Presentation by Simeon Calvert in Masterclass on 16 May 2012 on his phd research on probabilistic traffic flow models and "Help I've got a supervisor".

1. Do ‘normal’ traffic conditions really exist?
Why modelling variation & uncertainty is not a choice
S.C. Calvert MSc
TU Delft Masterclass May 2012
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2. Who am I?
Simeon Calvert, MSc, 28 yrs
• Traffic researcher at TNO & PhD-candidate at TU Delft
• Graduated at TU Delft, Transport & Planning, 2010.
• Specialisation in Traffic flow theory and traffic modelling
• PhD-subject is on probabilistic traffic flow modelling.
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3. Contents for today
• Is considering ‘normal’ traffic conditions good enough?
• Demonstration of variation in traffic flow
• Focus of my research
• Modelling variation using probability
• Supervision
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4. Is ‘normal’ traffic flow good enough?
• Traffic is affected by day-to-day variations in traffic demand,
weather conditions, road works, etc.
• Normal practice: take an average/representative situation
• Is it sufficient to consider normal traffic conditions
when modelling traffic? And why?
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5. Is ‘normal’ traffic flow good enough?
• Demonstration of variations present on roads
8000
7000
7000
6000 Traffic demand:
6000
5000
5000
4000
4000
Congestion:
3000
3000
2000
2000
1000
1000
0 0
0 1 2 3 4 5 6
5
x 10
1.46 1.47 1.48 1.49 1.5 1.51 1.52 1.53 1.54 1.55
5
x 10
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6. Is ‘normal’ traffic flow good enough?
• Capacity varied due to different weather conditions
• Two scenarios with same input, but one varied and one the
‘average’ situation
1
0.9
0.8
0.7
Capacity factor
0.6
0.5
0.4
0.3
0.2
0.1
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Cumulative probability
source: Calvert, Taale, Snelder & Hoogendoorn (2012)
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7. Is ‘normal’ traffic flow good enough?
• Results:
Scenario Median Travel times Average Travel times
(minutes) (minutes)
Variation in input 20.23 23.98
No variation in input 18.16 18.16
source: Calvert, Taale, Snelder & Hoogendoorn (2012)
• Varied capacity leads to a (much) higher travel time!
• Non-varied capacity does not sufficiently consider delays
• NB: This is often the case, but not always! -Calvert & Taale (2012)
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8. Focus of my research
• Effects of (external) events on traffic flow
• Such as weather, daily demand variation, incidents, …
• Data analysis
• Modelling variation in traffic
• Models used for planning/forecasting & evaluation
• Presentation of probabilistic model results
• Especially for policy-makers
?
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9. Modelling variation using probability
• Traffic modelling -> macroscopic / microscopic
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10. Modelling variation using probability
• Advanced Monte Carlo
• Many simulations, with a different combination of input values
for each simulation
• ‘Advanced’ refers to clever ways of selecting the input values
Histogram of network delay (Systemtic sampling)
1 12
• INPUT: 0.9
OUTPUT: 10
0.8
0.7
8
Capacity factor
0.6
Frequency
0.5 6
0.4
4
0.3
0.2
2
0.1
0 0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 1 2 3 4 5 6
Cumulative probability Network delay (vehicle hours) 4
x 10
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11. Modelling variation using probability
• Core probability
• Variation is calculated in the core of the model as sets of
probability distributions
• Faster, completer, ☺ … but harder to implement
Congestion if:
K > Kcritical = 25 veh/km
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12. Supervision
• MSc:
• 1 or 2 daily supervisors & professor
• PhD:
• 1 or 2 daily supervisors & promoter
• High degree of independence expected
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13. Supervision
• Important for dealing with supervisors:
• Make clear arrangements (about meetings, input, reporting, …)
• Be independent, but keep regular contact with supervisor(s)
• Remember: supervisors have been there before!
• …but you do have a say how you progress.
• Serving two masters (conflicting interests)*
• Sort out problems quickly, don’t ignore them!
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14. Opportunities / Interested?
• MSc-thesis project internship
• @TNO (or @ITS Edulab)
• Email: simeon.calvert@tno.nl
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