Predicting World Cup traffic: What mobility data reveals about congestion, risk, and travel demand 

Key takeaways

World Cup 2026 will reshape traffic patterns across host cities
A surge of global travel demand across 16 host cities and 104 matches will strain roadways, transit systems, and parking infrastructure over a condensed time period.

Mobility data reveals where congestion and risk will emerge
Insights from trip volume, driving behavior, and congestion patterns help organizations anticipate shifting travel demand and proactively respond to evolving safety and traffic risks.

Arrival waves and bottlenecks drive peak congestion
Fans arrive hours before kickoff, creating extended congestion windows, with road speeds near venues dropping sharply as localized bottlenecks form.

Each host city has a unique driving fingerprint
Differences in infrastructure, transit access, and driver behavior mean congestion, safety risks, and travel demand will vary widely across markets like Los Angeles, New York/New Jersey, Miami, and Dallas.

Mobility intelligence enables better planning across industries
Public agencies, retailers, and auto insurers can use mobility data to inform traffic management, optimize operations, and adapt to changing risk and consumer behavior during mega-events.

Introduction

Every four years, the World Cup brings millions of fans together from across the globe. The scale and complexity of this year’s event is unmatched:

• 11 U.S. cities
• 2 Canadian cities
• 3 Mexican cities
• 104 matches

This mega event, held over the course of just a few weeks, entails millions of fans converging on airports, train and bus stations, and roadways to get to and from stadiums.

Host cities face multiple transportation priorities:

• Scaling public transit capacity (timing, routes) and reliability (maintenance, response to breakdowns)
• Managing traffic congestion, parking issues, curb space (drop off and pick up, public transit stopping locations), ride-hailing zones, and impact to local businesses
• Coordinating road safety, security, closures, and ongoing construction projects that might impact routes and roadways
• Supporting ongoing transportation needs such as deliveries and day-to-day travel

Businesses and auto insurers face their own challenges:

• Changes in traffic patterns and driving behaviors may affect existing risk and safety profiles
• Shifts in commuting or drive-by behaviors could impact existing retail visit rhythms, visitor profiles, and customer needs

The role of mobility data

This intractable transportation challenge isn’t just a logistics problem; it’s an awareness problem. Traditional data sources such as crash reports show where issues already exist but fail to highlight where risk may be building.

However, with mobility data insights such as trip volume, congestion patterns, and driving behavior signals, organizations such as transportation agencies, retail locations, QSR, and auto insurers can better understand where and when travel demand – and risk – may be changing and respond more proactively.

About Arity, a mobility data and analytics company

Arity’s mission is to use mobility data to enable more precise pricing, planning, and performance for businesses and city agencies. We analyzed our vast dataset to explain how people move during major events and moments of disruption.

Using Arity’s privacy-safe, aggregated and de-identified Roadway Insights and real-time data solutions, our data analysts studied two Copa América 2024 group matches, the Copa América Final, and two NFL regular-season games to understand traffic and driving behavior patterns during mega-events.

The mobility reality of mega events

Upon analyzing our dataset, two insights stood out.

Arrival waves build hours before kickoff (not just at start time)

Arrival windows for sporting events typically spread out between four to six hours pre-match thanks to tailgating culture and the greater distances driven by fans.

Road speeds can drop dramatically near venues

The data also shows how quickly roads near a venue can slow as crowds arrive. For example, during Sunday Night Football at MetLife, average speeds on nearby bottleneck roads dropped from 44 mph on a comparable non-event Sunday to 20 mph.

Key mobility pressure points for host venues

Arity’s analysis suggests World Cup host markets should pay close attention to these potential congestion, safety, and demand risks:

• When fans begin arriving
• Where roads are most likely to slow
• How to absorb the increased demand
• The impact of international visitors on driving behavior patterns

Cities will need to consider these factors with discrete approaches because each host city has a different “driving fingerprint.”

Interpreting travel conditions across four World Cup venues with mobility intelligence

Every host stadium and city has a distinct transportation personality – its “driving fingerprint” – shaped by its infrastructure, volume, and typical driving behavior. Understanding these patterns can help organizations anticipate where congestion, safety risks, and demand shifts may emerge.

Using Arity’s Roadway Insights solution, our analysts:

• Modeled a baseline “driving fingerprint” using driving behaviors (distraction, acceleration, braking, and speed)
• Created a “transit accessibility score” based on rail proximity, airport-to-venue time (including transfers and walking time), and the number of event-day transit modes

Los Angeles: Congestion concentration risk

SoFi Stadium, Inglewood, California

Driving fingerprint

Planned World Cup transportation approach

SoFi Stadium has limited parking and relies on distributed park-and-ride locations and shuttle systems to move fans to and from the stadium. Large events typically create high congestion and slower recovery to typical traffic patterns.

