Activity-based Modelling: An overview (and some things we have been doing to advance state-of-the-art)

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Activity-based ModellingAn overview (and some
things we have been doing to advance
state-of-the-art)
E. Zwerts (With the cooperation of E. Moons and
D.Janssens) Transportation Research
Institute Data Analysis and Modelling Group,
Faculty of Applied Economic Sciences, Limburgs
Universitair Centrum, Diepenbeek, Belgium,
E-mail enid.zwerts_at_luc.ac.be
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Outline

• Why transportation modelling?
• Which kinds of transportation modelling?
• Why activity-based transportation modelling?
• Which activity-based transportation model?
• Model Selection Albatross
• What is Albatross?
• Things what we have been doing and are still
  going to do with respect to Albatross
• Introduction of an alternative modelling approach
  based on sequential dependencies in data (short
  version)

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Why transportation modelling?

• Transportation problem is multi-dimensional
• Traffic jams
• CO2-emissions
• Impact on economy
• Traffic accidents with significant number of
  casualties in Belgium
• ? The need for transportation infrastructure is
  high, due to
• Globalization
• Urbanization
• Governments cannot afford transportation
  constraints to have a negative impact on future
  competiteveness, foreign investments,
• However, changing the existing infrastructure is
• Expensive, have significant long-term effects
• No guarantee for succes
• Not trivial (existing spatial zones, restricted
  by local and federal regulations, legislation,
  etc.)

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Why transportation modelling?

• ?Therefore transportation models are often used.
  They can
• Support management decision making
• Make predictions in uncertain circumstances
• Changing infrastructure, environment
• Changing behaviour of people
• Changing socio-demographic circumstances
• ...
• The aim for these models is to portray reality as
  accurate as possible
• They are frequently used in different countries

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Transportation modelling Trip-based Approach
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Transportation modelling Tour-based Approach

• Trips that start and end from home or from the
  same work-location are modelled independent
• Direction (spatial) limitations
• No temporal dimension
• Independent tours, model is not capable of
  making the integration
• Uses Nested logit techniques

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Transport modelling An activity-based approach

• Travel demand is derived from the activities that
  individuals need/wish to perform
• Sequences or patterns of behaviour, and not
  individual trips are the unit of analysis
• Household and other social structures influence
  travel and activity behaviour
• Spatial, temporal, transportation and
  interpersonal interdependencies constrain
  activity/travel behaviour
• Activity-based approaches reflect the scheduling
  of activities in time and space.
• ? Activity-based approaches aim at predicting
  which activities are conducted where, when, for
  how long, with whom, the transport mode involved
  and ideally also the implied route decisions.

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Which Activity-based transportation model?

• Utility maximizing models
• Sequential models (computational process models)

ppredicted by the model nnot treated in model
gassumed given in model
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ALBATROSS

• Albatross A learning based transportation
  oriented simulation system
• activity-based model of activity-travel
  behavior, derived from theories of choice
  heuristics
• Developped in the Netherlands (Arentze,
  Timmermans 2000)
• The model predicts which activities are conducted
  when, where, for how long, with whom and also
  transport mode
• Decision tree is proposed as a formalism to model
  the heuristic choice
• ?Obviously, this is a crucial component of the
  model. The better the learning algorithm, the
  better the prediction

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Constraints that have been taken into account in
Albatross

• 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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Modelling 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).
• As said, rules are currently derived from
  decision trees
• Other rule-based learning algorithms can also be
  used

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The scheduling model
Aim Determine the schedule (agenda) of
activity-travel behaviour

• 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
Skeleton refers to the fixed and given part of
the schedule Flexible activities optional
activities added on the skeleton
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The sequential decision process(process model)
Each oval represents a DT
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Example
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The inference system in Albatross

• For each decision, the model evaluates dynamic
  constraints
• 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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Albatross derives DT based on Chaid-learning
algorithm
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 OD- trip matrices
• Conclusions till here
• 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

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Advance the state of the art

• Some things what we have been doing in our
  research group with respect to Albatross
• Two other rule-based techniques applied in the
  context of the Albatross model
• Integrate Decision tree techniques and feature
  selection Identify irrelevant attributes and
  build simple models
• Build advanced complex models by means of
  Bayesian networks and try to improve accuracy
• Use (and adapt) Albatross towards the
  application area of Flanders
• Evaluate the performance of activity-based
  models versus trip-based models

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Application 1 Build simple models by means of DT
and feature selection

• General idea Occams razor Entities are not to
  be multiplied beyond necessity
• ? Large set of attributes
• - likely to be correlated
• - larger trees, but not necessary better !
• ? Use feature selection techniques to identify
  irrelevant attributes that do not significantly
  improve accuracy and can thus be omitted in the
  final model

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Application 1 Empirical results

• Build a DT for every decision facet in the
  Albatross model
• Example location-facet

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Application 1 Empirical results
Full approach  
FS approach  
 
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Application 1 Empirical results
Model performance at activity pattern level  
? Conclusion There is no evidence of substantial
loss in predictive power when trimmed decision
trees are used to predict activity-travel
patterns.
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Application 2 Build complex models by means of
BN and try to improve accuracy

