traveler flexibility: the next piece of transportation resilience · 2014. 10. 1. · mowe-it...

Murray-Tuite 1

Traveler Flexibility: The Next Piece of

Transportation Resilience

Pamela Murray-Tuite, Ph.D. September 8, 2014

MOWE-IT Regional Conference

Berlin, Germany

Murray-Tuite 2

Presentation Outline

Introduction

Previous focus of transportation resilience assessments

Two examples

Hurricane Sandy

Winter Weather (2013-2014)

Future Directions

Challenges to transportation

Extreme weather underlines the importance of transportation resilience

Ability to resist catastrophic failure

Ability to recover quickly

Introduction 3

Image source: http://cdn.arstechnica.net/wp-content/uploads/2012/10/flood.jpg

4 R’s of resilient systems

Robustness: strength to withstand a given level of stress or demand Resourcefulness: the capacity to identify problems and mobilize resources

Redundancy: substitutes

Rapidity: timely response1

Introduction 4

Multidisciplinary Center for Earthquake Engineering Research's (MCEER)

Previous studies

Focus

Infrastructure

Single mode networks

Approaches

Topological analyses2,3

Treatment of demand levels as constant4

Deterministic and stochastic optimization

Quantifying resilience as the proportion of demand that can be served by the damaged network5-8

Assigning penalties for unfulfilled demand9

Previous focus 5

Recognizing changes in demand

Approaches

Multiplying the original demand by a reduction factor10

Optimization approaches based on user equilibrium traffic assignment11

Conceptual frameworks with a cognitive layer for human behavior in addition to the physical layer12

Evolving focus 6

Demand changes

Humans are flexible and adaptable to the options available

Options exist outside the standard transportation infrastructure and services

Evolving focus 7

Image Source: http://www.weather.gov/images/okx/Sandy/ManhattanLexingtonAve_WzohaibFlickrNHCReport.jpg

Hurricane Sandy impacts

Transportation impacts began on October 28, 2012, the day before the storm struck

The hurricane caused flooding in 7 of New York City’s subway tunnels and 1 of the Long Island Rail Road’s tunnels under the East River, NJ Path service disruptions and tunnel flooding, and vehicle tunnel closures

Hurricane Sandy 8

Agency response to disruption

The day after the storm, no transit service was available but bridges reopened; many tunnels remained closed

Transportation agencies and government officials tried various strategies to restore connectivity and manage demand

HOV 3+ restrictions on several major bridges to NYC on November 1 and 2

Restricting access to gasoline stations by license plate number

Modified taxi policies

Special ferry service

Hurricane Sandy 9

Commuter responses to disruptions A phone-based survey collected data on

commuter adaptation

397 records

January 2013

Questions pertained to:

Normal commuting behavior

Post-hurricane commuting and whether and how the commute was impacted by a particular disruption or recovery measure

Factors influencing decisions not to commute

Demographics Hurricane Sandy 10

Return to work

Hurricane Sandy 11

0

10

20

30

40

50

60

70

80

Tue, Oct 30

Wed, Oct 31

Thu, Nov 1

Fri, Nov 2

Sat, Nov 3

Sun, Nov 4

Mon, Nov 5

Tue, Nov 6

Wed, Nov 7

Thu, Nov 8

Fri, Nov 9

Sat, Nov 10

or later

Percen

tag

e o

f R

esp

on

den

ts

Day of Return to Normal Work Schedule and Location

Percent of Respondents

Cumlative Percentage

No transit; taxi policy; tunnels closed

Limited bus

HOV3+ Gas restrictions

S.I. Ferry resumes

Queens Midtown tunnel 1 lane for buses; NJ bus service access to ferries

Commute disruption duration

Hurricane Sandy 12

0

10

20

30

40

50

60

70

80

90

100

Percen

tag

e

Days of Disruption

Percentage of Respondents Cumulative Percentage

Commuter adaptations

Spending the night closer to work (changing origins/destinations)

Cancelling the commute

Changing transportation modes

Changing routes

Changing departure times

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Gas Restrictions

Hurricane Sandy 14

Delays/Crowding

Hurricane Sandy 15

MTA

Hurricane Sandy 16

Closure of Tunnels

Hurricane Sandy 17

Further investigation

Hurricane Sandy 18

Dependent Variable N Mean

Changing modes after Hurricane Sandy: 1

yes, 0 no 312 0.32

Not going to work after Hurricane Sandy: 1

yes, 0 no 312 0.54

Changed the commuting route that was

normally taken: 1 yes, 0 no 312 0.50

Altered commuting time by leaving home for

going to work earlier than usual: 1 yes, 0 no 312 0.54

Altered commuting time by leaving home for

going to work later than usual: 1 yes, 0 no 312 0.16

Changing modes

Commuting by transit under normal conditions

85 out of 100 who changed modes

None of the commuters who normally used private vehicles shifted modes

Encountering HOV3+ restrictions

Gender (male)

