Haque

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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.

Modelling Residential Location Choices with Implicit Availability of Alternatives.

Choice set generation is a challenging aspect of disaggregate level residential location choice modelling due to the large number of candidate alternatives in the universal choice set (hundreds to hundreds of thousands). The classical Manski method (Manski, 1977) is infeasible here because of the explosion of the number of possible choice sets with the increase in the number of alternatives. Several alternative approaches have been proposed in recent years to deal with this issue, but these have limitations alongside strengths. For example, the Constrained Multinomial Logit (CMNL) model (Martínez et al., 2009) offers gains in efficiency and improvements in model fit but has weaknesses in terms of replicating the Manski model parameters. The rth-order Constrained Multinomial Logit (rCMNL) model (Paleti, 2015) performs better than the CMNL model in producing results consistent with the Manski model, but the benefits disappear when the number of alternatives in the universal choice set increases. In this study, we propose an improved CMNL model (referred to as Improved Constrained Multinomial Logit Model, ICMNL) with a higher order formulation of the CMNL penalty term that does not depend on the number of alternatives in the choice set. Therefore, it is expected to result in better model fit compared to the CMNL and the rCMNL model in cases with large universal choice sets. The performance of the ICMNL model against the CMNL and the rCMNL model is evaluated in an empirical study of residential location choices of households living in the Greater London Area. Zone level models are estimated for residential ownership and renting decisions where the number of alternatives in the universal choice set is 498 in each case. The performance of the models is examined both on the estimation sample and the holdout sample used for validation. The results of both ownership and renting models indicate that the ICMNL model performs considerably better compared to the CMNL and the rCMNL model for both the estimation and validation samples. The ICMNL model can thus help transport and urban planners in developing better prediction tools.

Haque, M.D., Choudhury, C.F. & Hess, S. (2019), Modelling Residential Location Choices with Implicit Availability of Alternatives. Journal of Transport and Land Use, 12(1), pp. 597–618.

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