Vehicles

Pre-purchased parking will be limited, with policies designed to discourage on-site driving and reduce congestion near the stadium.

Public transit

The transportation plan emphasizes expanded bus service and coordinated regional transit, with light rail connections feeding into shuttle systems for final access to the stadium.

Potential match-day traffic patterns using mobility intelligence

• Based on historical event patterns for big events, congestion may build well ahead of match start times and persist beyond match completion.
• Elevated stop-and-go conditions may lead to higher rates of braking and acceleration events on surrounding roadways.
• Reliance on distributed hubs and shuttle transfers may concentrate demand in specific locations, creating localized bottlenecks and variability in arrival timing.

New York / New Jersey: Transit dependency at scale

MetLife Stadium, East Rutherford, New Jersey

Driving fingerprint

Planned World Cup transportation approach

The region relies heavily on already strained commuter rail and bus systems, with remote parking and transfer points also playing a central role in stadium access.

Transit systems can experience variable demand based on pricing, capacity, and scheduling constraints.

Vehicles

Parking will be unavailable at the stadium. Designated off-site parking and rideshare hubs will support vehicular access to the matches.

Public transit

Plans emphasize expanded rail and bus service, with coordinated scheduling to manage peak match-day demand.

Potential match-day traffic patterns using mobility intelligence

• Travel demand may concentrate into pre- and post-match windows, particularly along key rail corridors.
• Variability in travel patterns may be driven by transit pricing and mode options.
• Capacity constraints at major transit hubs could contribute to bottlenecks, while surrounding communities might see spillover traffic from parking and rideshare activity.

Miami: Distributed access

Hard Rock Stadium, Miami Gardens, Florida

Driving fingerprint

Planned World Cup transportation approach

Hard Rock Stadium, with limited direct transit access, relies on distributed regional hubs and connecting transportation options.

Vehicles

Pre-purchased parking is expected to be limited, with policies intended to reduce on-site traffic and encourage alternative transportation modes.

Public transit

Multiple regional transit hubs, integrated with rail and bus systems where available, will connect fans to the stadium by shuttle bus.

Potential match-day traffic patterns using mobility intelligence

• Travel patterns may be more distributed, with a mix of direct vehicle trips and hub-based journeys.
• Variability in access and capacity at transit hubs may introduce uncertainty in arrival timing, uneven concentrations across locations, and potential hub bottlenecks. Parking demand will shift towards these outer zones, farther from the stadium.

Dallas: Car-dependent pressure on limited infrastructure

AT&T Stadium, Arlington, Texas

Driving fingerprint

Planned World Cup transportation approach

AT&T Stadium has no direct rail access, resulting in heavy reliance on personal vehicles and buses. Parking capacity near the venue is limited.

Vehicles

Pre-purchased parking is expected to be limited, with policies intended to manage congestion and reduce last-minute vehicle access.

Public transit

The transportation plan relies on a train-to-bus transfer model, with increased transit capacity and frequency on key routes.

Potential match-day traffic patterns using mobility intelligence

• Highway corridors near the stadium may experience significant congestion during peak arrival and departure periods.
• The reliance on transfer-based transportation solutions may introduce variability in arrival timing, particularly during peak demand windows.
• Reliance on personal vehicles may concentrate traffic closer to the venue compared to more transit-oriented markets.

Why understanding World Cup traffic patterns matters for industries and municipalities

Various sectors can use mobility data for better planning around the World Cup – or any large event that changes the usual calculus for risk and safety on our roadways.

• Public sector: Mobility data can support planning by helping inform traffic flow, closures, and emergency response strategies, while providing additional context for transportation planning before, during, and after large-scale events.
• Retail businesses (QSR, fuel, and convenience stores): Mobility data can help leaders plan for surges in demand, supporting decisions around staffing, inventory, operating hours, and customer engagement based on observed movement patterns.
• Auto insurers: Mobility data can help identify potential risk patterns across time and location, informing pricing precision, claims preparedness, and the monitoring of driving behavior trends within specific geographies.

From disruption to decision advantage

Large-scale events like the World Cup reshape how people move, often in ways that traditional planning alone cannot fully anticipate. Mobility data provides a clearer view into these shifts, helping organizations prepare for changing traffic patterns, evolving risk profiles, and dynamic consumer behavior during this global tournament.

By grounding strategic decisions in data, businesses and public agencies can respond more effectively as conditions change rapidly and keep all the drama and excitement off the roadways and on the soccer pitch, where it belongs.