New SCAG White Papers Turn Big Data Into Local Mobility Insights
SCAG today published five new white papers that showcase how big data analytics can support local mobility planning in Southern California. The white papers correspond to five distinct mobility case studies in the cities of Anaheim, Culver City, Pasadena, and Pomona, as well as San Bernardino County.
The SCAG Transportation Data Analysis Technical Assistance (TA) Program promotes the use of emerging data tools to support data-driven planning across the region. As part of SCAG’s Regional Pilot Initiatives program, the TA Program supports local agencies in accessing advanced mobility datasets and technical guidance to address complex transportation challenges.
SCAG solicited research questions from public agencies and then selected each of the five case studies to demonstrate practical applications of mobility analytics to address transportation safety, congestion, transit performance, demand management, and land use impacts.
During the demonstration process, the TA Program helped agencies refine research questions, apply innovative analytical approaches, build data literacy, and develop replicable methods for integrating new data sources into transportation decision-making. SCAG’s regional subscription to StreetLight Insight® provided participating agencies with access to multimodal travel data, origin-destination analysis, safety analytics, speed data, and other mobility insights.
These case studies show how big data can complement traditional planning to improve transportation safety, enhance multimodal connectivity, reduce emissions, and support more equitable investments. While some lessons are specific to the study area and scope of work, several common themes emerged regarding the benefits of big data analytics for mobility planning processes, including more informed decision-making, improved safety and accessibility, stronger support for investments, and increased local capacity.
Case Studies
City of Anaheim – Multi-Modal Opportunity Network
- Developed a data-driven framework to identify roadway segments where pedestrian, bicycle, and transit investments could provide the greatest safety and accessibility benefits.
- Combined multiple datasets and evaluation criteria to create a consistent, transparent approach for prioritizing multimodal improvements citywide.
- Created a tool that can support project screening, grant applications, transportation planning, and public engagement.
City of Culver City – Public Accessway Network Analysis
- Evaluated how big data can measure the connectivity, safety, and public health benefits of a planned pedestrian and bicycle access network in the Fox Hills Specific Plan area.
- Used vehicle speed data and network analysis to understand pedestrian exposure and opportunities to improve walking and biking connections.
- Demonstrated how innovative data tools can support planning decisions, safety analysis, and evidence-based infrastructure investments.
City of Pasadena – Corridor Characterization Analysis
- Examined changes in traffic volumes and travel behavior following the 2025 Eaton Fire to help the city understand evolving transportation conditions.
- Analyzed vehicle activity along key corridors to identify areas where multimodal improvements and potential roadway changes may warrant further consideration.
- Demonstrated how emerging data sources can help communities evaluate transportation impacts following major disruptions and inform future planning decisions.
City of Pomona – Complete Streets Analysis
- Evaluated how observed vehicle speeds vary across roadway types and complete streets classifications to support the city’s complete streets initiative.
- Integrated StreetLight Insight speed data with local roadway characteristics to better understand safety conditions and street design opportunities.
- Provided a data foundation to support future roadway design decisions, safety improvements, and stakeholder communication.
- Analyzed commute travel patterns along highly congested corridors to support the Inland Empire Commuter Program’s efforts to encourage carpooling and other transportation options.
- Used big data analytics to examine travel characteristics, origin-destination patterns, and potential opportunities for shared-ride mode shifts.
- Demonstrated how mobility data can help identify target areas and commuter groups for more effective transportation demand management strategies.