Number of children under 15 years old in the household

Hurricane Sandy 19

Cancelling the trip to work


Teleworking options

Experiencing delays and crowding

Number of children under 15 years old in the household

Occupation (education/legal/community service/ arts and media fields)

Hurricane Sandy 20

Changing routes

Tunnel closures

Delays and crowding

School/day-care closures

Hurricane Sandy 21

Queens Midtown Tunnel flooding Source: Mark Valentin https://www.flickr.com/photos/mtaphotos/8204034522/in/set-72157632061221446

Departing earlier

Lower income

Lack of telecommuting options


Carpool restrictions

Delays and crowding

Hurricane Sandy 22

Departing later

Gender (male)

School/day-care closures

Gasoline restrictions

Hurricane Sandy 23

Image source: http://www.edweek.org/media/2012/11/05/sandynjschool_600.jpg

Disaster recovery implications

Not all transit users are transit dependent and may switch transportation modes

However, transit dependents may have to cancel their commutes, which could affect their employment in longer term disruptions

Recovery for working parents is constrained by school/child care recovery

Disruptions and resulting delays/crowding tend to lead to earlier departures

Telecommuting can help people avoid severe delays and congestion

Dependence on power and communications systems

Hurricane Sandy 24

Harsh winter 2013-2014

Snow storms and below freezing temperatures disrupted school and employer schedules as well as road operations

Winter Weather 25

Traveler survey

Northern Virginia portion of Washington D.C. Metropolitan area

418 responses

Asked about what commuters would do

If winter weather conditions began or were forecasted to begin while they were at work

If winter weather conditions began while they were at home

Asked all respondents whether they would make changes for other types of trips

Asked about the importance of various factors

Winter Weather 26

Weather dependent responses

Weather Mean N

Snow

1 if respondent would change transportation

plans if snow begins at work; 0 otherwise 0.53 293

Freezing

Rain


plans if freezing rain begins at work; 0

otherwise 0.52 293

Heavy

Rain


plans if heavy rain begins at work; 0

otherwise 0.15 293

Below

Freezing


plans if below freezing temperatures begin at

work; 0 otherwise 0.17 293

Icy Roads


plans if icy road conditions begin at work; 0

otherwise 0.71 293

27

Weather begins while at work

Commute changes Decision Mean N

Cancel a

Trip

1 if a trip would be canceled if winter weather

begins at work; 0 otherwise 0.72 269

Delay a Trip

1 if a trip would be delayed if winter weather

begins at work; 0 otherwise 0.71 260

Leave Work

Early

1 if respondent would leave work early if winter

weather begins at work; 0 otherwise 0.67 265

Add Trips

1 if respondent would add trips to the return

commute if winter weather begins at work; 0 o.w. 0.43 271

Change

Destination

1 if respondent would change the destination of a

trip if winter weather begins at work; 0 otherwise 0.49 273

Change

Route

1 if respondent would change routes if winter


Use More

Highways

1 if respondent would use more highways if winter


Change

Mode

1 if respondent would use change transportation

modes if winter weather begins at work; 0 o.w. 0.11 278 28

Cancelling a trip

Ethnicity (non-Hispanic)

Winter Weather 29

Teleworking options

Lack of child related travel responsibilities

Delaying a trip

Making stops normally

Winter Weather 30

Leaving work early

Commuting frequency

Work policy (winter weather absences excused)

Age of the youngest child

Making stops normally

Winter Weather 31

Adding trips

Race/ethnicity (non-White (Caucasian))

Leisure trip frequency

Number of children in the household

Assigning importance to road conditions

Assigning importance to employer decisions

Winter Weather 32

Changing a destination


Assigning importance to employer decisions

Winter Weather 33

Changing routes


Assigning importance to family considerations

Winter Weather 34

Image source: http://www.adn.com/sites/default/files/styles/ad_slideshow_normal/public/20131122%20icy%20roads%2004.jpg?itok=cJ1mbrzu

Using more highways

Conducting leisure trips on weekdays (less likely)

Conducting errands on the weekends (less likely)

Assigning importance to school decisions (less likely)

Normally using highways

(more likely)

