Transit Service

Increase annual ridership on Big Blue Bus

This indicator measures total annual passengers on Big Blue Bus.Explore the data
annual riders
Current
20,000,000annual riders
Dec 2020 Target
Goal Period ended December 2020

            What is this indicator?

            This indicator measures total annual passengers on Santa Monica’s Big Blue Bus.

            Why is it important?

            Santa Monica has one of the most extensive transit networks among cities of its size and is committed to creating multi-modal transportation systems that enhance mobility,while minimizing road congestion and pollution from single-occupancy vehicles.

            The City and Metro are making a significant investment in the Expo Light Rail Line, and also on facilities and services designed to increase access to future station areas for people walking, biking or busing. Mixed-use neighborhoods support transit oriented development and pave the way for the upcoming light rail train service in Santa Monica.

            Improving use of public transportation is also a key element of the Santa Monica Land Use and Circulation Element (LUCE) , which establishes the City’s land use,urban design, and transportation vision.

            How are we doing?

            Ridership on the Big Blue Bus remains strong despite a modest decrease in recent years. Based on National Transit Database (NTD) metrics, total ridership on Big Blue Bus has declined in the past two years; FY13 was 19.3M, FY14 was 18.8M andFY 15 closed at 18.7M. Declines in transit ridership were greater at other agencies in the Los Angeles region. BBB was following regional trends but appears to be reversing slightly, perhaps due to TAP, NextBus, route changes or other regional forces, such as higher employment and/or fuel costs,encouraging an uptick.

            What can I do?

            Look up your public transit options on the Big Blue Bus .

            Learn more about the Monica Land Use and Circulation Element (LUCE) .

      Data Governance

      describes the quality of the data itself. Governance issues generally indicate that the data source is considered incomplete or unreliable.

      Model Health

      describes the quality of the predictive model. If the model health is poor, the trend prediction should not be trusted.

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