The power of collaboration: How we can reduce traffic congestion

Network-aware navigation can reduce citywide congestion by rerouting a small share of vehicles away from heavily overloaded roads. In a six-month experiment across 10 U.S. cities, altering routes for fewer than 2% of trips increased speeds, lowered fuel consumption, and reduced emissions across the broader road network. The results suggest that navigation apps can evolve from optimizing individual trips to coordinating traffic for system-wide benefit.

Experiment Design

• Google Maps routing was modified to favor alternative routes with similar travel times and road characteristics.
• Approximately 100 historically congested road segments were selected in each city.
• The study used a citywide switchback design, alternating between standard and modified routing on consecutive days.
• Unlike experiments that randomly alter individual trips, the intervention was applied systematically across each city.
• Fewer than 2% of observed trips received changed recommendations.

Measurable Traffic Improvements

• Targeted congested segments experienced a median speed increase of about 2%.
• Fuel consumption rates on targeted segments fell by approximately 0.5% to 1%.
• Across all affected segments—including roads receiving diverted traffic—median speeds increased by about 0.35%.
• During morning and afternoon peaks, speeds improved by roughly 0.5%.
• The estimated impact could save thousands of tons of CO2e emissions per city each year.

Dispersing Traffic More Efficiently

• The intervention shifted vehicles away from major bottlenecks and distributed them across a larger number of peripheral roads.
• Alternative roads absorbed additional traffic without suffering comparable congestion because the volume increase was spread out.
• In Atlanta, for example, traffic was diverted from a central highway to a more distributed network around the city.
• Both navigation users and non-users benefited from reduced congestion on shared roads.

Analytical Approach

• Researchers used hierarchical Bayesian outcome modeling.
• The model estimated effects at both citywide and hourly local levels.
• Information was shared across cities and time periods, helping produce more reliable estimates for individual locations and time windows.
• Improvements in speeds and emissions were statistically significant across the network.

The study demonstrates that even limited, strategically coordinated rerouting can produce broad public benefits. Navigation platforms, connected vehicles, and smart-city systems could build on this approach to support dynamic traffic-signal control and real-time network optimization.