The Fall and Rise of the I-35W Bridge

Understanding the Traffic Flow
Evolution after Network Disruption

Aug. 1, 2007

Source:www.dot.state.mn.us

Prof. Henry Liu
Department of Civil and Environmental Engineering
Transportation Research Institute
University of Michigan, Ann Arbor

Sep. 18, 2008

International Symposium on Human Society and Risk
August 10, 2015
Sapporo, Japan

Google Map of the Twin Cities

I‐35W Mississippi River Bridge

2Mn/DOT Traffic Restoration Projects

Research Questions

Empirical Observations• How should transportation agencies optimize theirresources in response to the network disruption?
• How do traffic patterns evolve from a network
disruption?
– After bridge collapse– After bridge reopening

Freeway Travel Demand (AM Peak)
700,000

Total Counts

650,000

600,000
Bridge Collapse
on Aug. 1
550,000

500,000

Jul. 30 Aug. 3

Aug. 6 Aug. 10

1. Freeway Loop Detector Data
2. Travel Behavior Survey Data-- Questionnaire-- GPS Trajectories

References:1.

• MnDOT economists estimated that, without the I35 Bridge, the public lost $400k everyday in termsof economic productivity.

Jul. 23 Jul. 27

Data Sources:

Aug. 13 - Aug. 20 - Aug. 27Aug. 17 Aug. 24 Aug. 31

Weekdays from Jul. 23 to Aug. 31, 2007

2.
3.

Zhu, S., Levinson, D., Liu, H., Harder, K. (2011) The Traffic andBehavioral Effects of the I-35W Mississippi River Bridge Collapse,Transportation Research Part A, 44, 771-784.
Guo, X. and Liu, H. (2011) Bounded Rationality and irreversible networkchanges, Transportation Research Part B, 45(10), 1606-1618.
He, X. and Liu, H. (2012) Modeling the day-to-day traffic evolution
process after an unexpected network disruption, Transportation ResearchPart B, 46(1), 50-71

Three Cordons

Inbound Cordon Volumes (6-9AM)

Morning Congestion Impacts
6:00 to 9:00 a.m. – Relatively Unchanged
July 23, 2007

Sept. 10, 2007

Source: MnDOT RTMC

Findings from Bridge Collapse Survey• Handed out 860 surveys, and received 148
responses (Mid-Sept, 2007)• 56 respondents changed routes after bridge
collapse– 14 of them were NOT regular I-35W Bridge users• Changed their daily routes on Aug. 2nd, 2007 because ofanticipated congestion

Random Driver (Survey Results)

Random Driver (Before Collapse)

Same Driver (August 2nd)Same Driver (Weeks after Collapse)

Same Driver (Mid-Sept. 2007)

Observations on “Recovery” PatternInbound Cordon Volumes (6-9AM)• Traffic “shock” is observed close to bridge site Travelers avoid the area because of theanticipation of traffic congestion• Travelers learn and adjust their routes during
the transition time
• In long-term (aside from cordon at bridge),traffic recovers to pre-collapse levels

Irreversible Network Disruption

Findings from Bridge Reopening Survey• Handed out 840 surveys, and received 137
responses (Mid-October 2008)• 26 respondents changed routes after bridge
reopening• 3 respondents, who were regular I-35W
Bridge, did not use it as commute route after
new bridge reopened because they are satisfied
with their current routes

Random Driver (Survey Results)

Random Driver (Before Collapse)

Same Driver (Before Reopening)

Same Driver (on Sept. 18)

Same Driver (Weeks after Reopening)

Summary of Empirical Observations• Traffic Recovery Patterns are Different for
Unexpected Closure and Expected
Reopening– Unexpected Closure• Sudden Drop and Gradual Recovery– Reopening from A Closure• Seemingly immediate recovery and stabilization• Irreversible network flow change

Behavioral Explanations– Unexpected Closure• Travelers avoid the area because of the anticipation oftraffic congestion• Prediction of future traffic condition needs to be
included in the model– Reopening from a closure• Travelers are reluctant to change routes if the benefit issmall• Travelers are not perfectly rational. Bounded rationality
is behaviorally appealing.

GPS Trajectory of a Traveler

Reasons for Bounded Rationality

Indifference Bandε: deviation from the minimum cost

1. Driving habit
2. Cognitive limit“Satisfactory”

143 commuters morning trips
 Before: 2 or 3 weeks before
 After: 1 or 2 months after

Non‐impacter

Nonimpacter66

Switcher
Switcher
47*Source: huffingtonpost

Value of Indifference Band
*Source: Zhu (2010)

30

Stayer25

Frequency

ε=10%Time saving(9‐10)/10=‐10%“Optimal”

20

Stayer8

Switcher

15
10

6Stayer
30

57

10
116

8

3

12

5
5

10

15

5

1
2

3

3

20

25

30

35

Time Saving Percentage (%)

31

Conclusions• Empirical observations shows that drivers canadapt to a disrupted network rather quickly.
– No bridge, no problem.

• Driver adaptability and predictability, as well asbounded rationality, should be included in driver
behavior modeling.
• More studies are needed for disrupted
transportation network– Multimodal impacts– Congested networks

THANK YOU!
Contact Information:
Prof. Henry Liu
University of Michigan, Ann Arbor
Email: henryliu@umich.edu
Phone: 1-734-764-4354