Abstract
Discrete choice model acts as one of the most important tools for studies involving mode split in the context of transport demand forecast. As different types of discrete choice models display their merits and restrictions diversely, how to properly select the specific type among discrete choice models for realistic application still remains to be a tough problem. In this article, five typical discrete choice models for transport mode split are, respectively, discussed, which includes multinomial logit model, nested logit model (NL), heteroscedastic extreme value model, multinominal probit model and mixed multinomial logit model (MMNL). The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are also applied to a realistic intercity case of mode split forecast, which results indicating that NL model does well in accommodating similarity and heterogeneity across alternatives, while MMNL model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and similarity problems. This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics.
Highlights
A good understanding on the travellers’ mode choice behaviours serves as one of the prerequisites for passenger transport policy-making
The theoretical basis and application attributes of these five models are especially analysed with great attention, and they are applied to a realistic intercity case of mode split forecast, which results indicating that nested logit model (NL) model does well in accommodating similarity and heterogeneity across alternatives, while multinominal logit (MMNL) model serves as the most effective method for mode choice prediction since it shows the highest reliability with the least significant prediction errors and even outperforms the other four models in solving the heterogeneity and
This study indicates that conclusions derived from a single discrete choice model are not reliable, and it is better to choose the proper model based on its characteristics
Summary
A good understanding on the travellers’ mode choice behaviours serves as one of the prerequisites for passenger transport policy-making. To relax the MNL model’s IIA property, and in the keep its calculation convenience, the researchers gradually relax the restrictions on the assumption of utility random terms structures and successively explore and develop several MNL-based models, which are more capable of recurring decision-makers’ choice behaviours, such as nested logit model (NL) [2], generalised extreme value model [3, 4], heteroscedastic extreme value model (HEV) [5], mixed multinomial logit model (MMNL) [6], etc. Hensher et al [15] analysed choices of automobile models by a sample of consumers that offered a hypothetical menu of features In each of these cases, there is a single decision among two or more alternatives. We focused on transport mode choice behaviour modelling and made a comparison between five typical discrete choice models and discussed the rules for choosing the optimal discrete choice model
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