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Recent technological innovations have changed why, when, where, and how people travel. This, along with other changes in the economy, has resulted in declining transit ridership in many U.S. metropolitan regions, including Los Angeles. It is important that transit agencies become data savvy to better align their services with customer demand in an effort to redesign a bus network that is more relevant and reflective of customer needs. This paper outlines a new data intelligence program within the Los Angeles County Metropolitan Transportation Authority (LA Metro) that will allow for data-driven decision-making in a nimble and flexible fashion. One resource available to LA Metro is their smart farecard data. The analysis of 4 months of data revealed that the top 5% of riders accounted for over 60% of daily trips. By building heuristics to identify transfers, and by tracking riders through space and time to systematically identify home and work locations, transit trip tables by time of day and purpose were extracted. The transit trip tables were juxtaposed against trip tables generated using disaggregate anonymized cell phone data to measure transit market shares and to evaluate transit competitiveness across several measures such as trip length, travel times relative to auto, trip purpose, and time of day. Relying on observed trips as opposed to simulated model results, this paper outlines the potential of using Big Data in transit planning. This research can be replicated by agencies across the U.S. as they reverse declining ridership while competing with data-savvy technology-driven competitors.

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Climate risk factors, including wildfire, sea level rise, inland flooding, and extreme heat, as well as gentrification displacement pressures will be primary drivers of migration in the coming years. Travel demand modeling relies on reasonable and appropriate forecasts of demographic totals at the detail of travel analysis zones. Methodologies for developing scenarios in response to individual and combined climate risk factors are described, drawing on work undertaken for the Southern California Association of Governments SoCal Regional Climate Adaptation Framework. Methodologies for developing scenarios in response to gentrification displacement pressures of low-income workers are described, drawing on work carried out for the California Statewide Freight Forecasting and Travel Demand Model. These methodologies leverage modeling tools that are readily available to agencies, allowing for rapid testing of scenarios and integration with other planning processes. Climate adaptation and housing policy, respectively, are currently in need of greater integration and coordination. Future directions are explored to integrate these methodologies and create a combined demographic relocation model, sensitive to both climate risk factors and the affordability and gentrification displacement pressures arising out of shifting demand–supply dynamics and population–job balance in high growth areas.