Timothy Michael Hillel

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We propose a new modelling approach for daily activity scheduling which integrates the different daily scheduling choice dimensions (activity participation, location, schedule, duration and transportation mode) into a single optimisation problem. The funda ...

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between sub ...

Discrete Choice Models (DCMs) have a distinct advantage over Machine Learning (ML) classification algorithms, in that they employ a highly interpretable linear structure. However, a key drawback of DCMs compared to ML is the need to specify the utility fun ...

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables ...

Existing activity-based modeling predominantly focus on out-of-home activities in order to un- derstand transport demand. In this research, we extend the state of practice in activity-based modelling by determining both in- and out-of-home activities in a ...

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research ques ...

Since the 70s, there has been a growing interest in activity-based modelling. This approach models the need to travel as a result of performing daily activities (Bowman and Ben-Akiva, 2001). Nevertheless, the activities need to be scheduled which involves ...

This paper presents a novel activity-based demand model that combines an optimisation framework for continuous temporal scheduling decisions (i.e. activity timings and durations) with traditional discrete choice models for non-temporal choice dimensions (i ...

Timothy Michael Hillel

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Capturing trade-offs between daily scheduling choices

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Weak teachers: assisted specification of discrete choice models using ensemble learning algorithms

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Assisted specification of discrete choice models

A utility optimization-based framework for joint in- and out-of-home scheduling

A systematic review of machine learning classification methodologies for modelling passenger mode choice

Representing location choice within activity-based models

Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling

Household-level choice-set generation and parameter estimation in activity-based models

Integration of group decision-mechanisms into activity-based models

Modelling mobility tool availability at a household and individual level; A case study of Switzerland

An Early Stopping Bayesian Data Assimilation Approach for improved Mixed Multinomial Logit transferability