Bilal Farooq, Ryerson University IATBR 2018, Santa Barbara

Bilal Farooq, Ryerson University
IATBR 2018, Santa Barbara

§ Topics covered
   § Route choice prediction
  § Mode choice prediction
  § Discrete-continuous mix prediction
  § Spatial structure of travel/activities
  § Travel/activity pattern inference

§ Artificial Neural Networks [2 paper], DNN [3], CNN [1], RBM [1]
§ Decision Tree/Random Forest [3]
§ Clustering approaches [3]
§ Ensemble machines [1]

1. What is the scope of data-driven learning in the context of travel
   behaviour modelling
2. What are the key gaps in the research?
3. Develop three research projects that address these gaps

§ July 16, 2018
   § 4:00-4:15PM   Introduction to the workshop
  § 4:15-4:30PM    Participants introduction
  § 4:30-5:15PM    Developing the problem statement
  § 5:15-6:30PM    Identifying key research gaps

§ July 18, 2018
   § 4:00-4:15PM   Recap of the workshop
  § 4:15-4:20PM    Formation of three groups
  § 4:20-4:50PM    Research projects sketch
  § 4:50-5:00PM    Presentation/feedback
  § 5:00-6:00PM    Interaction with time use and travel workshop

§ Dr. Shadi Djavadian (Ryerson)
§ Melvin Wong (Ryerson)
§ Georges Sfeir (AUB)
§ Vishnu Baburajan (IST)

Discriminative models
§ Good for:
  § Extraction and analysis of travel patterns
    § Purpose of trip
    § Mode of transportation
    § Travel activity/diary
  § Classification of travellers
§ Key advantages in the case of activity/mobility surveys using GPS
 data from smartphone
 § Cheaper
 § Managing big data sources
 §…

Discriminative models
§ Classifying major modes/purpose only
  § Ignoring the purpose since it’s not an easy task to detect?
  § Abstract representation of purpose
§ Such models good for capturing unique patterns
   § Our responsibility to put semantic meaning to them

Generative models
§ Good for:
  § Predictive modelling
 § Exploring the distribution and correlations of variables
 § Dealing with missing data
 § Population synthesis
 § Merging multiple data sources
§ Spatio-temporal transferability of models
   § Assumption that behaviour remains the same

Generative models
§ Imbalance data: applications can be risky
   § Case of elderly population without smartphones
  § Use of probabilistic models based on historical data to predict
   missing part of the data (e.g. When phone is off)
§ Such models can be useful for diagnostics
   § Case of identification of latent classes

§ When and how to use data-driven learning techniques?
  § Alchemy!
§ Interpretation of the model; what can be done with them and what
 cannot; and what is their use
§ Incorporating dynamics in data-driven models
   § Beyond LSTM/time series models
§ Use in forecasting (especially the generative models)
§ Data-driven estimation techniques for hypothesis-driven modelling
  § Advancements in stochastic gradient decent

§ Exploring the abstract representation of travel purpose (and mode)
§ Benchmark datasets
   § Openly available datasets from North America, Europe, Asia
§ Using such techniques to capture unexplainable dimensions of
 hypothesis-driven modelling
§ Individual specific modelling
   § Rich longitudinal data on individuals
§ Privacy preserved model estimation
§ Incorporating context-aware variables in data-driven approaches

§ Improving predictive accuracy of discrete choice models with
 machine learning while maintaining interpretability
 § Exploration of hybrid model formulations
§ Selection processes for variables/features for interpretable and
 uninterpretable parts of utility function
§ Exploration of models for the uninterpretable information
§ Trade-off analysis

§ Benchmark dataset for comparative analysis
§ Definition of dataset
  § Which decision variable? Or families of decisions?
    § Balanced data
  § What location?
  § 1 day vs multiple days
  § Size of data
  § Related transportation data
§ Role of Kaggle sort of data repositories
§ Use of synthetic data?
§ Predictive power: what usage and and what cost

§ To what extent is privacy important in travel behaviour?
§ What could be the implications of masking/filtering private data in
 travel behaviour?
§ Training of privacy aware and counterpart models
§ Quantification of:
  § Improvement in privacy
  § Semantic data needs

§ Joint discussion on:
   § Theory/Hypothesis-driven and Data-driven approaches
§ Large dataset can inspire new theories
§ Predictability vs Transferability
§ Interpretability
   § What’s inside!?
§ Bayesian origin of machine learning
§ What problems are good to use this tool and what are not?