ANALYZING IMPACT OF I-85 BRIDGE COLLAPSE ON REGIONAL … · 2018-01-22 · ANALYZING IMPACT OF I-85...
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ANALYZING IMPACT OF I-85 BRIDGE COLLAPSE ON REGIONAL TRAVEL IN ATLANTA Masoud Hamedi, Sepideh Eshragh, Mark Franz, Przemyslaw Michal Sekula {masoud, eshragh, mfranz1, psekula} @ umd.edu To demonstrate application of Big-Data analytics for quantifying, analyzing and visualizing impact of the I-85 bridge collapse in Atlanta on the surface road network performance. Research Objective: In the evening of March 30, 2017, there was a massive fire under heavily travelled Interstate 85 (I-85) Bridge in Atlanta, Georgia. Flames reached 40ft high and a thick black smoke was visible from the distance, resulting in partial collapse of the bridge deck during rush hour. I-85 Bridge Collapse Photo by CNN The Aftermath In a report published by Atlanta Journal-Constitution (AJC), on top of the direct damage costs, the bridge collapse would affect nearby businesses, change commuter patterns and impact job growth in a city where the construction sector is soaring In order to repair the 100 foot long section of the bridge, approximately two miles of I-85 between the I-75 split and the interchange with SR-400 was shut down in both directions Data Preparation I-95 Corridor Coalition’s Probe Data Analytics (PDA) Suite was used to retrieve data for: Before collapse: Feb 13, 2017 to March 29, 2017 During reconstruction: March 30, 2017 to May 13, 2017 After reopening: May 14, 2017 to June 27, 2017 Corresponding time period in previous year: Feb 13, 2016 to June 27, 2016 In total approximately 14 GB worth of data containing more than 228 million records were retrieved. A GIS shapefile with detail information on the geometry, length and location of TMC segments was also downloaded for spatial analysis and visualization of the results. Detours and main routes were constructed by merging a subset of consecutive corresponding TMC codes. Bottleneck Analysis A bottleneck, as defined by the PDA Suite, occurs when, “the speeds observed for a roadway segment drop below 60% of the free flow speed for a period greater than 5 minutes. Adjacent roadway segments meeting this condition are joined together to form a bottleneck queue. The duration of the bottleneck is calculated until the time speeds are greater than 60% for more than 5 minutes. This definition uses minute-to-minute speeds available across the highway system to determine congestion patterns for the entire day Impact factor = Average duration (in minutes) X average max queue length (in miles) X number of occurrences. IMPACT FACTOR RANK Location Direction LENGTH (Mile) 2017 2016 BEFORE DURING AFTER BEFORE DURING AFT 1 GA-13 @ I-85/GA-403/NORTHWOOD AVE SB 0.24 55499 410317 26363 39232 43541 322 2 GA-42 @ US-78/US-23/US-278/US-29/GA-8 SB 1.36 36945 408120 20165 28288 32775 236 3 US-29 @ DECATUR RD NB 1.00 93123 297372 0 0 55797 0 4 US-23 @ GA-154/MEMORIAL DR SB 0.98 18301 131578 7400 4836 0 576 5 US-19 @ 17TH ST SB 0.26 38847 129764 3815 29829 26429 175 6 US-29S @ PIEDMONT AVE SB 0.64 23251 94745 0 742 1643 163 7 US-19N @ GA-236/LINDBERGH DR NB 1.2 34180 80473 8683 11132 13639 187 8 I-85 @ GA-13/EB EXIT 86 NB 0.24 2247 76890 1137 1007 1677 163 9 I-85S @ GA-400/EB EXIT 87 SB 0.37 246 49770 291 102 652 114 10 GA-400 @ SIDNEY MARCUS BLVD/EXIT 1 SB 0.39 423 33611 1750 200 276 767 1 Bottleneck Locations with Increased Impact Factor Only During Closure Before Collapse During Closure After Reopening Spiral congestion graph of segment GA - 13 S @ I - 85 /GA - 403 /Northwood The bottleneck scan tool generates time spiral congestion graphs for each individual bottleneck location. The Time Spiral shows bottlenecks and events occurring at the selected location. Each rotation on the spiral represents a different day of the study interval, with dates progressing moving out from the center of the spiral. The length of the arc indicates the duration of the