New perspectives on the performance of machine learning classifiers for mode choice prediction: An experimental review

It appears to be a commonly held belief that Machine Learning (ML) classification algorithms should achieve substantially higher predictive performance than manually specified Random Utility Models (RUMs) for choice modelling. This belief is supported by several papers in the mode choice literature, which highlight stand-out performance of non-linear ML classifiers compared with linear models. However, many studies which compare ML classifiers with linear models have a fundamental flaw in how they validate models on out-of-sample data. This paper investigates the implications of this issue by repeating the experiments of three past papers using two different sampling methods for panel data. The results indicate that using trip-wise sampling with travel diary data causes significant data leakage. Furthermore, the results demonstrate that this data leakage introduces substantial bias in model performance estimates, particularly for flexible non-linear classifiers. Grouped sampling is found to address the issues associated with trip-wise sampling and provides reliable estimates of true Out-Of-Sample (OOS) predictive performance. Whilst the results from this study indicate that there is a slight predictive performance advantage of non-linear classifiers over linear Logistic Regression (LR) models, this advantage is much more modest than has been suggested by previous investigations

From domestic energy demand to household activity patterns

Residential building energy usage can be considered as being derived from the activity patterns of individuals inside the home. As such an activity-based energy demand model that can create in-home energy usage profiles from household activity patterns is the key to a better building energy demand analysis. In order to find the relation between building energy usage and activity profiles, energy usage data with an overlapping activity diary survey is needed. However, there is no detailed data containing information on both household activity schedules and energy usage. Therefore, utilizing a Bayesian approach, we explore the possibility of reverse engineering to get the household activity patterns from energy usage profiles. The findings can be further used for linking the domestic energy demand to the activity schedules of the occupants

Integrated in- and out-of-home scheduling framework: A utility optimization-based approach

Existing activity-based modeling predominantly focus on out-of-home activities in order to understand 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 single scheduling framework. This approach has two main benefits: Firstly, it can capture the trade-offs between in-home and out-of-home activities. Secondly, in-home time-use patterns can be used to model high resolution energy demand. Our work builds on an existing optimisation framework, which treats individuals as maximising their total utility from completed activities and incorporates multiple scheduling decisions simultaneously. The approach is tested on a set of detailed daily schedules extracted from the the 2016-2020 UK Time Use Survey data. The results show that the model is able to generate peoples’ daily activity schedules based on their individual preferences and constraints

From one-day to multiday activity scheduling: Extending the OASIS framework

Applications of activity-based models for the estimation of transport demand have demonstrated to achieve greater behavioural realism than traditional trip-based models. However, state-of-the art models focus on single-day schedules as focal points to their estimations, thus ignoring fundamental dynamics that explain individual behaviour over longer periods of time. Several authors have highlighted the importance of multiday analyses in activity-travel contexts, which are still lacking in many state-of-the-art framework. In this paper, we present an extension of the OASIS framework, an integrated model for the simulation of single day schedules, to include intrapersonal interactions influencing longer term decisions. We formulate the multiday problem as a multiobjective optimisation problem where each day d is associated with a utility Ud. We consider an activity-based set up where individuals maximise the total utility of their schedules over multiple days (e.g. week). We discuss implications and requirements of this formulation, and illustrate the methodology with chosen examples

Integrated in- and out-of-home scheduling framework: A utility optimization-based approach

Existing activity-based modeling predominantly focus on out-of-home activities in order to understand 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 single scheduling framework. This approach has two main benefits and applications: Firstly, it can capture the trade-offs between in-home and out-of-home activities. Secondly, in-home time-use patterns can be used to model high resolution energy demand, which can contribute to demand side management. Our work builds on an existing optimisation framework, which treats individuals as maximising their total utility from completed activities and incorporates multiple scheduling decisions simultaneously. The framework has been extended to determine the choice of location for activities such as work, study, and leisure. The approach is tested on a set of detailed daily schedules extracted from the 2016-2020 UK Time Use Survey data. The results show that the model is able to generate peoples’ daily activity schedules based on their preferences and constraints

Simulating intra-household interactions for in- and out-of-home activity scheduling

Various interactions, time arrangements, and constraints exist for individuals scheduling their day as a member of a household, which affect their in-home as well as out-of-home activity schedule. However, the existing activity-based models are mostly based on the individual decision-making process, which are limited in their demonstration of behaviour. We simulate multiple intra-household interaction dimensions within the same framework and capture the coordination of the activity scheduling decisions among all household members. Our approach adopts the Optimisation-based Activity Scheduling Integrating Simultaneous choice dimensions (OASIS) framework, which is at the level of isolated individuals and focuses on out-of-home activity schedules. We jointly simulate in- and out-of-home activities and incorporate interactions into the framework. Our framework contributes to the state-of-the-art in activity-based modelling by explicitly capturing multiple interactions within the same model, such as the allocation of the private vehicle to household members, dividing household maintenance responsibilities, escorting, joint activity participation, and sharing rides. We operationalise the model using time-use-survey data from the United Kingdom. The simulation results demonstrate the ability of the framework to capture complex intra-household interactions. We then demonstrate how these interactions can cause individuals to deviate from their schedules planned in isolation. This is a general framework applicable to different household compositions and available resources

