Tim Hillel
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Household activity scheduling: choice-set generation and parameter estimation Open
Activity-based travel models that represent both household and individual decision-making involve intricate behavioural interactions and rely on complex simulation tools. As a result, calibrating their parameters presents significant metho…
Functional effects models: Accounting for preference heterogeneity in panel data with machine learning Open
In this paper, we present a general specification for Functional Effects Models, which use Machine Learning (ML) methodologies to learn individual-specific preference parameters from socio-demographic characteristics, therefore accounting …
Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning Open
Using a deep generative machine learning approach, we synthesise human activity participations and scheduling; i.e. the choices of what activities to participate in and when. Activity schedules are a core component of many applied transpor…
RUMBoost: Gradient Boosted Random Utility Models Open
This paper introduces the RUMBoost model, a novel discrete choice modelling approach that combines the interpretability and behavioural robustness of Random Utility Models (RUMs) with the generalisation and predictive ability of deep learn…
Empowering revealed preference survey with a supplementary stated preference survey: demonstration of willingness-to-pay estimation within a mode choice case Open
Mode choice models play a pivotal role in transport demand modelling and help transport planners, engineers and researchers with policy and infrastructure investment evaluation. Recent mode choice studies primarily use revealed preference …
A prediction and behavioural analysis of machine learning methods for modelling travel mode choice Open
The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes bey…
Estimating flexibility preferences to resolve temporal scheduling conflicts in activity-based modelling Open
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 cho…
ciDATGAN: Conditional Inputs for Tabular GANs Open
Conditionality has become a core component for Generative Adversarial Networks (GANs) for generating synthetic images. GANs are usually using latent conditionality to control the generation process. However, tabular data only contains mani…
Capturing trade-offs between daily scheduling choices Open
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 p…
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data Open
Synthetic data can be used in various applications, such as correcting bias datasets or replacing scarce original data for simulation purposes. Generative Adversarial Networks (GANs) are considered state-of-the-art for developing generativ…
An Early Stopping Bayesian Data Assimilation Approach for Mixed-Logit Estimation Open
The mixed-logit model is a flexible tool in transportation choice analysis, which provides valuable insights into inter and intra-individual behavioural heterogeneity. However, applications of mixed-logit models are limited by the high com…
Understanding travel mode choice: A new approach for city scale simulation Open
Understanding travel mode choice behaviour is key to effective management of transport networks, many of which are under increasing strain from rising travel demand. Conventional approaches to simulating mode choice typically make use of b…
View article: Recreating passenger mode choice-sets for transport simulation: A case study of London, UK
Recreating passenger mode choice-sets for transport simulation: A case study of London, UK Open
Urban transport infrastructure is under increasing pressure from rising travel demand in many cities worldwide. It is no longer sustainable or even economically viable to cope with increased demand by continually adding capacity to transpo…
View article: Assessing the discrepancies between recorded and commonly assumed journey times in London
Assessing the discrepancies between recorded and commonly assumed journey times in London Open
Transport models for infrastructure investment and operations planning make use of generalised trip cost to predict travel choice decisions. In cities, the most important factors in the generalised cost is trip duration. When calibrating s…