Decision Support System Tool for Assessing Vulnerability of Transportation Networks: Case of Mega Flood in Thailand

The First NIDA Business Analytics and Data Sciences Contest/Conferenceวันที่ 1-2 กันยายน 2559 ณ อาคารนวมินทราธิราช สถาบันบัณฑิตพัฒนบริหารศาสตร์

-Decision support system (DSS) คืออะไร สร้างได้อย่างไร น าไปใช้ประโยชน์อะไรได้บ้าง-Transportation network บอกอะไรเรา เราน าไปใช้อะไรได้บ้าง-มหาอุทกภัยในปี 2554 ให้บทเรียนอะไรแก่เราบ้างในด้านเครือขา่ยการขนส่ง

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อ. ดร. สราวุธ จันทรส์ุวรรณสาขาวิชา Logistic Managementคณะสถิติประยุกต์ NIDA

นวมินทราธิราช 3003 วันที่ 2 กันยายน 2559 10.15-10.45 น.

DECISION SUPPORT SYSTEM TOOL FOR ASSESSING

VULNERABILITY OF NATIONAL HIGHWAY NETWORKS: CASE

OF CHAO PHRAYA RIVER BASIN FLOOD IN THAILAND

Dr. Sarawut JansuwanNational Institute of Development Administration (NIDA), Thailand

Prof. Anthony Chen The Hong Kong Polytechnic University, Hong Kong

Dr. Kitti SubprasomDepartment of Highways, Thailand

Dr. Kasem PinthongSoutheast Asia Technology Group

World Conference on Transport Research SocietyShanghai July 2016

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Disasters (Natural or Manmade)

Natural Disasters: Earthquake, Tsunami

Avalanche, Flood, Wildfire, Volcano

Terrorist/ Man Made: 9/11 Attack, Riot

Disruption to

Transportation Network:

Congestion, Economic Impact

Disasters to Transportation Networks

Hurricane Sandy, NYMega Flood, Thailand

Earthquake, Tsunami,

Japan Earthquake, Haiti

(Thailand Mega Flood in 2011)

Estimates of economic losses =

1,425 billion THB (~$ 45.7 billion) (World Bank, 2012)

Typhoon Haiyan, The

Philippines

Earthquake, Nepal

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The research objectives are to:

Quantitatively assess the vulnerability of highway transportation network

Estimate the consequences (if the disaster occurs) using What-if scenario

analysis and,

Propose the highway development plan to mitigate the adverse impact

and strengthen transportation network

Picture sources: www.thehindu.com, www.wikipedia.com

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Decision Support System (DSS) Framework

Decision

Supporting

ToolVisualizing in GIS

Assessing the

Impact of Disaster

Demand

O-D Trip Table

Transportation

Network

Case Study/What-if

Results and

Recommendations

Assessing the

Probability of Disaster

Case of 2011 Chao

Phraya River Basin

Flood in Thailand

Risk

Analysis

Chao Phraya River Basin: Travel Demand

Four Travel Demand Types extracted from National Model

High probability: 0.68-1

Medium probability: 0.34-0.67

Low probability: 0-0.33

Probability of Flooding on

Highway Network

Use hydrological information of

the Basin (rainfall and return

period) to generate flood

probability map

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Consequences: Three Measures

Connectivity loss

Detour/ longer distance

Economic loss

Travel longer time/delay/increased

in transportation cost

Accessibility loss

Difficult to access to specific

areas (i.e., home, workplace)

Vulnerability Analysis for Transportation Networks

Connectivity Vulnerability Index (CVI)

• Use the connectivity analysis to determine the alternate or second-

best routes for rerouting traffic using Dijkstra shortest path

• Compute increased O-D distances after the disruption.

Increased OD distance( ( , )) ( ( , ))I J

l ij ij

i j

CVI d G V E l d G V E

Vulnerability Analysis for Transportation Networks

Economic Vulnerability Index (EVI)

• The transport efficiency can be measured by the transport cost in

terms of increased of travel time and distance (converted by

value of time (VOT) and vehicle operating cost (VOC))

Increased travel time ( ( , )) ( ( , ))

( ( , ) ( ( , ))

I J

l ij ij

i jI J

ij ij ij

i j

EVI t G V E l t G V E

f d G V E l d G V E

Increased VKT

VOT (locally calibrated for the Basin region, Baht per hour)

VOC (operating cost, Bath per kilometer)

Using Multiclass User Equilibrium ( Truck and PC)

Traffic Flow Pattern Change

After link in Nakornsawan

Province is closed due to flood

Vulnerability Analysis for Transportation Networks

Accessibility Vulnerability Index (AVI)

• Apply the gravity model from Hansen accessibility measure (Hansen,

1959)

• The accessibility impact in our study is analyzed into 2 types:

1) intra-zonal accessibility, and 2) inter-zonal accessibility.

(1 ) ,j jInter i i

i ij ij

j jk k k k

k k k k

P PP PA d t i

P P P P

,jIntra i

i ij

jk k

k k

QQA d i

Q Q

the travel impedance and opportunity to

access to major landuse activities within

the zone

measure the travel

impedance weighted by

the opportunity data based

on the socioeconomic data

of each district (e.g.,

population and

employment)

Accessibility Vulnerability Index (AVI)

Inter-zonal accessibility Intra-zonal accessibility

Dots represent landuse activities (e.g.,

school, hospitals, shopping centers)

Accessibility Vulnerability Index (AVI)

The GIS buffering technique to detect important landmarks for intra-zonal

accessibility

Relative Importance (RI) and

Relative Risk (RR)

Connectivity Economic Accessibility

Relative Link Importance: RI= Changes in CVI, EVI, AVI

Relative Risk Index: RR= RI x Prob (l)

Connectivity-based Risk (CBR) = CVI x prob (l)

Economic-based Risk (EBR) = EVI x prob (l)

Accessibility-based Risk (ABR) = AVI x prob (l)

Decision Support System Framework

Decision Support System (DSS)

• DSS integrates geodatabase, travel demand, vulnerability analysis,

network enhancement tool, and GIS visualization

Geodatabase

And Models

Flood Information

Planners

What-if Scenario

Decision Support System

Decision Support System (DSS)

• Scenario Database Management

Decision Support System

Decision Support System (DSS)

• Google Map background

Show path used for

diverted traffic

Zoom in to vulnerable bridges

Decision Support System

Decision Support System (DSS)

• What-if Scenario Management (Complete/Partial Disruption)

Decision Support System

Decision Support System (DSS)

• Display connectivity and traffic flow pattern changes based on user

queries

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Develop a Decision Support System to Assess

What-if Analysis

Develop a GIS-based Decision Support

System Tool to Assess Vulnerability

Use Chao Phraya River Basin Flood in 2011

Use DSS to identify

vulnerable

roadways (high

consequence

if they are disrupted)

Link Importance Map and Risk Map

Based on Impacts (if link a is

disrupted)

Based on Flood Probability and

Impacts)

Develop Construction Plan to Mitigate Consequence

Construct an elevated roadways in frequented flood areas (Reduce

probability)

Increase capacity to accommodate reroute traffic in alternative

routes

Build alternative route to avoid the frequent flood area

Conclusion

Major Contributions :

Develop a quantitative approach for assessing potential

vulnerability of transportation networks,

Develop a decision support system tool to facilitate the decision

making system

Assess vulnerability of Thailand highway networks.

Acknowledgement & Development Team

The authors gratefully acknowledge the financial support from Department of Highways, Thailand.

Dev. Team

Prof. ChenDr. Kitti

Dr. Kasem

Dr. Nakorn

Me!!