- CMC Online Seminar Series
The Choice Modelling Centre (CMC) is pleased to introduce a new online seminar series. The seminars will cover all aspects of choice modelling and will be aimed at a cross-disciplinary audience. In general, there will be one seminar per week, on a Tuesday. With some exceptions, such as speakers from East Asia and Australia, the seminars will take place at 3pm UK time.
Attending the seminars
Anyone who is interested is free to attend the online seminars. To help us with planning and capacity, for each seminar, there is a ‘register’ link, which will take you to a short online booking form. By registering, you will be sent an email two hours before the seminar is due to start with the link to attend the seminar (which will be hosted on Microsoft Teams). Where possible, we will add recordings of the seminars in the ‘previous seminars’ section below.
Who is presenting?
We seek to be inclusive in our selection of speakers, covering all topic areas where choice modelling is used, and also giving opportunities to PhD students and early career researchers to present their work. Our only criteria for selecting speakers are the quality of the work and the likely appeal to the wider community of choice modellers.
We have put together an exciting initial set of 13 seminars, covering the period from 23 June to 15 September (summary of seminars). New speakers will be added over time, and we are also happy for those interested in giving a talk to approach us directly at email@example.com
Details of future seminars to follow soon!
Please note that the videos below will work best if you either use the pop out option or download the video.
[22-09-2020] Michael Maness (University of South Florida) Can my activity choices be explained through what my friends offer? Exploring a Social Capital Theory of leisure activity.
Michael Maness (University of South Florida)
Can my activity choices be explained through what my friends offer? Exploring a Social Capital Theory of leisure activity.
NEW DATE: Tuesday 22nd September, 3-4pm (UK time) [register]
Abstract: In this presentation, two hypotheses are explored: (1) social capital is an integral determinant of leisure activity participation and (2) having access to instrumental social resources promotes instrumental outcomes demonstrated by increased leisure activity variety. Using indicators from both a position generator and resource generator, we find support for the influence of social capital in leisure activity behavior using a web-based survey of activity behavior. As social capital has distinct impacts even among homogeneous groups, we propose that choice modelers can derive insights from social capital measures to build more socially and behaviorally realistic models.
About: Michael Maness is an assistant professor of Civil and Environmental Engineering at the University of South Florida. His research programs centers on exploring the social sustainability and social resilience of technical systems by incorporating social interactions into behavioral modeling. Dr. Maness was awarded the 2015 Eric Pas Dissertation Prize and he has published research in leading transportation journals on advanced choice models with applications to car ownership, managed lanes, cycling behavior, activity behavior, and communication behavior.
[15-09-2020] Akshay Vij (University of South Australia) Modelling the dynamics of preference change
Akshay Vij (University of South Australia)
Modelling the dynamics of preference change
Tuesday 15th September, 11am-12pm (UK time)
Abstract: Choice models have traditionally assumed that individual preferences are inherently stable over time. The transformative nature of recent global events calls this assumption into serious question. This talk discusses how to repurpose commonly available datasets and econometric frameworks to explicitly model the dynamics of preference change.
About: Dr. Akshay Vij is a senior research fellow at the Institute for Choice, University of South Australia. He has previously worked as lecturer and post-doctoral scholar at the University of California (UC), Berkeley, with joint research appointments at the Institute of Transportation Studies and the Institute of Urban and Regional Development. He received his Ph.D. in Civil and Environmental Engineering, also from UC, Berkeley.
[08-09-2020] Abdul R. Pinjari (Indian Institute of Science) Choice Models with Stochastic Variables and Random Coefficients.
Abdul R. Pinjari (Indian Institute of Science)
Choice Models with Stochastic Variables and Random Coefficients.
Tuesday 8th September, 3-4pm (UK time)
Abstract: This talk will examine the use of the integrated choice and latent variable (ICLV) framework for identifying utility functions with stochastic variables and random coefficients on those variables. The framework is applied to formulate a joint model of travel time and route choice, where travel time is treated as inherently stochastic and travelers’ sensitivity to time is heterogeneous. It is shown that a model that neglects stochasticity in travel time results in a downward bias in the mean and variance of sensitivity to travel time.