Choice set generation for activity-based models

Activity-based models have seen a significant increase in research focus in the past decade. Based on the fundamental assumption that travel demand is derived from the need to do activities and time and space constraints (Hägerstraand, 1970, Chapin, 1974). ABM offer a more flexible and behaviourally centred alternative to traditional trip-based approaches. Econometric — or utility-based — activity-based models (e.g., Adler and Ben-Akiva, 1979, Bowman and BenAkiva, 2001) postulate that the process of activity generation and scheduling can be modelled as discrete choices. Individuals derive a utility from performing activities, and they schedule them as to maximise the total utility of the schedule. In classical discrete choice model applications, the parameters of the utility functions can be estimated by deriving their maximum likelihood estimators. As the likelihood function is defined over a full enumeration of the alternatives in the choice set, this approach is limited for activity-based applications: the set of possible activities and their spatio-temporal sequence is combinatorial and not fully observed by either the decision-maker or the modeller. While discrete choice models can be estimated over samples of alternatives (e.g., Guevara and Ben-Akiva, 2013) an appropriate definition of such sample is as crucial as it is challenging. This paper presents a methodology to sample a choice set of full daily schedules for a given individual and a list of activities. The Metropolis-Hastings algorithm allows us to explore the space efficiently and draw both high and lower probability alternatives for consistent estimation of the parameters. The methodology is tested on a sample of individuals from the 2015 Swiss Mobility and Transport Microcensus (Office fédéral de la statistique and Office fédéral du développement Territorial, 2017)

Integrated models of transport and energy demand: A literature review and framework

Energy and transport demand can both be considered as being derived from an individual’s activity participation. As such, both energy and transport demand are inherently linked: completing activities inside the home generates residential energy demand, where completing activities outside the home generates transportation and non-residential energy demand. Whilst there are several works in the literature that focus on either energy or transportation demand, there remain very few studies which explicitly investigate their interaction. To address this need, in this paper we conduct in-depth literature review of transportation and energy demand modeling. The review analyses the methodologies employed within each domain in order to (a) establish the state-of-research for energy demand modeling and (b) identify the suitable opportunities for joining these two domains. Drawing on a review of the current papers, we identify four key areas of practice: (i) activity scheduling, (ii) building energy demand, (iii) transportation energy demand, and (iv) the integration of components. Finally, based on the findings from the review, we propose a new framework for joint building and transportation energy demand modeling at an urban scale

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

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 subsequent choices, oversimplifying the scheduling process. In this paper we introduce OASIS, an integrated framework to simulate activity schedules by considering all choice dimensions simultaneously. We present a methodology for the estimation of the parameters of an activity-based model from historic data, allowing for the generation of realistic and consistent daily mobility schedules. The estimation process has two main elements: (i) choice set generation, using the Metropolis-Hasting algorithm, and (ii) estimation of the maximum likelihood estimators of the parameters. We test our approach by estimating parameters of multiple utility specifications for a sample of individuals from a Swiss nationwide travel survey, and evaluating the output of the OASIS model against realised schedules from the data. The results demonstrate the ability of the new framework to simulate realistic distributions of activity schedules, and estimate stable and significant parameters from historic data that are consistent with behavioural theory. This work opens the way for future developments of activity-based models, where a great deal of constraints can be explicitly included in the modelling framework, and all choice dimensions are handled simultaneously

DATGAN: Integrating expert knowledge into deeplearning for population synthesis

Agent-based simulations and activity-based models used to analyse nationwide transport networks require detailed synthetic populations. These applications are becoming more and more complex and thus require more precise synthetic data. However, standard statistical techniques such as Iterative Proportional Fitting (IPF) or Gibbs sampling fail to provide data with a high enough standard, e.g. these techniques fail to generate rare combinations of attributes, also known as sampling zeros in the literature. Researchers have, thus, been investigating new deep learning techniques such as Generative Adversarial Networks (GANs) for population synthesis. These methods have already shown great success in other fields. However, one fundamental limitation is that GANs are data-driven techniques, and it is thus not possible to integrate expert knowledge in the data generation process. This can lead to the following issues: lack of representativity in the generated data, the introduction of bias, and the possibility of overfitting the sample’s noise. To address these limitations, we present the Directed Acyclic Tabular GAN (DATGAN) to integrate expert knowledge in deep learning models for synthetic populations. This approach allows the interactions between variables to be specified explicitly using a Directed Acyclic Graph (DAG). The DAG is then converted to a network of modified Long Short-Term Memory (LSTM) cells. Two types of multi-input LSTM cells have been developed to allow such structure in the generator. The DATGAN is then tested on the Chicago travel survey dataset. We show that our model outperforms state-of-the-art methods on Machine Learning efficacy and statistical metrics