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The next chart displays average trip lengths (in miles) to and from the window area by mode across all scenarios. In general, DAT trips are typically longer than trips by other modes for travel to the window area. Walk trips tend to be about a mile in length, while bike trips average about 3-4 miles. Trips attracted to the window area tend to be longer than trips produced there. This reflects the area’s location near the urban core of the Boston region (allowing trips produced there to be relatively short) and its attractions-heavy character (which would indicate more trips from the remainder area coming to the window). Overall, trip lengths appear to be longest in the BRT scenarios, though transit trips to the window are longest in the RailVision scenario.

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TNC

Finally, the TNC post-processing phase of the model can be summarized in terms of TNC trip totals, stratified by purpose and/or mode replaced. The chart below provides an example comparing TNC trip totals to and from the window area by the mode being replaced. In all scenarios, transit trips are the most likely candidates for TNC trip replacement. TNC estimates for all future year scenarios are higher than the base year estimates, but there is substantial fluctuation. the LRTP and BRT-Scen A scenarios suggest the highest TNC utilization, while the RailVision and BRT-Scen B scenarios are only slightly higher than the base estimates.

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