Investigation of Equity Biases in Transportation Data: A Literature Review Synthesis

Equity is a critical field of study in transportation. The built transportation network does not serve the needs of the population to achieve equal levels of economic vitality and prosperity. Because of these concerns, there has been a recent effort to address these equity issues in the transportation network. This effort coincides with a massive growth in the data that are available for transportation practitioners, known as big data. The growth of data has led to data-driven decision-making to allow for more effective transportation policies and decisions than were afforded with classical methods. However, as the amount of data available has grown, the understanding of the biases within that data and the equity implications of those biases has not. Equity biases are any bias in a data source that produces a negative equity outcome by underrepresenting historically disadvantaged populations. This reframes the concept of data biases by focusing on the equity outcomes of data biases as opposed to the precipitating causes of bias. Understanding, quantifying, and mitigating these equity biases are critical to ensuring the practice of equity is maintained in the transportation field. This paper addresses this concept by first showing the historical and current practices related to equity in transportation, second by reviewing the growth of big data in transportation, and finally by reviewing the current practices related to data biases in transportation.

Automated summary

Equity is a critical issue in transportation, especially with the growth of big data. However, there is still a lack of understanding and addressing data biases and equity biases. Therefore, it is important to better understand and tackle equity biases in transportation data to ensure fairness in the field.