Winter Weather 35

Changing modes

General inertia

Driving alone (less likely)

Winter Weather 36

Conclusions

Understanding of traveler flexibility/ adaptability could not have been achieved without behavior data

Dependence on factors outside the transportation system

Strictly topological based approaches would likely over estimate immediate needs

Murray-Tuite 37

Suggestions for future study

Collect more post-event data

Travel choices and options

How behavior changes with mitigation measures

More comprehensively integrating demand and behavior into transportation system resilience assessments

Better understand post event needs

Target resilience enhancement strategies

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Acknowledgements

The Hurricane Sandy data collection was supported by National Science Foundation Award 1313674 for which the authors are grateful. The contents do not necessarily reflect the official views of the National Science Foundation.

The winter weather data collection was supported by the Mid-Atlantic University Transportation Center. The contents do not necessarily reflect the official views of MAUTC.

Murray-Tuite 39

References

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1. Bruneau, M., S.E. Chang, R.T. Eguchi, G.C. Lee, T.D. O'Rourke, A.M. Reinhorn, M. Shinozuka, K. Tierney, W.A. Wallace, and D. von Winterfeldt, A Framework to Quantitatively Assess and Enhance the Seismic Resilience of Communities. Earthquake Spectra, 2003. 19(4): p. 733-752.

2. Berche, B., C. Von Ferber, T. Holovatch, and Y. Holovatch, Resilience of public transport networks against attacks. The European Physical Journal B, 2009. 71(1): p. 125-137.

3. Dorbritz, R. Assessing the resilience of transportation systems in case of large-scale disastrous events. in Proceedings of The 8th International Conference on Environmental Engineering, Vilnius, Lithuania. 2011.

4. Werner, S.D., J.-P. Lavoie, C. Eitzel, S. Cho, C.K. Huyck, S. Ghosh, R.T. Eguchi, C.E. Taylor, and J.E. Moore REDARS 1: Demonstration Software for Seismic Risk Analysis of Highway Systems. c. 2003

5. Chen, L. and E. Miller-Hooks, Resilience: an indicator of recovery capability in intermodal freight transport. Transportation Science, 2012. 46(1): p. 109-123.

6. Faturechi, R., E. Levenberg, and E. Miller-Hooks, Evaluating and optimizing resilience of airport pavement networks. Computers & Operations Research, 2014. 43: p. 335-348.

7. Miller-Hooks, E., X. Zhang, and R. Faturechi, Measuring and maximizing resilience of freight transportation networks. Computers & Operations Research, 2012. 39(7): p. 1633-1643.

8. Nair, R., H. Avetisyan, and E. Miller-Hooks, Resilience framework for ports and other intermodal components. Transportation Research Record: Journal of the Transportation Research Board, 2010. 2166(1): p. 54-65.

9. Vugrin, E.D., M.A. Turnquist, and N.J. Brown, OPTIMAL RECOVERY SEQUENCING FOR ENHANCED RESILIENCE AND SERVICE RESTORATION IN TRANSPORTATION NETWORKS, 2013, Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States).

10. Bocchini, P. and D.M. Frangopol, A stochastic computational framework for the joint transportation network fragility analysis and traffic flow distribution under extreme events. Probabilistic Engineering Mechanics, 2011. 26(2): p. 182-193.

References

Murray-Tuite 41

11. Cho, S., Y.Y. Fan, and J.E. Moore, Modeling network flows as a simultaneous function of travel demand, earthquake damage, and network level service. Advancing Mitigation Technologies and Dsaster Response for Lifeline Systems: Proceedings of the 6th U.S. Conference and Workshop on Lifeline Earthquake Engineering, 2003: p. 868-877.

12. Leu, G., H. Abbass, and N. Curtis, Resilience of ground transportation networks: a case study on Melbourne. 2010.

Murray-Tuite 42

Contact [email protected]

Questions?

