
Over six months, contributed to BayAreaMetro/NetworkWrangler by engineering robust data and network configuration solutions for regional transportation planning. Developed and maintained Python-based scripts to automate baseline network specification, integrated multi-year transit and highway projects, and enhanced scenario planning through parameterized data models. Focused on data integrity by cleaning obsolete entries and ensuring cross-horizon consistency, while leveraging configuration management and data engineering best practices. Enabled reproducible datasets for planners and improved traceability with disciplined version control. The work supported long-range scenario analysis, streamlined project integration, and reduced manual maintenance, demonstrating depth in backend development, data structure manipulation, and transportation systems modeling.
April 2026: Delivered foundational enhancements in BayAreaMetro/NetworkWrangler that broaden the planning horizon and standardize baseline data. Key features implemented include a Baseline Network Specification Script for the 2023 Baseline Network, integration of the CalTrain Portal into the 2036 project specifications, and expansions to TIP_PROJECTS for 2030–2040 planning. No major bugs were reported this month; minor maintenance ensured consistency across project specs. Business impact includes a reproducible netspec dataset for planners, extended horizon for scenario analysis, and better alignment between transit and roadway planning data. Technologies demonstrated include Python scripting for netspec generation, data-driven project dictionaries, and disciplined Git-based version control across multiple commits.
April 2026: Delivered foundational enhancements in BayAreaMetro/NetworkWrangler that broaden the planning horizon and standardize baseline data. Key features implemented include a Baseline Network Specification Script for the 2023 Baseline Network, integration of the CalTrain Portal into the 2036 project specifications, and expansions to TIP_PROJECTS for 2030–2040 planning. No major bugs were reported this month; minor maintenance ensured consistency across project specs. Business impact includes a reproducible netspec dataset for planners, extended horizon for scenario analysis, and better alignment between transit and roadway planning data. Technologies demonstrated include Python scripting for netspec generation, data-driven project dictionaries, and disciplined Git-based version control across multiple commits.
March 2026 (2026-03): Delivered forward-looking network configuration enhancements in BayAreaMetro/NetworkWrangler to support 2035 Phase1 and TIP2027 2030 planning. The changes enable integrated multi-year project pipelines and improve configurability for future deployments, with clear commit-level traceability to project milestones.
March 2026 (2026-03): Delivered forward-looking network configuration enhancements in BayAreaMetro/NetworkWrangler to support 2035 Phase1 and TIP2027 2030 planning. The changes enable integrated multi-year project pipelines and improve configurability for future deployments, with clear commit-level traceability to project milestones.
May 2025 monthly summary for BayAreaMetro/NetworkWrangler: Delivered three Alt1-focused network updates to strengthen long-range scenario planning and reduce processing complexity. Highlights include removal of Alt1 exclusion to simplify blueprint processing, incorporation of EIR Alt1 2035/2050 transit service increases, and refinement of Alt1 transit scaling parameters for 2035/2050. These changes enhance forecast accuracy, reduce maintenance overhead, and enable more robust planning conversations with stakeholders. Key technologies include blueprint processing, parameterization, and version-controlled change management (Git commits shown).
May 2025 monthly summary for BayAreaMetro/NetworkWrangler: Delivered three Alt1-focused network updates to strengthen long-range scenario planning and reduce processing complexity. Highlights include removal of Alt1 exclusion to simplify blueprint processing, incorporation of EIR Alt1 2035/2050 transit service increases, and refinement of Alt1 transit scaling parameters for 2035/2050. These changes enhance forecast accuracy, reduce maintenance overhead, and enable more robust planning conversations with stakeholders. Key technologies include blueprint processing, parameterization, and version-controlled change management (Git commits shown).
April 2025 — BayAreaMetro/NetworkWrangler: Focused on data hygiene and data integrity. No new features released this month; one critical bug fix improved data quality.
April 2025 — BayAreaMetro/NetworkWrangler: Focused on data hygiene and data integrity. No new features released this month; one critical bug fix improved data quality.
March 2025 monthly summary for BayAreaMetro/NetworkWrangler focused on delivering long-term infrastructure planning enhancements and sustaining high-quality planning discipline. The month delivered cross-horizon blueprint expansions and reclassifications that align with strategic capacity growth and reliability goals across 2030, 2040/2045, 2025/2040, and 2050 planning horizons.
March 2025 monthly summary for BayAreaMetro/NetworkWrangler focused on delivering long-term infrastructure planning enhancements and sustaining high-quality planning discipline. The month delivered cross-horizon blueprint expansions and reclassifications that align with strategic capacity growth and reliability goals across 2030, 2040/2045, 2025/2040, and 2050 planning horizons.
February 2025: Delivered Transit Project Integration Across Planning Horizons in BayAreaMetro/NetworkWrangler, aligning three new transit projects across planning horizons and updating network configurations. Achieved cross-horizon consistency and cleaned up data by removing a duplicate committed-project entry. This enhances long-range and near-term planning accuracy and reduces manual configuration drift.
February 2025: Delivered Transit Project Integration Across Planning Horizons in BayAreaMetro/NetworkWrangler, aligning three new transit projects across planning horizons and updating network configurations. Achieved cross-horizon consistency and cleaned up data by removing a duplicate committed-project entry. This enhances long-range and near-term planning accuracy and reduces manual configuration drift.

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