Transit Triumph or Traffic Tangle?
Evaluating Los Angeles’ Transit Equity for the 2028 Car-Free Olympics.
Hot Off the Press: LA’s Bold ‘Car-Free’ Olympic Plan
As headlines buzz with optimism and skepticism, Los Angeles aims to make history with a "Car-Free Olympics." But can the city—long hailed as the "car capital" of the U.S.—truly overcome its traffic woes?
Our StoryMap invites you to go behind the headlines and explore whether LA’s ambitious plan can deliver equitable and efficient transit for everyone.
When Mega-Events Go Wrong: Müller’s 7 Syndromes
Big events, big promises—yet sometimes, big disappointments. Planning scholar Martin Müller warns that mega-events, like the Olympics, can overpromise, overspend, and overlook the everyday needs of local communities.
From bloated budgets to neglected neighborhoods, these pitfalls show how cities can inadvertently deepen inequalities—especially in public transit access.
Will LA28 be any different?
Measuring Accessibility: Enter the 2SFCA Approach
So if mega-events risk leaving certain communities behind, how do we measure that risk—especially in a sprawling metropolis like Los Angeles? This is where 2SFCA (the Two-Step Floating Catchment Area) method comes in.
Think of dropping a pebble into a pond: the ripples represent each facility’s reach, and the people within those ripples are its potential users.
By calculating how many facilities (or seats, or resources) each community can realistically access, we get a tangible measure:
Who gets served, and who doesn’t?
Breaking Down Equity: Spatial & Social
In urban studies, fairness in transit typically shows up in two ways:
- Spatial Equity: Do different neighborhoods enjoy similar levels of service or are some left out?
- Social Equity: Are vulnerable groups (low-income residents, seniors, people with disabilities) able to benefit equally from new transit lines or facilities?
By applying 2SFCA, we can spot where these inequities arise—and see whether LA28’s plan is truly inclusive or just another big promise.
Understanding the Journey
Here’s our workflow:
- Gathering the Data Everything starts with Los Angeles’ GTFS data for Metro rail and citywide bus lines, loaded into ArcGIS Pro for a precise look at potential transit routes and travel times. By incorporating each Olympic venue as a focal point, we can begin mapping real-world accessibility.
- Measuring Accessibility with 2SFCA Next, we used the Two-Step Floating Catchment Area method to calculate an “Accessibility Index” (AI) for each Census Tract. We set each venue’s supply at 10,000 seats and assumed every resident is a potential demand, with a 60-minute cutoff for travel via public transport. This step reveals who can realistically reach an Olympic venue—and who might be left out.
- Diving into Inequality Accessibility alone doesn’t tell the full story. To see how certain groups fare, we ran a Weighted 2SFCA for four key demographics—Disability, Seniors (65+), Poverty, and Race—then used tools like the Gini Coefficient, Lorenz Curve, and Local Moran’s I. These measures spotlight how evenly or unevenly public-transit access is distributed across different communities.
- Conclusions and Policy Insights Finally, by merging these findings—where and how accessibility falls short—we can propose actionable policies. From infrastructure upgrades to fare subsidies and multilingual resources, the ultimate goal is an LA28 that truly serves all Angelenos.
Where, How, Who?
Building the Puzzle of LA28 Accessibility
WHERE: The Olympic Venues
The LA28 Games will spread across multiple competition venues, each with unique seating capacities, sports events, and surrounding neighborhoods. Think of these venues as the “destination points” in our accessibility puzzle. Whether you’re cheering at a legacy stadium or a newly upgraded arena, each location needs to be reachable by public transit if Los Angeles hopes to keep its “Car-Free” promise.
Click on the Olympic icon above any venue to discover its name, the Olympic events it will host, and the Paralympic sports featured there.
Keep exploring the map for more venue details, or follow the link below to dive deeper into the excitement of LA28!
HOW: The Metro & Bus Network
Enter LA Metro and its extensive bus lines. Though Los Angeles is often dubbed the “car capital”, the city does have a robust (and growing) transit system. Metro lines crisscross the region, supplemented by bus routes reaching further into local communities. These networks form the “pathways” we’ll analyze for getting everyday residents—and visitors—reliably from point A to point B.
Click the link to explore detailed information about all the transit lines.
Azusa - Long Beach
Union Station to North Hollywood
Norwalk to Redondo Beach
Union Station to Wilshire/Western
East Los Angeles to Santa Monica
Expo/Crenshaw to Westchester/Veterans
WHO: The People Behind the Commute
We’re assuming everyone in Los Angeles County might want to attend an event or at least travel to a venue during the Olympics. Each Census Tract represents a slice of the population—families, students, seniors, workers—who all have different schedules and transit needs. In this study, we’re treating those neighborhoods as “demand points”, asking: Can public transit serve them within a reasonable travel time?
