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Stephane Hess and David Palma have obtained funding from the European Research Council under the Proof of Concept scheme for...

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Stephane Hess and David Palma are excited to announce the release of Apollo, their free software for advanced choice modelling....

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Details for CMC’s two annual courses are now available here.

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Stephane Hess, director of the Choice Modelling Centre, has been appointed as Honorary Professor of modelling behaviour in Africa at...

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The Choice Modelling Centre is hosting a ‘Choices in dynamic environments’ workshop, with guest speakers Joerg Rieskamp (University of Basel)...

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Discrete choice and machine learning: Thomson and Thompson?  Presented by Professor Michel Bierlaire, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland....

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Dr Sander van Cranenburgh and Dr Marco Kouwenhoven, Delft University of Technology, The Netherlands. An artificial neural network based method...

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Caspar Chorus, Professor of Choice Behavior Modeling, TU Delft. Obfuscation maximization for discrete choice analysis. Wednesday 6 March 2018, 10:00...

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Modelling the role of consideration of alternatives in mode choice: An application on the Rome-Milan corridor.

In this paper, we investigate the role consideration of the alternatives plays in mode choice models. On the Rome-Milan corridor, in Italy, where seven alternative modes of transport are available, we administered a stated choice (SC) experiment. Responses to supplementary questions on consideration of the different modes of transport and the presence of thresholds for the travel time attribute indicate travellers are less likely to consider the slower modes. Two model specifications, in which consideration for the slower alternatives is measured using both sets of supplementary questions, are proposed and contrasted against a model which assumes all alternatives are considered. Our results suggests that some of the unobserved preference heterogeneity could potentially be due to consideration effects. Accounting for consideration of alternatives also has direct impacts on choice probabilities, parameter estimates and willingness-to-pay measures.

Capurso, M., Hess, S. & Dekker, T. (2019), Modelling the role of consideration of alternatives in mode choice: An application on the Rome-Milan corridor. Transportation Research Part A, 129, November 2019, Pages 170-184.

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How do people choose their commuting mode? An evolutionary approach to travel choices.

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.

 

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 Car Trip Generation Models in the Developing World: Data Issues and Spatial Transferability.

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.

 

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Modelling residential mobility decision and its impact on car ownership and travel mode.

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.

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