Case Study:

Tren Urbano

Conclusions

Limitations

At the same time, the measures and methods employed here have some pitfalls and limitations:

• First, it is not clear how to value the accessibility measures in decision-making. The measures provide an idea of the relative benefits of various alternatives, but accessibility benefits are not directly valued in comparison to project costs.
• Second, the gravity-based accessibility measure does not have an intuitive interpretation, and its acceptability to planners and decision-makers has not been proven. While it has theoretical advantages over the threshold-based measure described in the Montgomery County case study, its interpretation is somewhat harder to explain.
• Third, as noted by the authors, the analysis by income group does not account for the potential future redistribution of population groups. This is a problem common to all equity analyses based on projections, since shifts in the location of population by income level cannot easily be modeled. Furthermore, the transportation project itself may affect this distribution. A rail transit project that provides significant benefits to commuters, for example, may attract higher-income residents to station areas, thus reducing the relative benefits of the system to low-income households.
• Fourth, the accessibility analysis does not account for potential "spatial mismatches" in housing and employment, for example, a lack of affordable housing near lower-paying jobs. A method of matching residents to jobs by occupational class is discussed in the Appendix to the San Francisco Bay Area case study.
• The analysis of transit service improvements provides a "first cut" at looking at the benefits of improvements across modes. However, the cost and feasibility of implementing a 10 percent level-of-service increase may vary considerably. Additional work would be required to identify potential strategies, costs, and implementation issues.

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