bottleneck, and the color indicates the maximum queue length. Traffic events and incidents occurring at the selected location are represented by yellow (for non-injury events), orange (for planned road work, and red diamonds (for injury events) The frequency, queue length, and duration of congestion episodes for this segment of the highway during the closure is dramatically higher when compared to the before the closure and after the re-opening After resuming service on I-85, the impact factor was increased by 18% relative to the before period in 2017. This observation may be explained by the behavior of drivers that used this segment of GA-13 South before the bridge collapse. During the closure period congestion became very severe, as evident with an over 20,000% increase in the impact factor. Once I-85 re- opened, some of these drivers may have tried I-85 South, experienced better conditions and decided to continue using I-85 South over GA- 13 South. In doing so, GA-13 South experienced relatively less congestion in the “after” period of 2017 compared to the “after” period of 2016. Regional Travel Time Reliability Analysis Outbound (8:00 to 9:00) Outbound (17:00 to 18:00) Inbound (8:00 to 9:00) Inbound (17:00 to 18:00) Inbound and outbound direction congestion levels (TTI) for morning and evening peak hours “during” the I-85 road closure period. Weekly morning peak TTI value for the outbound direction from Feb 13, 2017 to June 27, 2017. ( )= 0.5 ∗ ( + ) Outbound (8:00 to 9:00) Outbound (17:00 to 18:00) Inbound (8:00 to 9:00) Inbound (17:00 to 18:00) In order to track the overall changes in congestion in one snapshot, an index is defined to indicate the relative value of TTI during the road closure, compared to average TTI before and after the bridge collapse period. The following equation shows the comparative index: Overall changes in congestion based on Comparative Congestion Index (CCI) for inbound and outbound directions. Time Measurement TTI PTI Before During After CCI Before During After CCI OUTBOUND MEAN 1.64 1.89 1.46 1.25 1.91 2.24 1.71 1.26 Standard Deviation 0.68 1.12 0.50 0.68 0.83 1.40 0.66 0.66 Minimum 0.92 0.91 0.94 0.60 0.97 0.97 0.97 0.56 25% 1.10 1.19 1.11 0.97 1.22 1.38 1.34 0.95 50 % (Median) 1.57 1.44 1.46 1.02 1.83 1.65 1.69 1.01 75% 2.03 2.47 1.63 1.19 2.45 2.94 1.94 1.28 Maximum 3.63 5.34 3.13 3.33 4.21 6.40 3.80 3.16 INBOUND MEAN 1.88 1.91 1.81 1.11 2.22 2.25 2.15 1.12 Standard Deviation 0.81 0.90 0.86 0.48 0.99 1.10 1.07 0.54 Minimum 0.95 0.95 0.98 0.44 1.00 1.01 1.04 0.38 25% 1.23 1.18 1.09 0.83 1.42 1.32 1.27 0.77 50 % (Median) 1.70 1.60 1.51 1.00 1.96 1.84 1.76 0.98 75% 2.51 2.27 2.56 1.26 2.94 2.99 3.09 1.34 Maximum 3.51 3.51 3.67 2.63 4.24 4.17 4.45 2.63 ALL DIRECTIONS MEAN 1.76 1.90 1.63 1.18 2.07 2.25 1.93 1.19 Standard Deviation 0.75 1.00 0.72 0.59 0.92 1.25 0.91 0.60 Minimum 0.92 0.91 0.94 0.44 0.97 0.97 0.97 0.38 25% 1.14 1.18 1.09 0.89 1.30 1.34 1.27 0.90 50 % (Median) 1.60 1.53 1.48 1.00 1.86 1.77 1.74 1.00 75% 2.21 2.39 1.83 1.23 2.72 3.02 2.40 1.32 Maximum 3.63 5.34 3.67 3.33 4.24 6.40 4.45 3.16 Overall TTI and PTI Summary for Inbound and Outbound Travel Directions Conclusions Changes in congestion levels, bottleneck locations, and mobility performance measures are successfully captured and reflected by probe data. Statistical distribution parameters of travel time index and planning time index can be used as a proxy for network resiliency, and makes it possible to quantitatively compare resiliency of different travel directions in a network, or compare resiliency among different networks affected by major link closures. Data used in this study and bottleneck analysis tools were made available by I-95 Corridor Coalition as part of their Vehicle Probe Project. The results and conclusions in this document are those of the authors and not the I-95 Corridor Coalition.
Transcript of ANALYZING IMPACT OF I-85 BRIDGE COLLAPSE ON REGIONAL … · 2018-01-22 · ANALYZING IMPACT OF I-85...