Exploring the Reach: Mapping the Service Areas
Before diving into 2SFCA, let’s see how far LA’s transit can really take you. Using ArcGIS Pro’s Network Analysis, we combined GTFS data for Metro rail and buses with LA’s road network to build a comprehensive travel model. We then ran a Service Area analysis with a 60-minute cutoff, assuming people are willing to commute up to one hour during the Games.
The 60-minute cutoff is a widely recognized benchmark for public transit planning in large metropolitan areas. In a sprawling region like Los Angeles—especially under the heightened demand of the Olympics—a one-hour window provides a reasonable, yet conservative, estimate of what most residents might be willing to endure for a single trip.
It accounts for typical peak-hour congestion, potential delays, and the added crowds a global event can bring, ensuring the model remains both practical and grounded in real-world conditions.
On the map, you’ll see green-shaded regions indicating neighborhoods from which residents can reach an Olympic venue within 60 minutes by first walking to the nearest bus or rail stop, then transferring if needed. Meanwhile, the gray base shows the underlying network we used to calculate these reachable zones. It’s the backbone of LA’s transit system—and the starting point for our deeper 2SFCA exploration.
Unpacking the Scores: A Closer Look at Accessibility
After generating the service areas for each Olympic venue, we used a Two-Step Floating Catchment Area (2SFCA) method to calculate public-transit accessibility for every Census Tract in Los Angeles.
As you can see on the map, we categorized these scores into four levels—“No Accessibility” (0), “Low” (≤10), “Medium” (≤30), and “High” (≤100).
Overlaid on this layer are the locations of all LA28 Olympic venues, plus city-wide bus and Metro lines, giving a sense of how proximity and the supply-demand balance affect each tract’s accessibility.
Adding a Fairness Dimension
To explore equity, we applied a weighted 2SFCA formula, incorporating different demographic variables:
- (below 100% of the federal poverty line)
- (65+)
- Race (, , , )
By weighting tracts based on these populations, we can see not just overall accessibility, but also how it might vary for specific communities.
Notice how some areas—particularly around major bus and rail lines—consistently have better scores, while more distant or underserved neighborhoods tend to rank lower.
This comparison hints at possible disparities in public-transit access and points toward where additional resources or service improvements might help.
What Stands Out?
As you explore, keep an eye out for patterns:
Are there tracts that appear highly accessible in the base map but drop in certain weighted layers?
Conversely, do some areas show a particular need when you factor in poverty or age?
Highlighting these differences will help illustrate how planning decisions could be made with an eye to equitable access for all.
Equity in Focus: Weighted 2SFCA
After calculating accessibility indices (AI) for different demographic groups, the next step is to see how fairly these resources are distributed across Los Angeles. That’s where the Lorenz Curve and Gini Coefficient come in.
Why Lorenz and Gini?
- Lorenz Curve: This curve shows the cumulative share of accessibility on the vertical axis versus the cumulative share of Census Tracts on the horizontal axis. The “Perfect Equity” diagonal indicates a completely equal distribution: every tract would receive the same share of accessibility.
- Gini Coefficient: Ranging from 0 to 1, it measures how far the Lorenz Curve deviates from perfect equality. A lower value (closer to 0) signals greater equity, while a higher value (closer to 1) points to less equity.
Reading the First Chart
In the chart, you’ll see four Lorenz Curves, each representing:
- Overall AI (Unweighted)
- AI Weighted by Poverty
- AI Weighted by Disability
- AI Weighted by Age (Seniors)
Each curve’s Gini Coefficient (shown in the legend) quantifies how evenly (or unevenly) accessibility is shared. Notice how the unweighted AI compares to the weighted versions. Where the curves bend more sharply away from the diagonal, the Gini is higher, indicating that certain tracts receive disproportionately less accessibility relative to others.
To extend the equity conversation further, we’ll also look at how accessibility differs among White, Latino, Black, and Asian communities. The next chart follows the same logic, plotting one Lorenz Curve per racial group. These findings highlight that equity in accessibility isn’t uniform and can vary significantly by community.
Pinpointing Disparities: Local Moran’s I
While the Gini Coefficient reveals the overall level of inequality, we still need a geographic lens to determine where these disparities are most pronounced. Using Local Moran’s I, we identified clusters—or “hot spots” and “cold spots”—of high or low accessibility across Los Angeles.
Local Moran’s I acts like a geographic magnifying glass, highlighting neighborhoods that either form strong clusters or stand out as outliers. This spatial detail is crucial for policymakers: it pinpoints “where” interventions would be most impactful. For instance:
High-High Clusters
Tracts with high AI surrounded by neighbors that are also high. These regions often form “cores” of relative accessibility, where multiple adjacent neighborhoods enjoy better transit options or infrastructure.
Low-Low Clusters
Tracts with low AI whose neighbors are also low. These areas can signal deeper pockets of underinvestment or limited transit resources, potentially creating a significant barrier for residents with fewer economic means.