About: Abdul Rawoof Pinjari is an Associate Professor in Civil Engineering and Chairman of the Centre for infrastructure, Sustainable Transportation and Urban Planning (CiSTUP) at the Indian Institute of Science. His research is in mathematical modelling of human choice (consumer) behaviour in infrastructure service systems, particularly in transportation systems. He is currently Vice Chair of the International Association for Travel Behaviour Research (IATBR), an Associate Editor of Transportation Research Part B, and serves on the editorial boards of Journal of Choice Modelling, Transportation, and Transportation in Developing Economies.
[01-09-2020] Emma Frejinger (Université de Montréal) Discrete Choice Meets Discrete Optimization
Emma Frejinger (Université de Montréal)
Discrete Choice Meets Discrete Optimization
Tuesday 1st September, 3-4pm (UK time)
Abstract: One important goal with choice modelling is to use the models or the resulting predictions to solve decision-making problems. A prominent example is transport network design where a central authority makes design decisions (e.g., modifying infrastructure) and users react to those decisions by possibly changing their behaviour (e.g., route choice). This can be formulated as a bilevel optimization problem. In this talk we provide background on so-called recursive route choice models and show how they can be integrated in a bilevel formulation that allows for an effective solution approach.
About: Emma Frejinger is Associate Professor at the Department of Computer Science and Operations Research at Université de Montréal. She is the holder of the Canada Research Chair in Demand Forecasting and Optimization of Transport Systems, and the holder of the CN Chair in Optimization of Railway Operations. Most of her ongoing research activities are at the intersection between statistical learning and operations research. She is a member of CIRRELT, an associate member of Mila, and she works part-time as scientific advisor for IVADO Labs.
[25-08-2020] Abisai Konstantinus (CEO, Ndatara) Using choice modelling to inform maritime transport development in Southern Africa.
Abisai Konstantinus (CEO, Ndatara)
Using choice modelling to inform maritime transport development in Southern Africa.
Tuesday 25th August, 3-4pm (UK time)
Abstract: In a research project aimed to assess the viability of short-sea shipping in South Africa, seven different choice models were developed. Each of those models addressed different research objectives. What the study revealed is the suitability of choice modelling to studies on maritime transport development in Africa.
About: Dr. Abisai Konstantinus is the CEO of Ndatara, an applied research and consultancy firm based in Swakopmund Namibia (www.ndatara.com). Abisai has 16 years combined experience in the maritime industry spent as officer on ocean-going ships and as a marine pilot in Namibia (Walvis Bay and Lüderitz). He holds a NDip: Maritime Studies (CPUT), PQE: ICS (UK), MPhil: Shipping Law (UCT), MSc: Shipping Management and Logistics (WMU), PhD Transport Engineering (UCT).
[18-08-2020] Julian Arellana (University Del Norte) Choice experiments using virtual reality: some lessons compared to traditional text and image experiments.
Julian Arellana (University Del Norte)
Choice experiments using virtual reality: some lessons compared to traditional text and image experiments.
Tuesday 18th August, 3-4pm (UK time)
Abstract: This talk will describe some benefits and drawbacks of using Virtual Reality (VR) for choice experiments. Models estimated using VR data will be compared to models estimated from data collected by traditional text and image surveys, considering two contexts to study pedestrian behaviour.
About: Julian obtained his PhD degree from the Pontifical Catholic University of Chile. He currently works as Associate Professor and Director of Research and Graduate programs in Engineering at University del Norte in Barranquilla, Colombia. His research interests include transport modelling, sustainable transport planning in the Global South, and the novel application of choice experiments.
[04-08-2020] John Buckell (University of Oxford) Smokers’ choices and addiction: a hybrid choice model approach in the US.
John Buckell (University of Oxford)
Smokers’ choices and addiction: a hybrid choice model approach in the US.
(with Professor David Hensher and Professor Stephane Hess)
Tuesday 4th August, 3-4pm (UK time)
Abstract: We use a hybrid choice model, with a latent variable capturing addiction, to study US smokers’ cigarette/e-cigarette choices. We estimate how product and nicotine preferences vary by addiction, and addiction’s impact on the US government’s proposed lowering of nicotine in cigarettes.