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Transportation System Use

Murray-Tuite 45

Transit Mode

2012 Avg Weekday Ridership

2012 Total Ridership

Bridges &Tunnels

2011 Weekday

Traffic

NYC subway

5,380,184 1,654,157,543 Robert F. Kennedy Bridge

Bronx Plaza 72,759

MTA NYC bus

2,169,311 667,910,621 Robert F. Kennedy Bridge

Manhattan Plaza 84,684

MNR 82,953,628 Bronx-Whitestone Bridge 104,879

LIRR 81,745,989 Henry Hudson Bridge 61,638

NJ Transit Marine Parkway-Gil Hodges

Memorial Bridge 21,365

Bus 535,168 161,680,466 Cross Bay Veterans Memorial

Bridge 19,408

Rail 281,576 81,353,894 Queens Midtown Tunnel 83,686

Light Rail 72,345 21,820,962 Brooklyn-Battery Tunnel 53,206

Throgs Neck Bridge 101,431

Verrazano-Narrows Bridge 89,299

Introduction & Background

Carpool Restrictions

Hurricane Sandy 46

PATH

Murray-Tuite 47

Initial Results

NJ Transit

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Initial Results

Taxi Policies

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Initial Results

LIRR

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Initial Results

MNRR

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Initial Results

Hurricane Sandy Models

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Independent Variables

Change Mode Cancel Work

Trip Change routes Depart earlier Depart later

b Wald c2 b Wald c2 b Wald c2 b Wald c2 b Wald c2

Intercept -2.45 -1.34 -1.64 -0.54 -3.45

Female (binary) -0.61 4.07*** -1.07 6.27**

*

Income (continuous) -5.08E-

06 3.71**.

Children <15 yo

(continuous) 0.29 3.44** 0.23 2.94**

Telecommuting (binary) 0.53 2.96**. -0.69 3.28**

Transit commuters

(binary) 2.59

53.77**

** 1.75

44.2**

** 0.89

8.06**

*

Tunnel closure (binary) 1.32 3.87***

Carpool restrictions

bridges (binary) 2.29

20.98**

** 1.41

6.33**

*

Gasoline restrictions

(binary) 1.69

6.84**

**

Delays and crowding

(binary) 0.64

5.87**

* 1.31

14.48**

** 1.52

23.11*

***

Day care/school closed

(binary) 1.02 3.95*** 1.26 2.49*

Education legal community

service arts media

occupation (binary)

0.42 1.76*

Observations 305 303 173 221 174

Prob(>Chi-Square) <0.000

1

<0.000

1

<0.000

1

<0.000

1 0.0008

Pseudo R square 0.2932 0.1622 0.138 0.1982 0.1055

Area under ROC curve

(AUC) 0.8340 0.7616 0.7327 0.7830 0.7073

log likelihood restricted 192.23 208.43 119.77 152.80 79.99

-log likelihood unrestricted

(full)135.86 174.63 103.28 122.51 71.55

Winter weather effects on public schools Date Day of the Week Closure Delayed

Dec. 9-10, 2013 Mon, Tue X

Dec. 11, 2013 Wednesday X

Jan. 7, 2014 Tuesday X

Jan. 8, 2014 Wednesday X

Jan. 10, 2014 Friday X

Jan. 21-23, 2014 Tue-Thu X

Jan. 24, 2014 Friday X

Jan. 29, 2014 Wednesday X

Feb. 5, 2014 Wednesday X

Feb. 13-14, 2014 Thu, Fri X

Feb. 18, 2014 Tuesday X

Mar. 3-4, 2014 Mon, Tue X

Mar. 5, 2014 Wednesday X

Mar. 17, 2014 Monday X

Mar. 18, 2014 Tuesday X Winter Weather 53

Weather begins while at work

Cancel a Trip

Delay a Trip

Leave Work

Early

Add Trips

Change

Destination

Change Route

Use More

Highways

Change Mode

Variable Est.

Chi Sq

Est.

Chi Sq

Est.

Chi Sq

Est.

Chi Sq

Est.

Chi Sq

Est.

Chi Sq

Est.

Chi Sq

Est.

Chi Sq

Odds

Ratio

Odds

Ratio

Odds

Ratio

Odds

Ratio

Odds

Ratio

Odds

Ratio

Odds

Ratio

Odds

Ratio

Intercept 1.380 8.92*** 0.472 7.28*** -3.298 9.65*** -3.520 9.23*** -1.608 16.54*** -0.853 6.10** 0.702 3.08*

-

0.693 4.16**

Hispanic

-1.847 6.92***

0.158

White

-0.707 5.33**

0.493

ComNum Days

0.326 4.53**

1.385

LeisNum Days

0.239 7.29*** 0.164 4.00** 0.177 4.21**

1.270 1.179 1.193

Leis WkDay

-0.657 4.20**

0.518

Er WkEnd

-0.703 4.29**

0.495

Telework OptWW

1.267 6.80***

3.549

Absences Excused

1.691 10.63

*** 5.425

Child Travel Resp

-1.077 4.43**

0.341

Age Youngest Kid 0.126 5.29**

Murray-Tuite 54

traveler flexibility: the next piece of transportation resilience · 2014. 10. 1. · mowe-it...

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