Anne Halvorsen

• Area Lead, Operations Technology Pod: Set technical and product strategy for a cross-functional team spanning science, engineering, and operations; own biannual planning, roadmap execution, and org-level prioritization while managing and mentoring four data scientists. • Org-Wide Model Ownership: Lead end-to-end development of models used to inform decisions across Lyft’s Bikeshare business, from problem framing and system design through production deployment, monitoring, and iteration. - Electrification & Capital Strategy: Architected simulation and financial modeling frameworks guiding $10M+ in capital allocation and long-term electrification planning. - Demand Forecasting: Designed and productionized ridership forecasting model that improved accuracy up to 10% and now underpins staffing, budgeting, and CapEx planning across all operated markets. - Labor & Staffing Optimization: Own core staffing model used to set monthly labor needs. Added features to improve accuracy (e.g. fleet size elasticity) and reduce manual interventions (e.g. designing a process to take more inputs automatically). - Real-Time Task Valuation: Led redesign of pickup task valuation model, landing on a framework that can be more flexible to operations needs while improving productivity by 10%. • Data Quality & Monitoring: Elevated business health observability through improved datasets, dashboards, alerting, and scalable third-party access controls. • Science Community Leadership: Co-led DS Development group, coordinating 10+ volunteers to deliver mentorship programming, discussion panels, and a redesigned internal science knowledge hub.

• Managed small technical teams to develop automated passenger counter (APC) datasets, a bus delay webtool, and an improved bus ridership model, supporting planning and operations staff • Worked with NYCT staff and outside expert to develop a more statistically-sound fare evasion sampling plan, then personally implemented new sample generation plan with a streamlined python process • Coordinated the addition of new state-mandated performance metrics to public dashboards • Performed statistical analysis of APC data and analysis of COVID-19 impact on bus ridership for 2021 TRB presentation "An Examination of New York City Transit’s Bus and Subway Ridership Trends during the COVID-19 Pandemic" • Continued analytical work to support projects that began in previous position

• Contributed to hundreds of increased speed limits and recalibrated signals in the subway system by maintaining records for and helping to manage NYCT's Save Safe Seconds initiative • Performed numerous in-depth subway performance analyses, with topics including incidents, running times, passenger-centric metrics, and use of Control Center service management tool • Developed SQL and Python processes to import and clean new train movement and delay data streams • Updated dashboard and data processing tool front-ends using Javascript and HTML, and built a Slack app in python to share subway service data feed with NYCT communications team • Participated in selective OP Enrichment and Development program, which included working on a project to develop major employee communication plan and participation in enrichment events to gain exposure to other areas of NYCT • Publications at this position have included: "Passenger-Centric Performance Metrics for the New York City Subway." Transportation Research Record (2019) and "An Algorithm for Tracing Train Delays to Incident Causes" Transportation Research Board Annual Meeting (2020)

• Developed demand models and performed data analysis for modes ranging from transit and ridesharing in New York City to rail and toll roads across the US: - Built econometric regression models linking toll road demand to economic indicators and led development of a spreadsheet-based route choice model - Analyzed large quantities of data, including the NYC taxi database, cell phone-based OD matrices and Census/ACS data, for summary and model input purposes • Made significant contributions to written reports, from full technical reports to high-level public presentations, including designing maps and graphics • Participated in project management tasks including developing project scopes, costs, and timelines

Involved in 2 year research project with Hong Kong’s MTR system: • Analyzed and improved customer-centric reliability metrics using a variety of large transit data sources • Developed framework for developing travel demand management programs for transit systems, using MTR as a case study. Analyses included using AFC data and clustering techniques to understand user patterns, a customer panel analysis to monitor longitudinal trends, and calculation of elasticities for new TDM strategies • Publications from this work include "Reducing subway crowding: analysis of an off-peak discount experiment in Hong Kong." Transportation Research Record (2016) and "Demand management of congested public transport systems: a conceptual framework and application using smart card data." Transportation (2019)

• Assisted on study of how people perceive and adapt to unreliability in transit service. • Tasks included literature reviews; survey design, recruitment, and implementation • Co-authored paper “Passengers' Perception of and Behavioral Adaptation to Unreliability in Public Transportation” presented at 2013 TRB Meeting

• Developed inventories of crosswalks, traffic signals, and water/sewage systems • Performed traffic signal cost analysis • Reviewed High Speed Rail study and station design for City issues and prepared issues summary • Reviewed plans submitted for city permits