Public Transit & Infrastructure — Community-Led Transit Optimization

Overview / Context

Public transportation is the backbone of urban life — it connects people to jobs, schools, healthcare, and one another. Yet, in many cities, transit planning remains heavily top-down. Decisions about bus routes, stop placements, and service frequency are often made with limited community input, outdated ridership data, and little consideration of lived experience. The result is a system that too often fails the people who depend on it most: low-income families, students, seniors, and essential workers.

This project began with a deceptively simple question: What would happen if local communities could directly inform and co-design the public transit systems that serve them?
I led a research and design initiative focused on Community-Led Transit Optimization, aiming to make transit more responsive, equitable, and efficient by integrating citizen feedback, on-the-ground observations, and data-driven planning.

At the time, the city’s transportation department faced growing concerns: buses frequently ran off-schedule, riders struggled with unreliable transfers, and certain neighborhoods were underserved due to outdated population models. Meanwhile, local residents voiced frustration but lacked accessible channels to make their voices heard. My goal was to bridge this disconnect — to show that public participation could be more than a consultation checkbox; it could be an engine for smarter, fairer infrastructure planning.

Objectives

The overarching objective of this project was to reimagine public transit planning through human-centered, community-informed design.
Specifically, our goals included:

Empowering Residents: Create channels for citizens to directly contribute insights about local transit pain points.
Improving Efficiency: Use real-time feedback and data analytics to identify underperforming routes and optimize service schedules.
Advancing Equity: Ensure that historically underserved communities had a voice in infrastructure decisions and equitable access to transit.
Enhancing Trust and Collaboration: Strengthen partnerships between city agencies, transit operators, and community organizations.
Measuring Impact: Evaluate improvements in transit satisfaction, accessibility, and operational performance over time.

This initiative sought to blend quantitative optimization models with qualitative human experience, creating a blueprint for participatory transit design that could be scaled across the city.

Approach / Methods

The project unfolded over six months and combined data analysis, participatory mapping, ethnographic research, and prototyping. Our approach followed four main phases: discovery, co-design, data integration, and testing.

1. Discovery & Research

The project began with an in-depth field research phase, focused on understanding the lived realities of daily commuters.
I conducted over 60 semi-structured interviews with riders across different neighborhoods — from early-morning bus commuters to late-night shift workers. We also partnered with local organizations to distribute surveys in multiple languages, reaching residents who were less likely to engage through digital channels.

To complement these stories, we analyzed existing ridership and GPS datasets, revealing patterns of congestion, inconsistent headways, and route redundancy. This data triangulation — combining human stories with performance metrics — helped pinpoint both emotional and operational friction points.

Through this research, several key issues emerged:

Buses in low-income neighborhoods often ran less frequently and were more delayed.
Riders faced difficulty transferring between routes, especially during off-peak hours.
Existing city dashboards focused on metrics like "on-time performance" but ignored experiential metrics such as perceived reliability or safety.
Riders desired more transparent communication — not just route data, but updates that reflected their needs and feedback.

2. Co-Design Workshops

Next, we organized a series of community co-design workshops. Participants included residents, local business owners, transit advocates, and city officials. The workshops served as creative forums where participants could visualize their ideal transit systems using mapping exercises, route sketching, and storytelling sessions.

Each session focused on a key question:

What routes do you rely on most, and what challenges do you face?
If you could redesign a bus line, what would you change first?
How might we make transit more predictable, safe, and dignified?

Residents used color-coded stickers and physical maps to mark overcrowded stops, unsafe intersections, and missed connection points. These tangible artifacts were later digitized and layered onto the city’s GIS datasets, revealing patterns of inequity and inefficiency that had gone unnoticed by algorithms alone.

This phase was transformational. For many residents, it was the first time their insights were not only heard but visually represented in planning discussions. For city officials, it was an eye-opening reminder that quantitative optimization must be grounded in lived experience.

3. Data Integration & Modeling

After consolidating community feedback, we turned qualitative data into actionable insights. Working with the city’s data science unit, I developed an interactive transit optimization model that combined:

GPS and ridership data from the transit authority,
Crowdsourced rider feedback collected through workshops and SMS surveys, and
Open data on population density, demographics, and land use.

The model simulated potential route adjustments and predicted their impact on travel times, ridership, and service coverage. What made it unique was the inclusion of qualitative weighting factors — for example, prioritizing improvements in neighborhoods where residents expressed safety concerns or inadequate access to healthcare facilities.

Through iterative modeling, we identified several high-impact adjustments:

Reconfiguring one underutilized bus route to serve a growing residential area,
Adding an evening shuttle connecting two disconnected neighborhoods,
Adjusting stop placement for better alignment with community hubs like markets and schools.

4. Pilot Testing & Implementation

A six-month pilot was launched to test the proposed changes. Two modified bus routes and one microtransit service were implemented, with active community involvement in monitoring performance.

We developed digital feedback tools — including a mobile form and QR-code posters at bus stops — allowing riders to report delays, safety issues, or suggestions directly.
City planners received weekly summary dashboards integrating this real-time feedback with operational data.

Regular community meetings ensured transparency and accountability, giving residents updates on what changes were implemented and what feedback was still under review.

Outcomes / Impact

The pilot led to substantial improvements across multiple dimensions of transit experience and performance:

Ridership Growth: Average ridership increased by 18% on optimized routes within three months.
Service Reliability: On-time performance improved by 27%, particularly during peak hours.
Community Engagement: Over 1,200 riders provided feedback through workshops, surveys, and mobile tools.
Equity Advancement: Routes serving historically underserved neighborhoods saw the most improvement in accessibility.
Policy Integration: The city’s transportation department adopted the participatory model as a template for future planning cycles.

Perhaps most meaningfully, residents expressed a renewed sense of ownership over their public infrastructure. For many, riding the bus was no longer a source of frustration but a symbol of shared progress.

One resident commented,

“It’s the first time someone asked what we needed — and actually changed something because of it.”

These small yet powerful affirmations reflected the larger goal: transforming transit planning from a bureaucratic process into a community-centered dialogue.

Reflection / Lessons Learned

The Community-Led Transit Optimization project reinforced several core insights about designing for equity and sustainability in urban systems:

Human Experience Complements Data: Algorithms can reveal inefficiencies, but they cannot capture how a mother feels waiting for a delayed bus after dark. Real optimization must blend analytics with empathy.
Participation Builds Trust: When residents see their input reflected in real outcomes, civic trust grows. Transparency and feedback loops are as vital as the technical solution.
Small Pilots, Big Lessons: Starting with localized, community-driven pilots allows for faster iteration and builds momentum for system-wide change.
Design for Accessibility: Multilingual tools, mobile surveys, and visual mapping exercises expand participation and ensure no voice is left unheard.
Institutional Change Takes Time: Shifting from top-down to participatory planning requires cultural change within agencies — but once trust is built, the impact compounds.

Looking ahead, I hope to expand this initiative into a city-wide Participatory Mobility Framework, integrating new technologies like AI-assisted route prediction and real-time crowd density mapping while preserving the core principle: communities should lead the design of the systems that move them.

Closing Reflection

Public transit isn’t just about moving people — it’s about creating connections, dignity, and opportunity. This project showed me how technology and empathy can coexist to create infrastructure that listens, adapts, and serves equitably.
By centering community knowledge and pairing it with data-driven rigor, cities can build transportation systems that are not only efficient but truly human-centered — built with the people, not just for them.