ANALYZING IMPACT OF I-85 BRIDGE COLLAPSE ON REGIONAL TRAVEL IN ATLANTAMasoud Hamedi, Sepideh Eshragh, Mark Franz, Przemyslaw Michal Sekula{masoud, eshragh, mfranz1, psekula} @ umd.edu
To demonstrate application of Big-Data analytics for quantifying, analyzing and visualizing impact of the I-85 bridge collapse in Atlanta on the surface road network performance.Research Objective:
In the evening of March 30, 2017, there was a massive fire under heavily travelled Interstate 85 (I-85) Bridge in Atlanta, Georgia. Flames reached 40ft high and a thick black smoke was visible from the distance, resulting in partial collapse of the bridge deck during rush hour.
I-85 Bridge Collapse
Photo by CNN
The Aftermath In a report published by Atlanta Journal-Constitution (AJC), on top of the
direct damage costs, the bridge collapse would affect nearby businesses, change commuter patterns and impact job growth in a city where the construction sector is soaring
In order to repair the 100 foot long section of the bridge, approximately two miles of I-85 between the I-75 split and the interchange with SR-400 was shut down in both directions
Data Preparation I-95 Corridor Coalition’s Probe Data Analytics (PDA) Suite was used to retrieve data for:
Before collapse: Feb 13, 2017 to March 29, 2017 During reconstruction: March 30, 2017 to May 13, 2017 After reopening: May 14, 2017 to June 27, 2017 Corresponding time period in previous year: Feb 13, 2016 to June 27, 2016
In total approximately 14 GB worth of data containing more than 228 million records were retrieved.
A GIS shapefile with detail information on the geometry, length and location of TMC segments was also downloaded for spatial analysis and visualization of the results.
Detours and main routes were constructed by merging a subset of consecutive corresponding TMC codes.
Bottleneck AnalysisA bottleneck, as defined by the PDA Suite, occurs when, “the speeds observed for a roadway segment drop below 60% of the free flow speed for a period greater than 5 minutes. Adjacent roadway segments meeting this condition are joined together to form a bottleneck queue. The duration of the bottleneck is calculated until the time speeds are greater than 60% for more than 5 minutes. This definition uses minute-to-minute speeds available across the highway system to determine congestion patterns for the entire day
Impact factor = Average duration (in minutes) X average max queue length (in miles) X number of occurrences.
IMPACT FACTOR
RA
NK
Location
Direction
LENG
TH
(Mile)
2017 2016
BEFORE DURING AFTER BEFORE DURING AFT
1 GA-13 @ I-85/GA-403/NORTHWOOD AVE SB 0.24 55499 410317 26363 39232 43541 322 2 GA-42 @ US-78/US-23/US-278/US-29/GA-8 SB 1.36 36945 408120 20165 28288 32775 236 3 US-29 @ DECATUR RD NB 1.00 93123 297372 0 0 55797 0 4 US-23 @ GA-154/MEMORIAL DR SB 0.98 18301 131578 7400 4836 0 576 5 US-19 @ 17TH ST SB 0.26 38847 129764 3815 29829 26429 175 6 US-29S @ PIEDMONT AVE SB 0.64 23251 94745 0 742 1643 163 7 US-19N @ GA-236/LINDBERGH DR NB 1.2 34180 80473 8683 11132 13639 187 8 I-85 @ GA-13/EB EXIT 86 NB 0.24 2247 76890 1137 1007 1677 163 9 I-85S @ GA-400/EB EXIT 87 SB 0.37 246 49770 291 102 652 114 10 GA-400 @ SIDNEY MARCUS BLVD/EXIT 1 SB 0.39 423 33611 1750 200 276 767
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Bottleneck Locations with Increased Impact Factor Only During Closure
Before Collapse
During Closure
After Reopening
Spiral congestion graph of segment GA-13 S @ I-85/GA-
403/Northwood
The bottleneck scan tool generates time spiral congestion graphs for each individual bottleneck location.
The Time Spiral shows bottlenecks and events occurring at the selected location. Each rotation on the spiral represents a different day of the study interval, with dates progressing moving out from the center of the spiral.
The length of the arc indicates the duration of the bottleneck, and the color indicates the maximum queue length. Traffic events and incidents occurring at the selected location are represented by yellow (for non-injury events), orange (for planned road work, and red diamonds (for injury events)
The frequency, queue length, and duration of congestion episodes for this segment of the highway during the closure is dramatically higher when compared to the before the closure and after the re-opening
After resuming service on I-85, the impact factor was increased by 18% relative to the before period in 2017.
