A learningbased transportation oriented simulation system

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A learning-based transportation oriented
simulation system

• Theo A. Arentze, Harry J.P. Timmermans

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Abstract

• Albatross activity-based model of
  activity-travel behavior, derived from theories
  of choice heuristics
• The model predicts which activities are conducted
  when, where, for how long, with whom and the
  transport mode involved
• Decision tree is proposed as a formalism to model
  the heuristic choice

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Conceptual considerations regarding decision
making and choice behavior

• Postulate activity participation, allocation and
  implementation fundamentally take place at the
  level of household
• Decisions
• Long term marital status, number of children,
  choice of work and workplace, purchase of
  transport mode
• Short term
• Decisions influence the generation of activity
  calendars

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Constraints

• Situational constraints cant be in two places
  at the same time
• Institutional constraints such as opening hours
• Household constraints such as bringing children
  to school
• Spatial constraints e.g. particular activities
  cannot be performed at particular locations
• Time constraints activities require some minimum
  duration
• Spatial-temporal constraints an individual
  cannot be at a particular location at the right
  time to conduct a particular activity

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Choice behavior

• Models used to rely on utility-maximization
• Albatross assumes that choice behavior is based
  on rules that are formed and continuously adapted
  through learning while the individual is
  interacting with the environment (reinforcement
  learning) or communicating with others (social
  learning).
• Exploration vs. exploitation dilemma

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Learning theory sum

• Albatross is based on a learning theory which
  implies that rules governing choice behavior are
• heuristic
• context-dependent
• adaptive in nature

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The scheduling model

• Components
• a model of the sequential decision making process
• models to compute dynamic constraints on choice
  options
• a set of decision trees representing choice
  behavior of individuals related to each step in
  the process model

a-priori defined
derived from observed choice behavior
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Assumptions

• Skeleton refers to the fixed and given part of
  the schedule
• Flexible activities optional activities added on
  the skeleton

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The process model
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The inference system

• For each decision, the model evaluates dynamic
  constrains
• The implementation of situational, household and
  temporal constraints is straightforward
• We will look at spacetime constraints and choice
  heuristics determining location choices

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Feasible locations (step 6)
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Heuristics

• Having defined the location choice set, the
  proposed set of heuristics then define
  alternative ways of trading-off required travel
  time against attractiveness of locations.

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Heuristics
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Decision tree induction

• Condition-action rules
• Albatross generates the best tree based on
  techniques from C4.5 (Quinlann, 1993), CART
  (Breiman et al., 1984) and CHAID (Kass, 1980)

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Deriving decisions

• Use a probabilistic assignment rule. The
  probability of selecting the q-th response for
  each new case assigned to the k-th node
  iswhere fkq is the number of training cases
  of category q at leaf node k and Nk the total
  number of training cases at that node.

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Testing the model
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Results of inducing decision trees
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Branch of time-of-day tree
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Performance of Albatross

• The eventual goodness-of-fit of the model can be
  assessed only by a comparison at the level of
  complete activity patterns
• Eventual output of Albatross is trip matrices

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Number of activities of predicted and observed
patterns
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Correlation coefficient
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Summary

• Use of decision trees for choice heuristics,
  resulting in a considerable, but varying
  improvement over a null model
• A sample size of 2000 household-days suffices to
  develop a stable model
• Transferability of the model to another context
  than in which it was developed remains to be
  studied
• Model can be extended use models of
  reinforcement learning (as opposed to supervised
  learning which are implicitly used by decision
  trees)