High-Low Outliers
A tract with high AI next to tracts that are predominantly low. Such outliers may reflect localized improvements—like a new bus line or transit hub—that haven’t yet reached neighboring communities.
Low-High Outliers
A tract with low AI surrounded by higher-scoring neighbors. These can point to small islands of disadvantage, where residents struggle to access transit even though nearby tracts fare better.
In addition to the layer shown above, we’ve also computed Local Moran’s I for , , and Race(, , , ).
Five Categories of Local Moran's I
Each map highlights distinct patterns—whether Low-Low pockets of disability access or High-High clusters in predominantly senior neighborhoods—shedding light on how geographic disparities play out across multiple dimensions.
From Insight to Impact: Policy Pathways for an Equitable LA28
Our layered analysis—spanning the Weighted 2SFCA, Gini Coefficients, Lorenz Curves, and Local Moran’s I—reveals where and why transit accessibility falls short for different demographic groups. Now, we can transform these insights into a roadmap of targeted, data-backed strategies. By pairing maps and metrics with real-world evidence, we ensure these solutions aren’t just theoretical—they’re actionable steps toward a more inclusive 2028 Olympics and beyond.
Strengthen Public Transit Networks
Local Moran’s I pinpoints High-Low or Low-Low tracts in predominantly Latino, Asian, or low-income areas, revealing urgent transit gaps that can be addressed by:
- Microtransit & Flexible Services In neighborhoods labeled “High-Low”—where one tract maintains adequate access but surrounding tracts do not—on-demand shuttles or last-mile van services can bridge the gap.
- Frequency & Route Optimization Data from the Weighted 2SFCA “hot spots” can guide decisions about increasing bus or rail frequency. Demonstrating a revised timetable—where buses arrive more frequently in identified corridors—can underscore how even minor schedule tweaks significantly raise AI scores for underserved communities.
Build Inclusive Infrastructure
Low-Low clusters often signal neighborhoods with higher proportions of seniors, people with disabilities, or low-income households who face significant transit challenges:
- Barrier-Free Transport In these priority areas, installing low-floor buses, adding tactile paths, and improving wheelchair ramps can dramatically expand mobility options. For instance, a Disability-Weighted Moran’s I map may highlight “hotspots” where universal design features have the potential to create the greatest impact.
- Venue Accessibility Enhancements Olympic venues and surrounding streetscapes can be equipped with braille signage, audio announcements, and accessible seating. Even modest design updates—such as clear wayfinding for visually impaired visitors—can transform how these spaces are experienced by everyone.
Foster Cross-Agency Collaboration & Language Services
When the race-based analyses reveal lower accessibility among Latino or Asian populations—especially in Low-Low clusters—language and cultural barriers often compound the problem:
- Community Partner Networks Partner with local cultural centers, nonprofits, and faith-based organizations to co-design multilingual apps, signage, and outreach materials. Sharing brief video testimonies or photos from a pilot initiative in these neighborhoods can help readers see the tangible benefits of inclusive collaboration.
- Multilingual Guidance Hubs During the Olympics, setting up help desks or information kiosks in multiple languages ensures visitors and locals can navigate public transit confidently. Referencing the Race-Weighted AI Lorenz Curve or “Low-Low” cluster maps underscores how critical it is to provide language support where transit gaps are most pronounced.
Reference
Martinez, E. (2024, November 3). Angelenos skeptical about the pressure 'car-free' Olympics will put on public transit.University Times. Retrieved from https://csulauniversitytimes.com/olympics-public-transit/
Müller, M. (2015). The Mega-Event Syndrome: Why so much goes wrong in Mega-Event planning and what to do about it. Journal of the American Planning Association, 81(1), 6–17. https://doi.org/10.1080/01944363.2015.1038292
Perelman, R. (2024, October 7). Los Angeles 2028: War of words heats up on “car-free” 2028 Olympics plan, from Orange County Supervisor: Activist “pipe dream.”The Sports Examiner. Retrieved from https://www.thesportsexaminer.com/los-angeles-2028-war-of-words-heats-up-on-car-free-2028-olympics-plan-from-orange-county-supervisor-activist-pipe-dream/
Quinton, G. J. (2024, August 12). The countdown is on for the 2028 Olympics. Here's where LA stands on key transit projects.LAist. Retrieved from https://laist.com/news/transportation/la28-olympics-transportation
Xia, Y., Chen, H., Zuo, C., & Zhang, N. (2022). The impact of traffic on equality of urban healthcare service accessibility: A case study in Wuhan, China. Sustainable Cities and Society, 86, 104130. https://doi.org/10.1016/j.scs.2022.104130
Zagorsky, J. L. (2024, August 24). Will the ‘Car-Free’ Los Angeles Olympics work?WIRED. Retrieved from https://www.wired.com/story/how-the-car-free-los-angeles-olympics-will-work/
