Read the summary and the most important questions on Transportation and Spatial Modelling

• Basic Concepts

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• What assumption does the MNL hold in terms of different alternatives?

  That all different alternatives should be i.i.a --> independence of irrelevant alternatives.
  If red/blue bus problem --> Nested logit is more suitable.

• What does an alternative specific constant represent? (ASC)

  ASC represents a preference or reluctance (-/+) for that alternative. It captures attributes that are not included in the systematic utility function.
  It works on differences between alternatives.

• What does a nest parameter of 1 implies?

  A nest parameter that equals 1 states that there's NO NEST and that the alternatives within the nest should be considered as independent alternatives.

• How do you adapt a unimodal doubly constrained gravity model when expanding from 3-stage to a 4-stage model by introducing a logit-model based mode choice model

  - input gravity model is skim of the network
  - 3 stage the skim is the skim of the car network
  - multimodal case it should be a kind of aggregated skim of both modes
  - since a logit mode choice is modelled the logsum of the mode choice model could be used as a measure of the aggregated skim. Of course the parameters of the detterence function needs to be adapted as well.

• Supply modeling

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• What to do in a system optimal solutions when coming from DUE?

  Replace travel time function with marginal cost function

• What does that duality gap aim for?

  Minimization of the differences between the a priori matrix and the estimated matrix. This objective thus minimises the sum of the square of the differences at OD-level.

• Overall Summary

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• What are one disadvantage and one advantage of probit assignment?

  - Advantage: probit assignment accounts for overlap of routes
  - Disadvantage: takes up a lot of computing time than logit

• Describe how the elasticity Ek and it value range could be interpreted (of multiplier)

  Ek is the reliability of the count: i.e. value range between 0 and 1. 1 being a fully reliable counts and 0 being a non reliable count. 
  
  Multiplier = to achieve a better match between flows and traffic counts

• Describe algorithm steps if there's only one count per OD pair

  
  
  
  
  

  1. Assign the a priori OD-matrix
     
  2. Per count:
     2a Determine the multipliers per count
     2b Multiply the OD-pairs passing the count with the corresponding
     multiplier
     

  The resulting OD-matrix then yields a perfect match with the counts. 

• Algorithm with weighted averages of multipliers at OD levels

  
  
  
  
  

  1. Assign the a priori OD-matrix
     
  2. Determine the multiplier for each count
     
  3. For each OD-pair
     3a Determine the weighted average of the multipliers
     3b Multiply the trips for this OD-pair with this weighted average
     
  4. Assign the updated OD-matrix and go to step 2