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How Data Can Help Reduce Transportation Inequity

The National Association of City Transportation Officials (NACTO) stated that factoring in existing information on race, income, gender and similar identifiers is necessary for understanding patterns of inequity.

However, NACTO also stated that crash data could be improved by additional data points and granularity to fully understand the equity dimensions of road safety. Often, traffic-related accidents are concentrated in lower-income communities where larger percentages of residents do not have access to a single-occupancy vehicle and rely on other modes of transport, such as bikes, public transportation and pedestrian walkways.

Despite higher fatalities among bike riders and pedestrians, current crash data needs more specificity to address factors such as vehicle speed, site-specific issues and the mechanics of a pedestrian or bicyclist crash. It recommends the Department of Transportation (DOT) fund and supports the creation of datasets that evaluate physical access to transit such as the prevalence of sidewalks, safe crossing points, and bike lanes, stop accessibility and lighting, sign and information clarity, and presence of shelter and seating.

In one example of the benefits such efforts can yield, the San Francisco Municipal Transportation Agency added qualitative measurements for modal, race and disability statuses to its police crash reports and was able to create a High Injury Network map of its city. Cross-referencing this with another map displaying other inequity indicators allowed the agency to pinpoint unsafe areas within San Francisco.

The Chicago Metropolitan Agency for Planning (CMAP) also responded noting that existing assessment tools tend to aggregate disadvantaged groups into one, but their experiences and travel behaviour are often more varied. CMAP also suggested that while using the Census Bureau’s American Community Survey data can be beneficial in measuring person-level data about commute mode and travel time, it excludes those who are unemployed.

CMAP stated that to enable effective data collection, the Department should provide standards, guidance on best practices and funding to states and to facilitate the collection and analysis of equity data at the regional scale. Reporting of equity data could be operationalised through transportation planning products such as state/regional transportation plans and state/regional transportation improvement programmes.

Data-driven assessments of whether its programs and policies perpetuate systemic barriers to opportunities and benefits for people of colour and other underserved groups will better equip the Department to develop policies and programs that deliver resources and benefits equitably to all.

U.S. government agencies have been using data to make informed decisions in various areas as data-driven organisations deliver better outcomes, as reported by OpenGov Asia. A new report found that four out of five local government officials in the U.S. say they have improved their use of data in the past six years to drive better outcomes for residents.

Two key areas that have seen improvement are performance management and taking action, according to “Closing the Data Gap: How Cities Are Delivering Better Results for Residents. The report is based on a survey of 44 officials in the What Works Cities (WWC) network, an initiative to increase cities’ use of data.

Since then, the number of cities monitoring and analysing their progress toward key goals more than doubled from 30% to 75%, and the percentage of cities modifying existing programmes based on data analytics went from 20% to 61%. City leaders and staff are moving beyond old practices based on precedent or instinct. Instead, they are using data to make more effective operational, programmatic and policy decisions. Residents are reaping real benefits, from improved services to greater visibility into how their local government works.

Getting to more effective local government and better outcomes for residents requires the hard work of adopting foundational data practices, developing data skills across a broad swath of city staff, and putting in place critical data infrastructure.


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