• General idea Modelling travelling behaviour is
  non-trivial as it is multidimensional and complex
  in nature. Hidden, unknown relationships might
  have an impact on the final outcome
• Need for a technique that is able to deal with
  this Bayesian networks
• Able to capture (complex) relationships between
  variables
• Able to be learned from data
• Visualize interdependences between variables
• Prior and posterior probability distributions per
  variable
• Well suited to conduct what-if scenarios and
  sensitivity analysis
• White box

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• Case study on mode choice facet

Steps to follow (1) Build the network
(Structural Learning), (2) Choose a target
variable and prune the network, (3) Calculate
probability distributions (Parameter Learning),
(4) Perform what-if scenarios by entering
evidences in the network
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Example of pruning a network
?
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Application 2 Empirical results
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Application 2 empirical results

• Conclusions
• Better predictions
• ? Reason Unlike decision trees (CHAID),
  variables are selected simultaneously, no
  hierarchy of importance of the selected variables
• Selection of the variables /- the same in both
  approaches (? difference in performance more due
  to different nature than to additional insights)
• Much larger number decision rules in Albatross
  compared with CHAID, however performance is also
  OK on the test data(? additional research on
  other datasets is warranted)
• Interpretation is an issue, BN link several
  variables in sometimes complex direct and
  indirect ways.

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Application 3 Activity-based versus trip-based

• Use (and adapt) Albatross towards the application
  area of Flanders
• Evaluate the performance of activity-based models
  versus trip-based models
• Transportation models trip based
• Mobility Plan Flanders (2003)
• Predict in a static way reliable results for
  distribution, substitution and route effects
• They cannot manage generative and temporal
  shiftings
• Need for a more dynamic and more complete model

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• Activity based models
• Travel demand is derived from activities
• 24 hour schedule with activities
• Household interaction
• Time and space constraints

• Trip based models
• Just consider one-way trips
• Only during peak hour
• Individual trips
• Calibration is needed to fit the data to the real
  situation

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• But ...
• Trip based models take the outcomes (traffic
  flows, passengers numbers, ... ) as input in the
  calibration
• As expected, the outcomes are robust and fit the
  actual situation perfect
• The influence of the calibration is much stronger
  than the influence of the input data

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• Aim the application of an activity based model
  in Flanders
• Albatross ? developed for the Dutch situation
• First stage use of the Dutch decision tables
• Comparison of the results of the two model types
  and their performance on the same input data

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• Data
• Travel behaviour study urban region of Leuven
  (2001) trip-based model
• Trip schedules (no information on in-home
  activities)
• Locations zip code ? statistical sector
• Assumptions
• Overestimation of car and bike availability per
  household
• Standard values for work time
• Transport mode longest distance in the trip
• Facility data not yet available

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• Assumption trip based models predict the actual
  situation almost perfect
• ALBATROSS
• Mean length of the schedules is shorter than in
  the Dutch example (reason conversion trip
  schedule to an activity schedule)
• SAM values (parameters for Goodness-of-Fit) are
  very high ?predictions are not good

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• OD matrices match reasonably well
• Activity type
• Good predictions for work and bring and get
• Grocery and non-grocery is a problem
• Length of tours
• Predict too much short tours (lt 2 km)
• Transport mode
• Too much public transport and car passengers
• Too little car drivers

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• Predictions are not good fortunately!
• Refinement of input data/ facility data
• Adaptation to the Flemish situation of the
  decision tables
• Trip based model runs without traffic flows and
  passenger numbers for a real fair comparison
• Run model on other Flemish regions

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Some words on what else we have been doing

• An alternative approach to model activity-travel
  decisions is also under development at our
  research group
• This model assumes that each diary consists of
  correlated successive activities.
• For instance during morning Sleep-Having
  Breakfast-Transportation to work

• Markov chains are often used to model this type
  of dependences
• Transition Matrix
• First-order Markov Chain

• Transition Matrix
• Second-order Markov Chain
• Etc.

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Artificial Example
Diary 1 TcFFFFFFFFFFFFFFFFE Diary 2
TcEEFREREERFTcFTcFFTcFETcF Diary 3
RREFEFEETcTcR Diary 4 EEFFTcFTcFRRTcTcRTcRR Diary
5 FFTcFFRE Diary 6 EETcFRRE
With Tc Transportation, with car as transport
mode, Fvisit Family, EEat, RRead
Tc E R F
Tc 0.11 0.03 0.16 0.70 E
0.23 0.40 0.08 0.29 R
0.10 0.53 0.30 0.07 F
0.21 0.20 0.28 0.31  
These probabilities can be computed by means of
Markov Chains
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Example derived from data

• Simulation procedure Simulate Xt as a function
  of the values taken by Xt-1 and Xt-2 ? Repetitive
  procedure

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• Some results

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Lets recapture things

• Why transportation modelling?
• Which kinds of transportation modelling?
• Why activity-based transportation modelling?
• Which activity-based transportation model?
• Model Selection Albatross
• What is Albatross?
• Things what we have been doing and are still
  going to do wrt Albatross
• Introduction of an alternative modelling approach
  based on sequential dependencies in data (short
  version)

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• Questions?