The bridge collapse in March 2024 instantly removed one of Baltimore’s most important links for commuters, freight, and port-bound traffic. Emergency detours went into effect within hours, and public messaging followed soon after. But the bigger questions took longer to surface: Where did all that traffic actually go? How much longer are people spending on the road? And which neighborhoods are absorbing the worst of it?
Answering those questions with evidence required two kinds of information: network-wide mobility data that shows how traffic was redistributed across the region, and qualitative input from residents and workers about how their daily lives changed. This research area integrates both to create a holistic picture of how residents were affected.
Regional Mobility Data
The team used large-scale, anonymized traffic data from platforms including the Regional Integrated Transportation Information System (RITIS) to track how speeds and travel times shifted across the Baltimore freeway and arterial network after the collapse.
The analysis compared three time windows:
- Pre-collapse — normal baseline conditions
- Immediate aftermath — roughly the first month after the collapse
- Mid-term — conditions approximately one year later
To isolate the bridge’s effect from day-to-day noise, the team focused on multiple incident-free weekdays within each window rather than single snapshots. This approach reveals:
- New bottlenecks that formed as traffic shifted away from the Key Bridge corridor
- Routes that improved as drivers redistributed or agencies adjusted operations
- How much additional time travelers are spending on the road during a typical commute — not just during one-off incidents
These patterns give transportation agencies a network-wide view of where the system is coping and where targeted intervention would do the most good.
Community Travel Behavior Survey
To understand how the collapse affected residents’ travel patterns, the team designed a regional survey focused on travel behavior and experience.
The survey reached residents and workers in Baltimore City and surrounding counties, especially those whose commutes, errands, and freight-related jobs were most likely disrupted. To get a broad sample, the team used a combination of outreach methods, including:
- Paper flyers at shopping plazas, gas stations, bus stops, and convenience stores
- Geo-targeted online posts reaching people in the affected areas
- Church and congregation partnerships to connect with residents who might not see digital outreach
Data collection ran from mid-May through mid-July 2024 and produced 222 valid, usable responses after cleaning.
Survey Snapshot
- ~90% reported increased traffic congestion in their daily travel
- 40% now leave home earlier for work or school
- 53% report higher travel expenses since the collapse
- 32% expressed reluctance to live or work near bridges in the future
Early Findings
Detailed scientific findings will appear in forthcoming research publications. At a high level, the combined mobility data and survey responses are beginning to reveal three important patterns.
I. Rerouting reshaped the network in uneven ways.
Many drivers shifted to alternate bridges, tunnels, and local streets. That created new congestion hot spots, especially particularly at the I-95 and I-895 tunnel approaches. Surprisingly, this diversion actually eased pressure on some segments that had previously carried Key Bridge overflow traffic.
II. The burden is focused in particular neighborhoods
Some workers and neighborhoods face significantly longer commutes, higher fuel costs, and more day-to-day uncertainty. Hourly workers, single-car households, and residents of communities closest to the bridge have the fewest alternatives and the least flexibility to adapt.
III. People are adapting, but at a cost.
Residents are responding in a range of ways: changing routes, leaving earlier, consolidating trips, exploring transit, and in some cases cutting travel entirely. But survey respondents described real frustration, fatigue, and financial strain.