About: I’m a senior researcher at the Health Economics Research Centre (Nuffield Dept of Population Health) and Health Behaviours (Nuffield Dept of Primary Care Health Sciences), University of Oxford. I use experiments and secondary data to study health behaviours in tobacco, obesity, and genomics.
[28-07-2020] Adele Diederich (Jacobs University) Parallel or sequential? Modelling decision making for multiattribute alternatives.
Adele Diederich (Jacobs University)
Parallel or sequential? Modelling decision making for multiattribute alternatives.
Tuesday 28th July, 3-4pm (UK time)
Abstract: Dynamic stochastic models for decision making account for choice frequencies and decision times. However, almost all models assume that information of attributes is collapsed prior to preference accumulation. Here I propose a non-time homogeneous framework that treats the processing of attributes separately.
About: Professor of Psychology.
PhD 1991 – Purdue University and Hamburg University. Habilitation 1995 – Oldenburg University.
Chief editor of Journal of Mathematical Psychology.
[21-07-2020] Bilal Farooq (Ryerson University) Generative machine learning for discrete-continuous choice data.
Bilal Farooq (Ryerson University)
Generative machine learning for discrete-continuous choice data.
Tuesday 21st July, 3-4pm (UK time)
Abstract: We propose a plausible perspective to exploit the use of generative machine learning techniques for modelling the underlying heterogeneities in large-scale discrete-continuous (MDC) choice data. For interpretability, the conditional probabilities and elasticities are derived as well as statistical analysis on the latent construct is performed. An application on GPS based travel data indicates that the model can generate statistically similar distributions in forecasting and performs better than purely discriminative models.
About: Bilal Farooq is Canada Research Chair in Disruptive Transportation Technologies and Services. He is currently an Associate Professor in Transportation Engineering at Ryerson University, Canada and the Director of Laboratory of Innovations in Transportation (LiTrans). He has received Early Researcher Awards in Québec (2014) and Ontario (2018), Canada.
[14-07-2020] Aruna Sivakumar (Imperial College, London) Modelling the effect of social influence on choice behaviour
Aruna Sivakumar (Imperial College, London)
Modelling the effect of social influence on choice behaviour
Tuesday 14th July, 3-4pm (UK time)
Abstract: I present two empirical case studies involving the estimation of choice models for the adoption of a new transport technology/service. The first case study explores the role of peer attitudes in the individual’s intent to purchase an electric vehicle; while the second case study analyses the individual’s intent to use a pro-environmental transport mode (bike-sharing) within a cohort of students. Through these case studies the talk will identify several insights and guidelines for the inclusion of social influence within choice models.
Bio: Aruna Sivakumar is a senior lecturer in consumer behaviour and urban systems at the Centre for Transport Studies, Imperial College London. She is also director of the Urban Systems Lab, and leads several smart city and systems modelling initiatives. Her research is focused on the mathematical and statistical modelling of behaviour and demand especially as it relates to mobility and resource (energy) consumption.
[07-07-2020] Michiel Bliemer (University of Sydney) Risk and uncertainty in choice experiments using simulated experiences.
Michiel Bliemer (University of Sydney)
Risk and uncertainty in choice experiments using simulated experiences.
Tuesday 7th July, 1-2pm (UK time)
Abstract: Including risky and uncertain attributes into a choice experiment is challenging because respondents may find them difficult to understand and imagine. In this seminar, the focus is on route choice experiments with three levels of travel time unreliability, namely risky, uncertain, and endogenous travel times. In each experiment, respondents experience travel times by driving in quasi-naturalistic driving simulators in the Travel Choice Simulation Laboratory (TRACSLab).
About: Michiel Bliemer is Professor of Transport Planning and Modelling at the Institute of Transport and Logistics Studies at the University of Sydney Business School, and an external affiliate of the Choice Modelling Centre in Leeds. Michiel has made significant contributions to the literature in the areas of discrete choice modelling and stated choice experimental design as well as network modelling and traffic assignment. Michiel is one of the co-developers of the Ngene experimental design software and active in answering any stated choice experiment questions on the ChoiceMetrics forum.
