A Logit Mixture Model Estimating the Heterogeneous Mode Choice Preferences of Shippers Based on Aggregate Data

Abstract

Understanding the modal split in freight transportation is a key factor for the successful implementation of innovations. Mode choice models should then be as representative of reality as possible. The use of disaggregate shipment data can help to achieve it. However, shipment data are often unavailable due to confidentiality issues. As a result, numerous models using only aggregate data have been developed, but their capacity to capture heterogeneity in preferences remains limited. In this paper, we propose a Weighted Logit Mixture model to estimate heterogeneous mode choice preferences of shippers directly from aggregate data. The proposed Weighted Logit Mixture is applied to a case study along the European Rhine-Alpine corridor and allows to estimate the probability distribution of the cost sensitivity among the population. The estimation results show that there exists a substantial variation of the cost sensitivity regarding intermodal transport. The proposed methodology is also compared to a state-of-the-art Weighted Logit model to assess its potential. This reveals that the proposed Weighted Logit Mixture exhibits at least a similar predictive power to the benchmark while achieving a better description of the population's preferences that enables policy-makers to take better informed decisions and appropriate actions.

Loss-Aware Histogram Binning and Principal Component Analysis for Customer Fleet Analytics

“…For histogram binning, we usually regard such histograms as empirical distributions and compare them to original distribution via K-S statistics, expressed in (2). For PCA, the variance unexplained quantifies the fraction of variance lost by projecting the high-dimensional dataset into a low-dimensional latent space described by those principal components (PCs), formulated in (8). Spanning binning and PCA as a sequence where binning does not take place on the analytics site but in the vehicle control units, data scientists usually focus on PCA guided by variance explained or unexplained.…”

“…A common approach to manage both, preparing the data for analytics and privacy preservation, is to aggregate the operational data from customers and solely keep the aggregated data as historical sensor data, e.g., by binning the data [7], [8]. Binning temporal data is typically an initial step in preprocessing sensor data, often already conducted directly on vehicle control units, i.e., on the customer side.…”

Integrating a Mode Choice Model into Agent-Based Simulation for Freight Transport Planning and Decarbonization Analysis

“…In the freight system, the choice of transport mode can have a significant impact on the environment, and the environmental consequences of our transportation choices should be considered. Modal shift to more sustainable alternatives, such as rail and waterways with lower environmental impacts (emissions are several times less per tonne-km), is one of the most cost-effective ways of reducing transport emissions (Nicolet et al 2022;Pearce and Zdemiroglu 2002). Modal shift could be driven by multiple factors.…”