2024 Eclipse - Traffic Case Studies in Congestion

Eclipse traffic episode #2 focuses on five metro areas and the duration/pattern of congestion following the eclipse. The information is built by using snap shots in time from Google Traffic starting at the end of the eclipse and continuing into the evening 12 hours later. While congestion was present across the path of totality in the USA, I pulled out five corridors in greater detail to assess the pattern of congestion building to the peak and then recovering over time, including:

·??????? San Antonio, TX was within totality but the centerline of totality was about 40 miles further west near Kerrville, TX and a large metro area (Houston) to the east which was outside totality

·??????? Russellville, AR was virtual on the centerline of totality, a smaller city that had a good weather forecast and had larger metro areas a few hours to the west (Tulsa 200 miles and Oklahoma City 250 miles)

·??????? Indianapolis, IN was fully within totality with a large metro area (Chicago) 165 miles away

·??????? Syracuse, NY was on the fringe of totality with several large cities to the south

·??????? Burlington, VT had a clear forecast leading up to April 8th with numerous metro areas outside of totality 200-300 miles to the south (Boston, Providence, Hartford, Springfield, Albany, New York).

Two key congestion metrics were evaluated. Duration and miles of congestion over a 12-hour period following the eclipse, at ? hour intervals. These were tracked on Interstate freeways that exited totality toward adjacent metro areas which were outside totality. Comparisons were done between “Live traffic” and “Typical traffic” in the “Traffic” layer in Google Traffic. Anything green in “Typical traffic” that was another color (yellow, red, maroon) was considered congested until the conditions matched between “Live” and “Typical”. Reliability of live travel times from Google were not found adequate as times resembled typical travel times rather than unique congested events (for example in Vermont three and four-hour travel times were noted when reality was 10 and greater). Therefore, the travel time feature was not utilized.

Hours of Congestion

The range of congestion duration varied from a low of 1.5-hours to a high of 12-hours. There appears to be three key factors that drive congestion duration.

1.????? Totality path coverage. If over large metro areas or the large metro areas are outside a three to four-hour drive reduces travel.

2.????? Weather forecasts. Clear skies drive more impromptu activity.

3.????? Freeway size and density. Greater network connectivity and more lanes without lane reductions reduce congestion. It seems travelers depend on the interstate system for longer trips from large metro areas to totality and the US routes are utilized sub-regionally (and seem to clear quicker, even though small towns are impacted).

Here is a ranked list of two dozen corridors monitored following the eclipse with their duration of congestion (in hours).

Springfield, MO US 60/63 Northbound 4

Dallas, TX I-35 Northbound 5

Branson, MO/Arkansas Ozarks US 65 5

San Antonio, TX I-10 Eastbound 6

Austin, TX SR 71 Eastbound 6

Memphis, TN I-555 Southbound 6

Cincinnati, OH I-74 Westbound 6

Columbus, OH US 33 Southbound 6

St. Louis, MO I-57/I-64 Northbound 6.5

Louisville, KY I-65 Southbound 7

Memphis, TN I-40 Eastbound 7.5

Niagara Falls to Toronto, Northbound 8

Toledo, OH I-75/I-475/US 23 Northbound 8.5

Cleveland, OH I-80/I-76 Eastbound 8.5

Pittsburgh, PA I-79 Southbound 8.5

Syracuse, NY I-81, Southbound 8.5

Russellville, AR I-40 Westbound 9

St. Louis, MO I-55 Northbound 9

Champlain, NY I-87 Southbound 9.5

Indianapolis, IN I-65 Northbound 10

Burlington, VT I-89 Southbound 10.5

Nashville, TN I-24 Southbound 11

Newport, VT I-91 Southbound 11

Johnsbury, VT I-93 Southbound 12

Length of Congestion

The miles of congestion for these corridors were also monitored. This was measured off Google Maps using Google Traffic and comparisons between “Live traffic” and “Typical traffic” for a Monday. The peak congestion was the ? hour snapshot with the most congestion. All of the longest congestion occurred on corridors where large metro areas were outside totality. Here are the same corridors with the peak congestion shown in miles and their peak congestion time (shown as Pacific Time for comparison).

