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Understand the complexity of urban transport
problem, using the Causal Loop Diagram technique
Peraphan Jittrapirom, M. Eng., PhD. Candidate
Technical University of Vienna
peeratop@gmail.com
www.kamanakom.wordpress.com
If I have one hour.....
System thinking Causal Loop
Diagram (CLD)
Urban Transport
Problems
Application of
CLD in urban
transport
problems
• A way to understand how a set of related entities function
by understand their connectivity
• Contrast to the reductionist approach
• Understand root cause of problem, no just symptom
System thinking
• Entity
• Dangle (input and output)
• Positive and negative arrows
• Arrow with time lag
Entity Dangle
+ -
Causal Loop
Diagram (CLD)
CLD warnings
• Must be CAUSAL not
correlation 

• Plus arrow indicates
two entities are
moving in the same
direction

• Minus arrows indicates
two entities moving in
opposite direction
Love Trust
+
+
Tired-
ness
Sleep
-
+
Reinforcing loop
Balancing loop
Causal Loop
Diagram (CLD)
Time
Time
Love/ Trust
Sleep/tiredness
Love Trust
+
+
Affair
-
Causal Loop
Diagram (CLD)
Love/ Trust
Time
Cave
Water/food
Urban transport problem
Home
Workplace
Urban transport problem
• Using CLD to illustrate the casual links between transport
entities, their causes, and their effects.
Application of
CLD in urban
transport
problems
Congestion CLD
Private
Public transport CLD
Private
Public transport CLD
Private
Vehicle ownership CLD
Private
Private
Walking and cycling trips CLD
Private
Private
Political hindrance and infrastructure CLD
Private
Private
Urban sprawl CLD
Private
Private
Tests real policy with the CLD
• Seven measures were extracted from 19 Chiang mai's Land transport
sub-committees:
①Promotion of non-personal vehicle modes

②Road capacity expansion

③Road capacity reduction

④Relocation of traffic attractors and expansion of the city area

⑤Land use planning 

⑥Population controls 

⑦Improve transport organisation
Application of
CLD in urban
transport
problems
Impact assessment of Measure#2 Road capacity expansion
Private
Summary of measures assessed
• Two mitigations proposed have
direct effects on alleviating
transport problems;
• Two have directs effects on
aggravate the problems;
• Two with direct effects but their
impacts depends on the detail of
the polity; and
• One with indirect effects.
• Decision makers have slightly less
than 30% chance of solving the
transport problems using
mitigations that they believe will
alleviate the problem.
Types of mitigation
proposed
Effects
1. Promotion of non-
personal vehicles mode
Alleviate transport
problem
2. Road capacity
expansion
Aggravate transport
problem
3. Road capacity reduction Alleviate transport
problem
4. Relocation of traffic
attractors and expansion
of city
Aggravate transport
problem
5. Land use planning Impacts depending on
measures
6. Population control Indirect effects.
7. Improve transport
organization
Impacts depending on
measures
Conclusion
• Problem understanding increases the chance to solve it
• System thinking and CLD are tools to help better
problems understanding and to solve them
• Can be used with partial differential equations or other
mathematical models to provide quantitative analysis

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Understand the complexity of urban transport problem, using the Causal Loop Diagram technique

  • 1. Understand the complexity of urban transport problem, using the Causal Loop Diagram technique Peraphan Jittrapirom, M. Eng., PhD. Candidate Technical University of Vienna peeratop@gmail.com www.kamanakom.wordpress.com
  • 2. If I have one hour.....
  • 3. System thinking Causal Loop Diagram (CLD) Urban Transport Problems Application of CLD in urban transport problems
  • 4. • A way to understand how a set of related entities function by understand their connectivity • Contrast to the reductionist approach • Understand root cause of problem, no just symptom System thinking
  • 5.
  • 6.
  • 7. • Entity • Dangle (input and output) • Positive and negative arrows • Arrow with time lag Entity Dangle + - Causal Loop Diagram (CLD)
  • 8. CLD warnings • Must be CAUSAL not correlation • Plus arrow indicates two entities are moving in the same direction • Minus arrows indicates two entities moving in opposite direction
  • 9. Love Trust + + Tired- ness Sleep - + Reinforcing loop Balancing loop Causal Loop Diagram (CLD) Time Time Love/ Trust Sleep/tiredness
  • 13. • Using CLD to illustrate the casual links between transport entities, their causes, and their effects. Application of CLD in urban transport problems
  • 18. Walking and cycling trips CLD Private Private
  • 19. Political hindrance and infrastructure CLD Private Private
  • 21. Tests real policy with the CLD • Seven measures were extracted from 19 Chiang mai's Land transport sub-committees: ①Promotion of non-personal vehicle modes ②Road capacity expansion ③Road capacity reduction ④Relocation of traffic attractors and expansion of the city area ⑤Land use planning ⑥Population controls ⑦Improve transport organisation Application of CLD in urban transport problems
  • 22. Impact assessment of Measure#2 Road capacity expansion Private
  • 23. Summary of measures assessed • Two mitigations proposed have direct effects on alleviating transport problems; • Two have directs effects on aggravate the problems; • Two with direct effects but their impacts depends on the detail of the polity; and • One with indirect effects. • Decision makers have slightly less than 30% chance of solving the transport problems using mitigations that they believe will alleviate the problem. Types of mitigation proposed Effects 1. Promotion of non- personal vehicles mode Alleviate transport problem 2. Road capacity expansion Aggravate transport problem 3. Road capacity reduction Alleviate transport problem 4. Relocation of traffic attractors and expansion of city Aggravate transport problem 5. Land use planning Impacts depending on measures 6. Population control Indirect effects. 7. Improve transport organization Impacts depending on measures
  • 24. Conclusion • Problem understanding increases the chance to solve it • System thinking and CLD are tools to help better problems understanding and to solve them • Can be used with partial differential equations or other mathematical models to provide quantitative analysis