The new methodology used in the 2018 INRIX Global Traffic Scorecard INRIX Research allowed us to analyze not only congestion, but the severity of it. As part of this new approach, INRIX Research collected peak (slowest travel times) and inter peak (fastest point between morning and afternoon commutes) travel times. Together they provide a holistic account of congestion throughout the day. Using peak and inter-peak travel data, INRIX captures the range of outcomes the vast majority of car commuters experience. The following tables reveal how time loss varies throughout the commuting day. On average, drivers’ commutes increase by roughly 30% during peak versus inter-peak hours.
Los Angeles is the most car dependent of the Top 10 cities, meaning their road network has a greater exposure to commuter behavior. Whereas, in cities like Boston, Washington DC and Chicago, a higher proportion of trips are taken via public transportation, walking or cycling. The lower number of car commuters means the users roads in these cities are more likely to be business related trips (deliveries, taxis, ride share etc.). The result is the demand for road space is much more consistent throughout the day. In other words, there is less variation between peak and inter-peak periods in these cities. Los Angeles experiences a more severe drop in performance compared to other cities due to it having exceptional levels of automobile use relative to its density. LA originally developed around the streetcar and retains density levels characteristic of public transit cities. However, it turned heavily towards private automobiles, creating a hybrid city of moderate density and high automobile use.
Densest Big Cities in the U.S.
Peak versus inter-peak time loss is remarkably consistent across all cities independent of their geographic size or population density. The Top 10 cities for congestion in the US are amongst the country’s densest, while cities 11-20 have lower population densities. The consistency in travel time increases is due primarily to facility breakdown, which is when the number of cars trying to enter the road system overwhelms the network resulting in gridlock. Time delay increases by about the percentage across all cities going from inter-peak to peak hours.
Table 1 – Densest Cities
| 2018 Impact Rank (2017) | Urban Area | Inter-Peak Hours Lost (Rank 2018) | Peak Hours Lost (Rank 2018) | Percent Difference |
| 1 (1) | Boston, MA | 129 (2) | 164 (1) | 27% |
| 2 (2) | Washington, DC | 131 (1) | 155 (2) | 18% |
| 3 (5) | Chicago, IL | 113 (4) | 138 (4) | 22% |
| 4 (3) | New York City, NY | 96 (7) | 133 (5) | 39% |
| 5 (4) | Los Angeles, CA | 81 (12) | 128 (6) | 58% |
| 6 (6) | Seattle, WA | 121 (3) | 138 (3) | 14% |
| 7 (11) | Pittsburgh, PA | 101 (6) | 127 (7) | 27% |
| 8 (7) | San Francisco, CA | 86 (11) | 116 (9) | 35% |
| 9 (10) | Philadelphia, PA | 78 (15) | 112 (10) | 45% |
| 10 (8) | Portland, OR | 89 (10) | 116 (8) | 32% |
Least Dense Big Cities in the U.S.
Interestingly, Honolulu, Austin and Charlotte exhibit almost no variation between their peak and inter peak period, and Seattle shows less than normal variation. This is due to morning and afternoon peaks having grown into each other on major roads. Austin, Charlotte and Seattle are experiencing exceptional growth with investments in transportation trailing their growth. It is likely that long-term their peak versus inter peak variation will converge towards more typical variation levels as infrastructure ‘catches up’ and people adapt. Nashville displays exceptional volatility, likely attributable to its high rates of growth. Whereas its network did not previously enter severe states of facility breakdown, it has now reached a ‘tipping point’ where its network enters breakdown during peak hours. However, it still has enough capacity to recover between the morning and afternoon commutes. Nashville’s ability to enter breakdown and recover gives it an exceptionally volatile profile.
Table 2 – Less Dense Cities
| 2018 Impact Rank (2017) | Urban Area | Inter-Peak Hours Lost (Rank 2018) | Peak Hours Lost (Rank 2018) | Percent Difference |
| 11 (13) | Atlanta, GA | 80 (13) | 108 (11) | 34% |
| 12 (9) | Miami, FL | 77 (16) | 105 (12) | 37% |
| 13 (14) | Houston, TX | 79 (14) | 98 (14) | 24% |
| 14 (12) | Austin, TX | 104 (5) | 104 (13) | 1% |
| 15 (16) | Baltimore, MD | 71 (17) | 94 (16) | 32% |
| 16 (15) | Charlotte, NC | 95 (8) | 95 (15) | 1% |
| 17 (19) | Tampa, FL | 66 (19) | 87 (19) | 31% |
| 18 (17) | Honolulu, HI | 91 (9) | 92 (17) | 1% |
| 19 (18) | Denver, CO | 66 (18) | 83 (20) | 24% |
| 20 (23) | Nashville, TN | 51 (31) | 87 (18) | 73% |
In summary, major rates of growth reveal themselves in high volatility or almost none. Cities in a state of ‘congestion equilibrium’ exhibiting about a 30% differences between peak and inter peak. However, there is one notable outlier, Los Angeles. While Los Angeles is experiencing high rates of growth, its volatility is unusual, since unlike Nashville, traffic is a longstanding issue for the city. The variation is likely attributable to it being a ‘hybrid city’. In general, the percent increase in delays is largely constant across city types and sizes. No matter the city a commuter finds themself, getting stuck in rush hour increases a commute by the same proportion.