Remote sensing of environment and disaster laboratory

Institute of Industrial Science, The University of Tokyo, Japan

Risk assessment of rail infrastructure in India to support
business continuity plan

photoDheeraj JOSHI, Wataru TAKEUCHI
Institute of Industrial Science, The University of Tokyo
Abstract: Rail infrastructure in India is the main transport mode for passenger and freight transportation. But rail infrastructure assets arefrequently exposed to multi-hazards and disruptions like disasters have the potential to interrupt the organization’s entire operations andpreventing them from continuing the business in a normal way. The SDG 11 and the Sendai Framework for Disaster Risk Reduction calls forunderstanding risks through risk assessments towards avoiding frequent disruptions in operations and losses to rail business due to disasters.
Risk to existing railway infrastructure in this study is defined as per UNDRR terminology, which is a function of hazard – the probability andseverity of an event; exposure – assets subject to the hazard; and vulnerability – physical, social and economic susceptibility of assets tosuffer damage under hazard of given severity. The planned infrastructure like HSR corridors are analysed on the urban vulnerability of lossof green space around the HSR stations. Further, frequency of emergency situations recorded in the system are also utilised as acomprehensive indicator of risk assessment as it combines all the factors of risk. Therefore, this study focuses on creating an enabling
environment towards disaster risk informed investment on critical infrastructure and business continuity planning for safe rail operations.
Keywords: Multi-hazards, Vulnerability, Exposure, Green Space, Sustainability.

Background

Absence of risk assessment studies incorporating actual local vulnerability of key safety affecting railway elements like bridges andlevel crossings; Similarly, haphazard state of urbanization with loss of green space is causing increased surface runoffs (Mumbai city
suffering from floods every year) and lack of planning the same for transport infrastructure is a key issue; Further, there is absence ofrisk studies due to systemic issues highlighted by emergency situations;assessment for existing rail infrastructure exposed
Objective 1. Risk
to hazards under local compounding vulnerabilities,

Results Fig 3. Spatial risk assessment under multi-hazards

2. Planned rail infrastructure under urban vulnerabilities,
3. Risk assessment under consequential emergency cases.

Methodology

Fig 1. Research flow for the study
(A)

(C)

(B)

Risk assessment under multi-hazard scenarios:(A) – Landslide susceptibility under social vulnerability (sv)(B) – Flood and earthquake hazard under sv(C) – Landslide susceptibility under physical vulnerability (pv)
(D) – Flood and earthquake hazard under pv(E) – Risk to level crossings infrastructure under economicvulnerability
Assuming the same damage under the same risk level ranking

(D)

(E)

Fig 4. Zonal risk assessment ranking under multi-hazards

Fig 2. Risk analysis methodology due to local compounding vulnerability•The zones of NR and CR have highest risk while NWR has lowest risk.

Fig 5. Risk assessment in IR under emergency cases scenario

Accident % during each season in IndianRailways

Season-wise analysis of risk of accidents in Railways

1.2

10%

129%

0.8
0.6

37%

0.4
24%
0.20•

The classification of liquefaction susceptibility is done into five classes – very low, low, moderate, high andvery high with very low value corresponding to VS30 > 620 m/s (Koks et al, 2019). Different scenarios ofearthquake hazard events are considered (hazard map obtained from UNDRR and resolution calibrated at1.2 Km) with liquefaction susceptibility (1.2 Km resolution map obtained from Koks et al, 2019). The
bridges data for the Indian Railways is plotted as per their ORN rating Zone wise for physicalvulnerability. Similarly, the labour wages and GSDP (at constant prices) across the regions in India levelcrossings is presented in under social and economic vulnerability respectively.

Ranking criteria and key equations utilized in the study:

FUTURE TASK

Cold weather

Hot weather

South-westmonsoon

Retreatingmonsoon

Cold weather

Hot weather

South-west monsoon

Retreating monsoon

The zone of NR has highest risk while zone of NWR is amongst lowest risk.

Fig 6. Green space availability in TOD zones of MAHSR stations
SBI - 01
BKC - 19 0.7
BKC - 01
0.6

TNA - 19TNA - 01
VR- 19VR- 01

0.5
0.4
0.3
0.2
0.1
0•

SBI -19ADI - 01
ADI - 19
ANND - 01
ANND - 19
BRC - 01

BOR - 19

BRC - 19

BOR - 01

BH - 01

VAPI - 19
VAPI - 01
BIM - 19
NDVI

BH - 19
BIM - 01
NDVI 5 Km

ST - 01
ST - 19
NDVI 1 Km•

There is decline in
MAHSR stationcities from 2001 ingreen space for
critical TOD zone of
5 Km in 2019.
Need for integrated
urban andtransport planning.

Conclusion• Consideration of local vulnerability is critical inunderstanding nature of risk.
• This study is good enough as first step in spatial risk
assessment and risk informed decision making towards
investment for improvement of critical infrastructure.
References:• Risk analysis under environmental vulnerability scenario• Quantifying aspects of hazards damage (currently assumed same under similar vulnerability rank)

1) Koks et al, 2019. “A global multi-hazard risk analysis of road and railway infrastructure assets”. In: nature communications.
2) UNDRR Global Risk Data Platform. https://www.preventionweb.net/english/professional/maps/. Accessed on 14 October, 2020.
3) Wong et al., 2019. “High-resolution calculation of the urban vegetation fraction in the Pearl River Delta from the Sentinel-2 NDVI
for urban climate model parameterization”. In: Geosci. Lett. (2019) 6:2.
4) Sobrino JA, Jimenez-Munoz JC, Paolini L,2004. “Land surface temperature retrieval from LANDSAT TM 5”. In: Remote Sens Environ
90(4):434–440

For further details, contact: Dheeraj Joshi, Bw-601, 6-1, Komaba 4-chome, Meguro, Tokyo 153-8505 JAPAN (URL: http://wtlab.iis.u-tokyo.ac.jp/ E-mail: dheeraj-joshi@g.ecc.u-tokyo.ac.jp)