1. 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
2. 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
3.
4. 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
6. 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
7. 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?)
9. Rain Induced Capacity Degradation
• What if capacities don’t stay as designed
• It depends on the rainfall intensity
Chung et al. (2006)
10. Traffic Routing Under Capacity Degradation
• Rainfall intensities differ; known probabilities
• Extrapolated to the disruption probabilities
• Employ Probabilistic User Equilibrium
• Convex combinations algorithm to solve
11. 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
12. A Sample Exercise
System Travel Time using PUE
assignment
Probabilities corresponding to
rainfall intensities
June – September Mumbai
Monsoon data (1970-2009)
13. 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