This observation may be explained by the behavior of drivers that used this segment of GA-13 South before the bridge collapse. During the closure period congestion became very severe, as evident with an over 20,000% increase in the impact factor. Once I-85 re-opened, some of these drivers may have tried I-85 South, experienced better conditions and decided to continue using I-85 South over GA-13 South. In doing so, GA-13 South experienced relatively less congestion in the “after” period of 2017 compared to the “after” period of 2016.
Regional Travel Time Reliability Analysis
Outbound (8:00 to 9:00) Outbound (17:00 to 18:00)
Inbound (8:00 to 9:00) Inbound (17:00 to 18:00)
Inbound and outbound direction congestion levels (TTI) for morning and
evening peak hours “during” the I-85 road closure period.
Weekly morning peak TTI value for the outbound direction from Feb 13, 2017 to June 27, 2017.
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐼𝐼𝐶𝐶𝐼𝐼𝐶𝐶𝐼𝐼 (𝐶𝐶𝐶𝐶𝐼𝐼) =𝑇𝑇𝑇𝑇𝐼𝐼 𝐼𝐼𝑑𝑑𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶
0.5 ∗ (𝑇𝑇𝑇𝑇𝐼𝐼 𝑏𝑏𝐶𝐶𝑏𝑏𝐶𝐶𝐶𝐶𝐶𝐶 + 𝑇𝑇𝑇𝑇𝐼𝐼 𝐶𝐶𝑏𝑏𝐶𝐶𝐶𝐶𝐶𝐶)
Outbound (8:00 to 9:00) Outbound (17:00 to 18:00)
Inbound (8:00 to 9:00) Inbound (17:00 to 18:00)
In order to track the overall changes in congestion in one snapshot, an index is defined toindicate the relative value of TTI during the road closure, compared to average TTI before andafter the bridge collapse period. The following equation shows the comparative index:
Overall changes in congestion based on Comparative Congestion Index (CCI) for inbound and outbound directions.
Time Measurement TTI PTI
Before During After CCI Before During After CCI
OU
TBO
UN
D
MEAN 1.64 1.89 1.46 1.25 1.91 2.24 1.71 1.26
Standard Deviation 0.68 1.12 0.50 0.68 0.83 1.40 0.66 0.66 Minimum 0.92 0.91 0.94 0.60 0.97 0.97 0.97 0.56
25% 1.10 1.19 1.11 0.97 1.22 1.38 1.34 0.95 50 % (Median) 1.57 1.44 1.46 1.02 1.83 1.65 1.69 1.01
75% 2.03 2.47 1.63 1.19 2.45 2.94 1.94 1.28
Maximum 3.63 5.34 3.13 3.33 4.21 6.40 3.80 3.16
INB
OU
ND
MEAN 1.88 1.91 1.81 1.11 2.22 2.25 2.15 1.12 Standard Deviation 0.81 0.90 0.86 0.48 0.99 1.10 1.07 0.54
Minimum 0.95 0.95 0.98 0.44 1.00 1.01 1.04 0.38 25% 1.23 1.18 1.09 0.83 1.42 1.32 1.27 0.77
50 % (Median) 1.70 1.60 1.51 1.00 1.96 1.84 1.76 0.98 75% 2.51 2.27 2.56 1.26 2.94 2.99 3.09 1.34
Maximum 3.51 3.51 3.67 2.63 4.24 4.17 4.45 2.63
AL
L D
IRE
CT
ION
S MEAN 1.76 1.90 1.63 1.18 2.07 2.25 1.93 1.19
Standard Deviation 0.75 1.00 0.72 0.59 0.92 1.25 0.91 0.60
Minimum 0.92 0.91 0.94 0.44 0.97 0.97 0.97 0.38
25% 1.14 1.18 1.09 0.89 1.30 1.34 1.27 0.90
50 % (Median) 1.60 1.53 1.48 1.00 1.86 1.77 1.74 1.00
75% 2.21 2.39 1.83 1.23 2.72 3.02 2.40 1.32
Maximum 3.63 5.34 3.67 3.33 4.24 6.40 4.45 3.16
Overall TTI and PTI Summary for Inbound and Outbound Travel Directions
Conclusions Changes in congestion levels, bottleneck locations, and
mobility performance measures are successfully captured and reflected by probe data.
Statistical distribution parameters of travel time index and planning time index can be used as a proxy for network resiliency, and makes it possible to quantitatively compare resiliency of different travel directions in a network, or compare resiliency among different networks affected by major link closures.
Data used in this study and bottleneck analysis tools were made available by I-95 Corridor Coalition as part of their Vehicle Probe Project. The results and conclusions in this document are those of the authors and not the I-95 Corridor Coalition.
