UC Berkeley

Metropolitan Planning Organizations (MPOs) regularly perform equity analyses for their long-range transportation plans, as this is required by Environmental Justice regulations. These regional-level plans may propose hundreds of transportation infrastructure and policy changes (e.g. highway and transit extensions, fare changes, pricing schemes, etc.) as well as land-use policy changes. The challenge is to assess the distribution of impacts from all the proposed changes across different population segments. In addition, these agencies are to confirm that disadvantaged groups will share equitably in the benefits and not be overly adversely affected. While there are a number of approaches used for regional transportation equity analyses in practice, approaches using large scale travel models are emerging as a common existing practice. However, the existing methods used generally fail to paint a clear picture of what groups benefit or do not benefit from the transportation improvements. In particular, there are four critical shortcomings of the existing transportation equity analysis practice. First, there is no clear framework outlining the key components of a transportation equity analysis at the regional-level. Second, the existing zonal-level group segmentation used for identifying target and comparison groups are problematic and can lead to significant biases. Third, the use of average equity indicators can be misleading, as averages tend to mask important information about the underlying distributions. Finally, there is no clear guidance on implementing scenario ranking based on the equity objectives.

In addressing the first shortcoming of existing equity analysis practices, we present a guiding framework for transportation equity analysis that organizes the components of equity analysis in terms of transportation priorities, the model, and the equity indicators. The first component emphasizes the need to identify the priority transportation improvement(s) relevant for communities, as this guides the transportation benefits (or costs) to be evaluated. The second component is the model to be used for facilitating scenario analysis and measuring the expected transportation and land-use changes. The third component refers to the selection of equity indicators (ideally selected based on the transportation priorities identified), and the evaluation of these indicators. This three-part framework is also useful for outlining the research needs for transportation equity analysis. Among other key research needs, the literature indicates that the development of meaningful distributional comparison methods for transportation planning and decision-making and the use of more comprehensive measures of transportation benefit (for use as equity indicators) are critical.

The primary contributions of this dissertation relate to the third component; we develop an advanced approach for evaluating transportation equity outcomes (as represented by the equity indicator(s)). Our proposed analytical approach to transportation equity analysis addresses the existing shortcomings with respect to zonal-level group segmentation and average measures of transportation equity indicators. In addition, our approach emphasizes the importance of scenario ranking using explicit equity criteria. Our approach leverages the disaggregate functionality of activity-based travel demand models and applies individual-level data analysis to advance the existing equity analysis practices.

There are four steps in our proposed equity analysis process. The first step is to select the equity indicators to be evaluated and segment the population into a target group and comparison group(s). In this case we advocate for an individual -unit of segmentation and therefore individual-level equity indicators. This minimizes the biases associated with aggregate group segmentation and average equity indicators. The second step is to calculate the indicators from the model data output, which involves determining the exact measures (formulas) for the selected equity indicators. Here we advocate for measures that are comprehensive and sensitive to both transportation system changes and land-use factors, such as the logsum accessibility and consumer surplus measure. The third step in the process is to generate and evaluate distributions of the individual-level equity indicators. In particular, we advocate for the use of what we refer to as the "Individual Difference Density" comparison, which compares distributions of individual-level changes for the population segments across the planning scenarios. This comparison allows for the "winners" and "losers" resulting from the transportation and land-use plans to be identified. The fourth and final step in the process is to identify equity criteria (associated with the chosen equity standard (objective)) and rank the planning scenarios based on the degree to which they meet the equity criteria.

We present two conceptual demonstrations of the advantages of distributional comparisons, relative to average measures. The first case uses a synthetic data set and simple binary mode choice model to show and the second case uses an empirical data set (the 2000 Bay Area Travel Survey) and more sophisticated mode choice model. These demonstrations show that distributional comparisons are capable of revealing a much richer picture of how different population segments are affected by transportation plans, in comparison with average measures. Further, distributional comparison provides a framework for evaluating what population's characteristics and conditions lead to certain distribution transportation outcomes.

Our proposed process for regional transportation equity analysis is subsequently applied in a case study for the San Francisco Bay Area. We evaluate joint transportation and land-use scenarios modeled using the Metropolitan Transportation Commission's state-of-the-art activity-based travel demand model. We demonstrate the power of individual-level data analysis in a real-world setting. We calculate individual-level measures of commute travel time and logsum-based accessibility/consumer surplus using the model output and compare the scenario changes across income segments. We generate empirical distributions of these indicators and compare the changes associated with the planning scenarios for low and high income commuters. Further, we apply criteria for a set of equity standards (which represent alternative equity objectives) and rank the planning scenarios. There are four key takeaways from this case study. First is that our results show a significant difference in equity outcomes when using the individual-level population segmentation approach, compared to using the zonal segmentation approach done in practice. In fact we find opposite results. For average commute travel time, the Metropolitan Transportation Commission's zonal segmentation approach indicates that low income commuters are worse off than all other commuters, while the individual segmentation approach (in our case) indicates that low income commuters are significantly better off than high income commuters. While the underlying causes for these results warrant further investigation, we hypothesize that this difference is due to the fact that the zone-based approach only captures 40% of the target (low income) group. The individual-level segmentation approach is able to capture 100% of the target group. Second is regarding the equity indicators evaluated. The commute travel time indicator results indicate that low income commuters are better off than high income commuters, while the accessibility/consumer surplus results indicate that low income commuters are worse off than high income commuters. The underlying causes for these results warrant further investigation, but we hypothesize that this difference in results to due to the fact that the logsum accessibility/consumer surplus measure by design is able to capture transportation and land-use related factors, while the travel time measure only captures one dimension of transportation user factors. Focusing on travel time may be misleading because it does not fully capture the true benefits of the transportation scenarios. Third is regarding the use of distributional comparisons, relative to average measures. We find that distributional comparisons are much more informative than average measures. The distributional measures are capable of providing a much richer picture of individuals-level transportation impacts, in terms of who gains and who loses due the transportation planning scenarios. Using the accessibility/ consumer surplus measure, the Individual Difference Densities show that as many as 33.3% of low income commuters experience losses, compared to 13.4% for high income commuters. Finally, we make the case that the use of equity standards for scenario ranking plays an important role in the equity analysis process. Our results show that different equity standards result in different rankings for the transportation planning scenarios. This points to the need for agencies (and communities) to make conscious decisions on what equity standard(s) should be used and apply this/these in the scenario ranking process.

This dissertation work includes the first known full-scale application of a regional activity-based travel model for transportation equity analysis that involves distributional comparisons of individual-level equity indicators and scenario ranking based on equity criteria. We find that while the existing practice is to use average measures to represent how difference are affected by transportation plans, distributional comparison are able to provide for a richer evaluation of individual-level transportation impacts. Distributional comparisons provide a framework for quantifying the "winners" and "losers" of transportation plans, while average measures and be misleading and uninformative. We make significant progress with regard to evaluating equity indicators (part three of the guiding framework). However, our proposed process is flexible and can be extended to include a number of additional advances, including more environmental and long-term land-use related equity indicators (e.g. emissions exposure, gentrification and displacement risk, employment participation, etc.) and additional population segments (e.g. age, ethnicity, household type, auto-ownership class, etc.). Among other important research directions, our analytical framework for regional transportation equity analysis can be applied to investigating why certain groups are more likely to be "losers" and what factors of transportation planning scenarios to modify in order to arrive at a more equitable transportation and land-use plan.