calvert, do ‘normal’ traffic conditions really exist? why modelling variation & uncertainty...

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


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.


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


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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• Demonstration of variations present on roads

Traffic demand:

Congestion:



• 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)

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Cumulative probability

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• 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


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

?


Modelling variation using probability

• Traffic modelling -> macroscopic / microscopic



• 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:

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Network delay (vehicle hours)

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Histogram of network delay (Systemtic sampling)



• 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


Supervision

• MSc:

• 1 or 2 daily supervisors & professor

• PhD:

• 1 or 2 daily supervisors & promoter

• High degree of independence expected


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!


Opportunities / Interested?

• MSc-thesis project internship

• @TNO (or @ITS Edulab)

• Email: [email protected]

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