Detecting Bias in TDM Research

The transportation demand management sphere is increasingly being influenced by quantitative analysis, and scientific research. These approaches have huge potential to make positive impacts. However, to ensure that policies based on data and TDM research are equitable, professionals need to be aware of their own potential biases and understand how they can impact outcomes.

Bias in TDM research: what is it?

With science playing an ever-growing role in the TDM space, inherent biases in scientific models stand to have an increasing impact on research outcomes. As leading experts have pointed out, prevailing approaches to scientific inquiry and the scientific method itself are grounded in society’s dominant, traditional sociopolitical systems.

Thinkers and theorists preoccupied with questions of inclusion and equality have pointed out the shortcomings and problems with these systems. They tend to be grounded in perspectives that favor the majority order rather than prioritizing the needs and viewpoints of minority communities. As a result, policies based on TDM research can become shaded by inherent viewpoint biases that do not account for equitable social justice.

How to overcome bias in TDM research

Accounting for and correcting biases in TDM research requires active effort on the part of the scientists, professionals, and policymakers who interpret data to make decisions. Here are some questions to ask when looking at data-based information that’s going to inform policy:

• Does that data reflect inherent social biases? What biases are accounted for, and what biases are not?
• Who collected the data? What biases might they have?
• Who interpreted the data? What biases might they have?
• Does the data represent the needs, viewpoints, and experiences of all members of the population group to which it applies?

Most of all, it’s critically important to supplement data-based analysis with actual field research. It’s critically important for policymakers to have a clear, accurate understanding of what’s really happening “on the ground” in the location in question. In many cases, the raw numbers may not reflect the real, lived experiences of the people the future policies will affect.

These shortcomings can interfere with equitable access to transportation systems. This is particularly true in a time of ongoing gentrification, as the traditional demographics of cities and neighborhoods continue to experience rapid, dramatic changes.

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