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This paper focuses on a special class of real-time railway traffic management problem: efficiently reallocating trains to platforms at …

This paper presents a software package developed in Python that allows the application of the technique known as Kernel Logistic …

The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes beyond simple predictive performance, and is instead a balance of many factors, including behavioural interpretability and explainability, computational complexity, and data efficiency. There is a growing body of research which attempts to compare the predictive performance of different ML classifiers with classical Random Utility Models (RUMs). However, existing studies typically analyse only the disaggregate predictive performance, ignoring other aspects affecting model choice. Furthermore, many existing studies are affected by technical limitations, such as the use of inappropriate validation schemes, incorrect sampling for hierarchical data, a lack of external validation, and the exclusive use of discrete metrics. In this paper, we address these limitations by conducting a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice (out-of-sample predictive performance, accuracy of predicted market shares, extraction of behavioural indicators, feature importance analysis, and computational efficiency). The modelling problems combine several real world datasets with synthetic datasets, where the data generation function is known. The results indicate that the models with the highest disaggregate predictive performance (namely Extreme Gradient Boosting (XGBoost) and Random Forests (RF)) provide poorer estimates of behavioural indicators and aggregate mode shares, and are more expensive to estimate, than other models, including Deep Neural Networks (DNNs) and Multinomial Logit (MNL). It is further observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness To Pay (WTP).

Searching for achieving an ambitious reduction in greenhouse gas emissions, the EU has set as a goal a modal shift in freight transport …

The pervasive use of mobile devices has brought a valuable new source of data. The work presented here has a twofold objective: …

Unexpected events may perturb operations and generate conflicts that must be addressed promptly to limit delay propagation and other …

Rail operations are often disrupted by accidents that cause traffic to diverge from the scheduled operations, rendering it difficult to …

This paper describes the components of an intermodal dispatcher of ride pooling requests to integrate these urban mobility systems with …

Dynamic traffic models require dynamic inputs, one of the main ones being the Dynamic Origin–Destinations (OD) matrices describing the …