Anastasia Soukhov, Ignacio Tiznado-Aitken, Matthew Palm, Steven Farber & Antonio Paez
Abstract
This work provides a synthesis of how transportation fairness, justice and equity academic literature has defined and operationalized standards. We first clarify the key concepts of fairness, equity, justice and standards, and introduce a flexible “Who, What, Where, When, Why, and How” (5WH) framework for examining fairness questions. We then systematically reviewed academic literature published across three decades (1992 to 2022), ultimately identifying 165 relevant articles that operationalize transportation fairness standards. We use the 5WH framework to collate the review, supplementing the work with additional recent contributions. From our review, we find that most articles on this topic were published within the last five years and focus on urban contexts, with 40% examining Global South cases. Income groups, followed by specific age groups (i.e., older adults) and people with disabilities, are the most common “subjects of justice”. Transit and pedestrian modes are the most frequently studied “mobility tools”; with many analyses being multimodal and comparative in nature. The benefits and burdens–the “What” of mobility– focuses on movement (i.e., quality of trips taken) or the potential for movement (i.e., accessibility). Conceptualizations of fairness are varied (e.g., Vertical Equity, Wellbeing, Rights-Based) but are most often operationalized through supporting opportunity thresholds (i.e., number of parks within a 30-min travel time) or population thresholds(i.e., bottom income quartile), with Infrastructure (i.e., level of service) and Environmental + (i.e., air pollution) thresholds also appearing. We conclude with five calls to action for advancing transportation research and practice: (1) ground tailored standards in explicit conceptualizations of justice; (2) co-create and refine methods that embed systems-thinking approaches to fairness; (3) treat data as a matter of justice, ensuring diverse destinations beyond conventional datasets are represented; (4) create clearer links between standards and lived experiences; and (5) evaluate interventions and policies against explicit fairness criteria.
