I-85 Bridge Collapse Dataset

In a metro region of 6 million, easily a third of the population could feel the effects. After the smoke (literally) cleared, regional decision-makers and Georgia DOT had a lot of questions about who those quarter million people were, where they come from, and where they’re going. ARC partnered with Citilabs to leverage Streetlytics data to understand the origins and destinations of typical morning and evening peak hour trips that would have crossed the bridge. This allowed the city and the State to make better decisions about alternative routing, road closures, detour planning, and communications. The purpose of this interactive visualization was to better illustrate how to understand who would be impacted by a potential road closure/narrowing and how to best plan for it. As such, an ArcGIS online story map was created to demonstrate the impact. As illustrated, the affected area on I-85 is a critical link in the regional transportation network. In the morning travel period, trips routinely flow from as far south as Newnan and from as far north as Cumming.

As a result of the I-85 bridge collapse, planners were able to take away new ideas for how they can better serve their communities by considering how disasters impact populations, as well as determining where they travel to and from. The process of studying transportation disaster impacts has now been demonstrated, and this case study with accompanying datasets are now available for future research. The Atlanta Regional Commission (ARC) learned a lot from this unfortunate event, such as how to better analyze effective connections between specific origin-destination pairs, and most important, develop a resiliency framework plan to better account for roadway network redundancy, multi-modal travel alternative diversity, and roadway network spare residual capacity planning.

Data Collection Agency

ARC prepared this dataset as part of the Federal Highway Administration’s second Strategic Highway Research Program (SHRP2).

Incident Background

From the National Transportation Safety Board’s Highway Accident Brief for Accident Number HWY17IH012:

“On Thursday, March 30, 2017, about 6:05 p.m., construction materials stored under an Interstate 85 (I-85) overpass in Atlanta, Georgia, were set on fire. The fire propagated throughout the storage area. Just over 1 hour later, at 7:14 p.m., span 30 NB – a 92-foot-long elevated span of I-85 – collapsed. No fatalities or injuries were reported from the fire and subsequent bridge collapse. One person was arrested and later charged with criminal damage to property. The Georgia Department of Transportation (GDOT) had been using the area for storage for 76 reels of high-density polyethylene conduit and nine racks of fiberglass conduit. The materials were left over from an earlier project of State Route 400 (SR-400) and were secured inside a chain-link fence.

The interstate bridge is located over Piedmont Road, 4 miles northeast of downtown Atlanta. GDOT determined that five bridge spans adjacent to the collapse – two in the northbound direction and three in the southbound direction – required removal due to structural damage from heat exposure. The $15-million replacement project required 43 days.”

Data Collection Methodology

ARC researchers pulled data from the INRIX/NPMRDS database using the RITIS massive data downloader. The data covers the 21-county Atlanta region, totaling 10,607 TMCs. Observations for passenger vehicles, trucks, and the combination of passenger vehicles and trucks were pulled and parsed into three unique datasets.

The data collected represents the time periods before the collapse, during the reconstruction phase, and after the bridge was reopened. Data was collected for these time periods in the incident year (2017), as well as for the preceding year (2016) and the succeeding year (2018). The inclusive dates for the time periods are described below in Table 1.

Table 1
Period	Start Date	End Date
Period 1	February 13	March 29
Period 2	March 30	May 13
Period 3	May 14	June 27

The raw data from INRIX includes a combined date and timestamp in a single attribute field. These were separated into two fields, then the time field was associated with a “time-of-day period” that aligns with the five available time-of-day periods in the ARC’s activity-based travel demand model. The time-of-day periods are described below in Table 2.

Table 2
Time-of-Day Period Name	Abbreviation	Begin Time	End Time
Early A.M.	EA	3:00 a.m.	5:59 a.m.
A.M. Peak	AM	6:00 a.m.	9:59 a.m.
Midday	MD	10:00 a.m.	2:59 p.m.
P.M. Peak	PM	3:00 p.m.	6:59 p.m.
Evening	EV	7:00 p.m.	2:59 a.m.

Each TMC link was also associated with the appropriate zip code and county name from the NPMRDS shapefile provided with the INRIX data downloads.

This processed data was then aggregated by hour and by time-of-day period to decrease the number of records. Each attribute (e.g. speed) was averaged to display a single record for each TMC in the network for a given hour or time-of-day. This aggregated data was then additionally separated to include records for the interstates only and records for the arterials only. In total, there are 306 CSV files in the dataset. The resulting dataset was duplicated with spatial association in the form of shapefiles of all modeling roads and of all interstates for each year, period, time-of-day, and vehicle type combination.