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The design and pricing of services are two of the most important decisions faced by any intermodal transport operator. The key success factor lies in the ability to meet the needs of the shippers. Therefore, making full use of the available information about the demand helps to come up with good design and pricing decisions. With this in mind, we propose a Choice-Driven approach, incorporating advanced choice models directly into a Service Network Design and Pricing problem. We evaluate this approach considering both deterministic and stochastic choice models. To reduce the computational time for the stochastic instances, we propose a predetermination heuristic. The proposed models are compared to a benchmark, where shippers are solely cost-minimizers. Results show that the operator's profits can be significantly improved, even with deterministic models. The stochastic versions further increase the realized profits: in particular, considering shippers' heterogeneity allows a better estimation of the demand. • Including more information about shippers improves transport operators' decisions. • Embedding mode choice models in pricing can increase profits by at least a factor two. • Even if the exact values are not known, decisions are better than without information. [ABSTRACT FROM AUTHOR]

Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively). However, the underlying model relies on strong assumptions on the inter- and intra-consumer mixing distributions; these distributions are assumed to be normal (or log-normal), and the intra-consumer covariance matrix is assumed to be the same for all individuals. This paper presents a latent class extension to the model and the estimator proposed by Becker et al. (2018) to account for flexible, semi-parametric mixing distributions. This relaxes the normality assumptions and allows different individuals to have different intra-consumer covariance matrices. The proposed model and the HB estimator are validated using real and synthetic data sets, and the models are evaluated using goodness-of-fit statistics and out-of-sample validation. Our results show that when the data comes from two or more distinct classes (with different population means and inter- and intra-consumer covariance matrices), this model results in a better fit and predictions compared to the single class model. • We extend the logit mixture model with inter- and intra-consumer heterogeneity to account for flexible distributions. • We use semi-parametric distributions and allow different individuals to have different intra-consumer covariance matrices. • A Hierarchical Bayes estimator is proposed to estimate the model with the extensions. • Using synthetic data, we recover the true number of classes using out-of-sample validation and statistical testing. • The model and the estimator are applied to the Swiss Route Choice data set. [ABSTRACT FROM AUTHOR]

Highlights • New Hierarchical Bayes estimator for Logit Mixtures with two levels of heterogeneity. • Accounts for heterogeneity across and within respondents. • We present a Monte Carlo Simulation for the recovery of the true parameters, a comparison to MSL and a real-world example on transportation mode choice for GPS traces. • The runtime is substantially shorter than for the corresponding MSL estimator. • Useful for updating preferences in a system whose data is continuously collected. Abstract Estimating discrete choice models on panel data allows for the estimation of preference heterogeneity in the sample. While the Logit Mixture model with random parameters is mostly used to account for variation across individuals, preferences may also vary across different choice situations of the same individual. Up to this point, Logit Mixtures incorporating both inter- and intra-consumer heterogeneity are estimated with the classical Maximum Simulated Likelihood (MSL) procedure. The MSL procedure becomes computationally expensive with an increasing sample size and can be burdensome in the presence of a multi-modal likelihood function. We therefore propose a Hierarchical Bayes estimator for Logit Mixtures with both levels of heterogeneity. It builds on the Allenby-Train procedure, which considers only inter-consumer heterogeneity. To test the proposed procedures, we analyze how well the true patterns of heterogeneity are recovered in a simulation environment. Results from the Monte Carlo simulation suggest that falsely ignoring intra-consumer heterogeneity despite its presence in the data leads to biased estimates and a decreased goodness of fit. The latter is confirmed by a real-world example of explaining mode choices for GPS traces. We further show that the runtime of the proposed estimator is substantially faster than for the corresponding MSL estimator. [ABSTRACT FROM AUTHOR]