Climate Change Impacts on

Coastal Urban Road Networks:

A Methodological Approach

B. K. Bhavathrathan1, C. G. Madhusoodhanan2, Gopal R. Patil1 and T. I. Eldho2

1Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology Bombay, India

2Water Resources Engineering, Department of Civil Engineering, Indian Institute of Technology Bombay, India

Introduction

• Transportation: a key service connecting various facets

• Urbanization induced congestion

• Coastal cities: Constrained on one side and prone to

climate change

• Precipitation: Key variable that affects road capacity; also

a key climate variable

Effect of Rainfall

• Rainfall increments travel time on the network

• Routine operations may remain unhindered

• Largely an unattended menace

• Conventional models consider demand-increment;

neglect climate change impact

Transportation Network

Concept of System Travel Time

• Grand sum of time taken by all vehicles on all roads

• To find STT we should know how many vehicles are there

on each road

• This exercise is called traffic routing

Traffic Routing

• To determine traffic volume on urban roads

• To determine which route would people take

• Different philosophies: AON, UE, DUE, PUE, SO

• User Equilibrium: commuters learn, switch routes and

finally equilibrate

• At Equilibrium: Travel times on all route options will be

same.

• Road Capacity should be known (Big deal?)

1 2

3

Rain Induced Capacity Degradation

• What if capacities don’t stay as designed

• It depends on the rainfall intensity

Chung et al. (2006)

Traffic Routing Under Capacity Degradation

• Rainfall intensities differ; known probabilities

• Extrapolated to the disruption probabilities

• Employ Probabilistic User Equilibrium

• Convex combinations algorithm to solve

Cause, Chance & Consequence

• The Cause: Rainfall at (varying) intensities

• The Chance: Probability of an intensity level

• The Consequence: Increment in STT

• Risk: probability times consequence

A Sample Exercise

System Travel Time using PUE assignment

Probabilities corresponding to rainfall intensities

June – September Mumbai Monsoon data (1970-2009)

Conclusion

• Climate change affects travel time on urban network

• Requirement of a tool to analyze and cross-compare

• Scope limited to presenting a methodology

• We hope that such quantification would encourage

transportation planners to account for the losses that are

expectable due to climate change

THANK YOU!