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?