Austin, TX SR 71 Eastbound 10 2PM

Dallas, TX I-35 Northbound 17 2PM

Branson, MO/Arkansas Ozarks US 65 22 1:30PM

Cincinnati, OH I-74 Westbound 24 2PM

Memphis, TN I-40 Eastbound 25 6PM

Springfield, MO US 60/63 Northbound 30 2PM

San Antonio, TX I-10 Eastbound 38 2PM

Johnsbury, VT I-93 Southbound 38 9PM

Louisville, KY I-65 Southbound 39 1:45PM

Columbus, OH US 33 Southbound 39 2:30PM

Memphis, TN I-555 Southbound 40 2:30PM

Niagara Falls to Toronto, Northbound 59 2PM

Pittsburgh, PA I-79 Southbound 60 5PM

Indianapolis, IN I-65 Northbound 67 6PM

St. Louis, MO I-57/I-64 Northbound 70 2PM

Russellville, AR I-40 Westbound 72 2:30PM

St. Louis, MO I-55 Northbound 78 2PM

Cleveland, OH I-80/I-76 Eastbound 78 1:45PM

Toledo, OH I-75/I-475/US 23 Northbound 85 1:45PM

Nashville, TN I-24 Southbound 93 2PM

Syracuse, NY I-81, Southbound 93 3PM

Burlington, VT I-89 Southbound 93 6PM

Newport, VT I-91 Southbound 100 4PM

Champlain, NY I-87 Southbound 120 3PM

Pattern of Congestion over Time

The duration of congestion was evaluated for the five case studies. The plots of the congestion duration are in the graphic above this article. There were two basic patterns that emerged:

·??????? A 1.5 to 2-hour rise to peak congestion followed by a 5 to 7-hour dissipation period.

·??????? A longer rise to peak congestion – 2.5 to 5 hours – with a 5 to 8-hour dissipation period.

The first pattern was present in San Antonio (from Kerrville on I-10) and Russellville (toward Tulsa and Oklahoma City on I-40). In both these cases the plume of traffic migrated toward the metro area in the later hours of recovery until the freeways reached system interchanges with other freeways.

The second pattern in Syracuse (toward Binghamton and Scranton on I-81), Indianapolis (toward Chicago on I-65) and Vermont (collectively I-87, I-89, I-91 and I-93) has longer duration to peak congestion and the peak occurred with long downstream plumes of traffic seeking to reach adjacent large metro areas. When congestion did peak, it took just as long to recover as the first pattern (in Vermont, a little longer).

The difference between the two seems to be the length from the large metro areas and the freeway density of intersecting freeways that disperse traffic to those metro areas. In the Russellville example, where the split to Tulsa and Oklahoma City occurred, that added system capacity dispensed with the congestion. In the case of Vermont, the plume of congestion continued southward from the Burlington area over time. In Franconia Notch, due to the single lane interstate, congestion was persistent into the early morning hours and trip durations double to triple normal to the Boston, Hartford, Providence, and Springfield.

Personal Notes

I had planned to travel to El Paso, Texas from Portland, Oregon and drive on April 7 for four hours to Midland then drive 4 hours to Kerrville the morning of the eclipse. Unfortunately, all the forecasts were saying thunderstorms and clouds. That was just too much a risk for all that travel and costs and I did not go (sadly). However, the strategy to avoid congestion was brilliant. On April 8, heading west out of Kerrville following the eclipse there was almost no congestion given that the largest metro area was 8 hours away and most of the communities to the west were much smaller. I-10 and I-20 were able to handle those traffic loads easily. This points strongly to the relationship between large metro area outside totality within a reasonable driving distance as the central congestion factor, as many other routes on the national highway system were not nearly as congested as the ones presented above.

Summary

The length of congestion following the eclipse was significant in many metro areas. Some, where totality passed directly over large metro areas, experienced less congestion where there were no large adjoining metro areas which were outside totality within a reasonable drive (less than 3 to 4 hours). Dallas and Austin in Texas were examples of this. Others areas experienced severe congestion over long durations (Burlington, Vermont) where the plume of congestion migrated toward the large metro areas outside of the totality zone. Recovery times varied the same way from a rapid rise to peak congestion followed by a slower recovery. Other situations the rise to congestion was delayed due to based on the distance of the metro area from the totality zone.

Next up is the morning after – what happened on Tuesday April 9th in the AM peak.