Guest Lectures and Special Events

This presentation was delivered as part of the Choice Modelling Centre and Institute for Transport Studies (ITS) research seminar series: https://environment.leeds.ac.uk/events/6/transport. Introduction by Dr Manuel Ojeda Cabral, ITS, Leeds. Abstract: In this presentation, we propose an Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. This method is highly flexible and complements recently proposed nonparametric methods to calculate the VTT from choice data. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making strong assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and account for panel effects. Therefore, in contrast to other nonparametric methods, the ANN-based method is suitable to derive estimates for the mean VTTs for appraisal. In this presentation, we first show the results from a series of Monte Carlo experiments. Using these, we assess how well the ANN-based method works in terms of being able to recover the true underlying VTT distribution. After having demonstrated that the method works well on Monte Carlo data, we apply our method to data from the 2009 Norwegian VTT study. Finally, we extensively cross-validate the method by comparing it with a series of state-of-the-art discrete choice models and nonparametric methods. Based on the encouraging results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies. Finally, we discuss explore further directions to capitalise these machine learning methods for VTT research, and more generally for choice behaviour analysis. Sander van Cranenburgh is Assistant Professor in the Transport and Logistics Group of Delft University of Technology. His research focusses on methods for choice behaviour analysis. During his Postdoc he worked on Random Regret Minimization (RRM) based choice models, and made several methodological contributions to this field, including the development of new regret models and efficient design theory for RRM models (which has recently been implemented in Ngene).His current research mainly focusses developing data-driven modelling approaches for choice behaviour analysis. In this research he specifically seeks the edge between theory-driven approaches, such as discrete choice models, and data-driven approaches, such as Artificial Neural Networks. Marco Kouwenhoven is Research Leader at Significance, an independent research institute specialised in quantitative research on mobility and transport, and a part time Researcher in the Transport and Logistics Group of Delft University of Technology. One of his research fields is stated preference research, and especially to determine the value of travel time, value of travel reliability and value of comfort. He has worked on many stated preference projects, among which the latest value of time studies in the Netherlands and Norway.

Workshop delivered by Georgina Vince.

Jasmine Dotiwala. Jasmine is a broadcaster, producer, director and columnist who has worked within the television and music industry for over two decades. She reports on arts and culture stories for Channel 4 News, BBC Radio London and SKY News. She is currently Head of Youth Media at Media Trust where she trains young people from all across the UK and London in multi-media skills for TV, radio and digital platforms. She is also a host producer annually on the annual MTV Europe Music Awards, and an international judge for music awards in Uganda and Namibia (NAMAs).

This presentation was delivered as part of the Institute for Transport Studies (ITS) research seminar series: https://environment.leeds.ac.uk/events/6/transport. Introduction by Professor Ronghui Lui. Abstract: A major problem of public transport, and railways in particular, is to improve quality of operations by updating an offline timetable to the ever changing delays situation, in order to improve performance of the transport system. In railway systems, this relates to reduce train delays by reordering retiming or rerouting trains, and/or change connection plans and route advised to passengers, to improve their traveltime. Key point of research is the interaction between the problem (of the infrastructure manager) to reschedule trains, and the problem (of the travellers) to find the optimal route in the network. In fact, changing passenger flows, respectively delaying trains and/or dropping passenger connections, varies the setting under which the two decision makers respectively interact. The interaction of the two decisions makers is mediated by the information one decision maker has about the other, and the service which is offered/used. We report different methods to address this dilemma, by agent-based simulation. Bio: Francesco Corman holds the chair of Transport Systems at the Institute of Transport Planning and Systems, Swiss Federal Institute of Technology, ETH Zurich, Switzerland. He has a PhD in Transport Sciences from TUDelft, the Netherlands, on operations research techniques for realtime railway traffic control. He has academic experience at KU Leuven, Belgium and TUDelft as research associate in transportation and logistics. Main research interests are in the application of quantitative methods and operations research to transport sciences, especially on the operational perspective, public transport, railways and logistics.

This screencast provides a practical introduction to the R statistical programming language and to the RStudio integrated development environment, which is a powerful interface for working with R. It is designed for those new to R, in particular those who would like to attend some of the workshops Language @ Leeds provides.