CMC aims to be a globally leading centre for academic research in choice modelling
CMC aims to bring together expertise from all key disciplines and create an environment of collaboration by breaking down traditional barriers
CMC is based in one of the largest and most research intensive universities in the UK, with excellent national and international links
Tuesday 3 September 2019
Stephane Hess and David Palma have obtained funding from the European Research Council under the Proof of Concept scheme for...
Sunday 7 April 2019
Stephane Hess and David Palma are excited to announce the release of Apollo, their free software for advanced choice modelling....
Friday 9 November 2018
Stephane Hess, director of the Choice Modelling Centre, has been appointed as Honorary Professor of modelling behaviour in Africa at...
11 June 2019
Discrete choice and machine learning: Thomson and Thompson? Presented by Professor Michel Bierlaire, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland....
19 March 2019
Dr Sander van Cranenburgh and Dr Marco Kouwenhoven, Delft University of Technology, The Netherlands. An artificial neural network based method...
26 February 2019
Seminar and Workshop with the Extending the QALY project team Tuesday 26th February The Academic Unit of Health Economics is...
Tuesday 1 October 2019
A considerable amount of studies in the transport literature is aimed at understanding the behavioural processes underlying travel choices, like mode and destination choices. In the present work, we propose the use of evolutionary game theory as a framework to study commuter mode choice. Evolutionary game models work under the assumptions that agents are boundedly rational and imitate others’ behaviour. We examine the possible dynamics that can emerge in a homogeneous urban population where commuters can choose between two modes, private car or public transport. We obtain a different number of equilibria depending on the values of the parameters of the model. We carry out comparative-static exercises and examine possible policy measures that can be implemented in order to modify the agents’ payoff, and consequently the equilibria of the system, leading society towards more sustainable transportation patterns.
Calastri, C., Borghesi, S., & Fagiolo, G. (2019). How do people choose their commuting mode? An evolutionary approach to travel choices. Economia Politica, 36(3), 887-912.
Tuesday 1 October 2019
In many countries of the developing world, it is difficult to conduct large-scale household travel surveys to collect data for travel behaviour model estimation and application. This paper focuses on two candidate solutions to the problem: (1) developing models that can be applied for prediction using secondary data collected for other purposes and include socio-demographic information but do not include transport specific information such as the car and/or transit pass ownership (e.g. census, public health records, etc.), (2) ‘borrowing’ a model developed using data from a similar city within the same region. In the first approach, we investigate the feasibility of developing car trip generation models which imputes the car ownership variable with estimated car ownership propensities. The proposed framework is applied in two East African cities, Nairobi and Dar-es-Salaam. The estimation results indicate that for both cities the proposed approach outperforms the models that exclude the car ownership variable. In the second approach, we investigate the spatial transferability of the models developed in the first approach between the two cities to evaluate if it is justified to apply models from one developing country to another in the absence of local models. Results indicate that though some of the estimated parameters are not significantly different from each other between the two cities, statistical tests do not support direct transferability of all the models from Nairobi to Dar-es-Salaam or vice versa. However, interestingly, the simpler model (which excludes car-ownership) outperforms the model with imputed car ownership propensity in terms of transferability. These findings provide useful insights into the development of trip generation models under data constraints which can practically be very useful for developing countries.
Bwambale, A., Choudhury, C. F., & Sanko, N. (2019). Car Trip Generation Models in the Developing World: Data Issues and Spatial Transferability. Transportation in Developing Economies, 5(2), 10.
Tuesday 1 October 2019
Household residential relocation can happen at different scales – local, regional national and international. The impacts of the different scales of residential relocation is likely to have varying impacts on mid-term (e.g. car or transit pass ownership) and day-to-day mobility decisions (e.g. mode choice for a specific trip for example). These mobility changes can be of different levels as well. For example, there are differences between the decision to transition from owning no car to one car and from one car to two cars. Identifying which factors affect the different magnitudes of mobility changes and quantifying the impact of various scales of residential relocation on these changes are crucial to better understanding of travel behaviour. The present study uses discrete choice models on revealed preference data to address these research questions. To complement the travel behaviour models, a residential relocation model has also been developed to predict the probability of a household to stay in the current location vs. to move locally, regionally or nationally at a given point of time. Given that the residential relocations are rare events, the British household panel survey (BHPS) spanning 18 years has been used to model the choices made by the same households in terms of residential relocation, car ownership and commute mode of the household head. Our results indicate that sociodemographic characteristics, travel behaviour and life events of the households have a significant effect on relocation, car ownership and commute mode choice. As expected, the parameters of the car ownership and commute mode choice models vary significantly with the type of relocation. Further, the socio-demographic factors and life-events also have a varying impact on the scale of relocation. The residential relocation, car ownership and commute mode choice models developed in this research can be used to better predict the medium and long term changes in travel behaviour over course of time.
Haque, Md B., Choudhury, C.F., Hess, S. & Crastes dit Sourd, R. (2019), Modelling residential mobility decision and its impact on car ownership and travel mode. Travel Behaviour and Society, Volume 17, October 2019, Pages 104-119.
Sunday 1 September 2019
Small and Rosen’s (Econometrica 49(1):105–130, 1981) method for measuring consumer surplus using discrete choice models has been widely adopted in public policy analysis. For the case of a price change, the present paper elucidates five theoretical assumptions inherent within Small and Rosen’s measure, and employs indifference maps to demonstrate that this measure is only applicable to the context of a single discrete choice free of non-linear income effects. The paper argues that, where non-linear income effects are present, the aforementioned theoretical assumptions should be relaxed, and the consumption context revised from discrete choice to discrete–continuous demand. Furthermore, the paper proposes a simple analytical method for approximating the expected Hicksian compensating variation in the presence of non-linear income effects, and compares the empirical performance of this method against existing methods using data from Morey et al. (Am J Agric Econ 75(3):578–592, 1993). As well as offering a simple approximation, the proposed method yields insights on the potential range of the compensating variation depending on the extent of switching between choice alternatives, and on the attribution of the compensating variation to the relevant choice alternatives.
Batley, R., & Dekker, T. (2019). The intuition behind income effects of price changes in discrete choice models, and a simple method for measuring the compensating variation. Environmental and Resource Economics, 1-30.