[30-06-2020] Jette Bredahl Jacobsen (University of Copenhagen) Will farmers shoot the wolf?
Jette Bredahl Jacobsen (University of Copenhagen)
Will farmers shoot the wolf?
Tuesday 30th June, 3-4pm (UK time)
Abstract: The wolf recently immigrated to Denmark after more than 200 years of absence. While protected by law, the question was how farmers would react. We conducted a CE and let them express preferences for illegal actions by “hiding” it in bundles (alternatives) of also legal activities. Now, a couple of years later, we can relate it to what actions they actually did.
About: Jette Bredahl Jacobsen is professor at University of Copenhagen in environmental and resource economics, working with a broad range of topics and methods within this field. Her interest in choice modelling is to use it as a tool to learn about preferences the goods being investigated. She has a particular interest in valuation of non-use values (e.g. biodiversity), but also works with environmental use values (e.g. outdoor recreation)
[23-06-2020] Cinzia Cirillo (University of Maryland). A decade of research on vehicle ownership and use: methods, results and impacts on U.S. policy making.
Cinzia Cirillo (University of Maryland)
A decade of research on vehicle ownership and use: methods, results and impacts on U.S. policy making.
Tuesday 23rd June, 3-4pm (UK time)
Abstract: The seminar will give an overview of methodological advances for vehicle ownership and use modeling, and presents empirical results obtained on US data. A dynamic model formulation for new vehicle technology adoption will be presented and its integration with continuous variables for the prediction of Vehicle Miles Travelled will be briefly introduced. Estimates of rebound effect and elasticity to fuel cost will be discussed in the light of the 2018 Notice of Proposed Rulemaking (NPRM) issued by EPA and NHTSA.
About: Cinzia Cirillo is a Full Professor at the University of Maryland (USA), Department of Civil and Environmental Engineering. She has been an active member of the travel behavior community for about two decades. Her work has focused on the fundamentals of the disciplines: discrete choice analysis, survey methodologies including national travel surveys and stated preference, and activity based modeling.
[02-06-2020] Shenhao Wang, MIT. Deep Neural Networks for Choice Analysis
Abstract: There exists a tension between the classical travel behavioral models and the new machine learning (ML) classifiers. While many studies have shown that ML classifiers can outperform choice models for prediction, this presentation discusses how to generate mutual benefits between deep neural networks (DNNs) and classical choice modeling in the context of travel behavioral analysis. Specifically, we ask bidirectional questions: how to interpret DNN-based choice models for behavioral analysis and how to use behavioral theory to design new DNN architectures to improve predictive power and model interpretation. In the first paper, I demonstrate that DNNs can provide economic information as complete as classical discrete choice models (DCMs). The economic information provided by DNNs includes choice predictions, choice probabilities, market shares, substitution patterns of alternatives, social welfare, probability derivatives, elasticities, marginal rates of substitution (MRS), and heterogeneous values of time (VOT). Unlike DCMs, DNNs can automatically learn utility function and reveal behavioral patterns that are not pre-specified by domain experts, particularly when the sample size is large. However, the economic information obtained from DNNs can be unreliable when the sample size is small, because of the three challenges associated with the automatic learning capacity: high sensitivity to hyper-parameters, model non-identification, and local irregularity. In the second paper, I will demonstrate how to use behavioral knowledge to design a particular DNN architecture with alternative-specific utility functions (ASU-DNN). Theoretically, ASU-DNN can substantially reduce the estimation error of fully connected DNN because of its lighter architecture and sparser connectivity. Empirically, ASU-DNN has 2-3% higher prediction accuracy than a fully connected DNN over the whole hyper-parameter space. The alternative-specific connectivity is associated with the independence of irrelevant alternative (IIA) constraint, which as a domain-knowledge-based regularization method is more effective than the most popular generic-purpose explicit and implicit regularization methods and architectural hyper-parameters. Overall, this presentation demonstrates the dynamic interaction between behavioral modeling and DNNs, and the synergy of the two parts can generate mutual benefits in terms of model prediction, interpretation, and robustness.