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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".
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1Challenge the future
Do ‘normal’ traffic conditions really exist?
Why modelling variation & uncertainty is not a choice
S.C. Calvert MSc
TU Delft Masterclass May 2012
2Challenge the future
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.
3Challenge the future
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
4Challenge the future
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?
5Challenge the future
1.46 1.47 1.48 1.49 1.5 1.51 1.52 1.53 1.54 1.55
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Is ‘normal’ traffic flow good enough?
• Demonstration of variations present on roads
Traffic demand:
Congestion:
6Challenge the future
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
source: Calvert, Taale, Snelder & Hoogendoorn (2012)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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Cumulative probability
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7Challenge the future
Is ‘normal’ traffic flow good enough?
• Results:
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)
Scenario Median Travel times (minutes)
Average Travel times (minutes)
Variation in input 20.23 23.98No variation in input 18.16 18.16
8Challenge the future
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
?
9Challenge the future
Modelling variation using probability
• Traffic modelling -> macroscopic / microscopic
10Challenge the future
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
• INPUT: OUTPUT:
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
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Cumulative probability
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Network delay (vehicle hours)
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Histogram of network delay (Systemtic sampling)
11Challenge the future
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
12Challenge the future
Supervision
• MSc:
• 1 or 2 daily supervisors & professor
• PhD:
• 1 or 2 daily supervisors & promoter
• High degree of independence expected
13Challenge the future
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!
14Challenge the future
Opportunities / Interested?
• MSc-thesis project internship
• @TNO (or @ITS Edulab)
• Email: [email protected]
15Challenge the future