Exploring rail futures using scenarios: experience and potential

In 1995 the author of this paper undertook a scenario exercise for British Rail to identify priorities for rail science and technology developments under the new privatised regime. Four marketbased 2010 scenarios were developed for UK rail transport: 1) cost driven; 2) quality driven, 3)technology driven and 4) environmentally driven. These helped to identify areas of strategic R&D that were needed to improve rail’s competitiveness. It is now over a decade since this scenario exercise took place. This paper, updating an earlier review (Potter and Roy, 2000), revisits the 1995 scenarios and compares them to what actual market strategies emerged within the privatised railway industry. It explores whether the four scenarios did succeed in capturing the range of market responses that emerged from rail privatisation and what lessons this contains for the use of scenarios transport research

Gated Ensemble of Spatio-temporal Mixture of Experts for Multi-task Learning in Ride-hailing System

Designing spatio-temporal forecasting models separately in a task-wise and city-wise manner poses a burden for the expanding transportation network companies. Therefore, a multi-task learning architecture is proposed in this study by developing gated ensemble of spatio-temporal mixture of experts network (GESME-Net) with convolutional recurrent neural network (CRNN), convolutional neural network (CNN), and recurrent neural network (RNN) for simultaneously forecasting spatio-temporal tasks in a city as well as across different cities. Furthermore, a task adaptation layer is integrated with the architecture for learning joint representation in multi-task learning and revealing the contribution of the input features utilized in prediction. The proposed architecture is tested with data from Didi Chuxing for: (i) simultaneously forecasting demand and supply-demand gap in Beijing, and (ii) simultaneously forecasting demand across Chengdu and Xian. In both scenarios, models from our proposed architecture outperformed the single-task and multi-task deep learning benchmarks and ensemble-based machine learning algorithms.Comment: arXiv admin note: text overlap with arXiv:2012.0886

Infinite Mixtures of Multivariate Gaussian Processes

This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. As an extension of the single multivariate Gaussian process, the mixture model has the advantages of modeling multimodal data and alleviating the computationally cubic complexity of the multivariate Gaussian process. A Dirichlet process prior is adopted to allow the (possibly infinite) number of mixture components to be automatically inferred from training data, and Markov chain Monte Carlo sampling techniques are used for parameter and latent variable inference. Preliminary experimental results on multivariate regression show the feasibility of the proposed model.Comment: Proceedings of the International Conference on Machine Learning and Cybernetics, 2013, pages 1011-101

The Community Foehn Classification Experiment

Strong winds crossing elevated terrain and descending to its lee occur over mountainous areas worldwide. Winds fulfilling these two criteria are called “foehn” in this paper although different names exist depending on region, sign of temperature change at onset, and depth of overflowing layer. They affect local weather and climate and impact society. Classification is difficult because other wind systems might be superimposed on them or share some characteristics. Additionally, no unanimously agreed-upon name, definition nor indications for such winds exist. The most trusted classifications have been performed by human experts. A classification experiment for different foehn locations in the Alps and different classifier groups addressed hitherto unanswered questions about the uncertainty of these classifications, their reproducibility and dependence on the level of expertise. One group consisted of mountain meteorology experts, the other two of Masters degree students who had taken mountain meteorology courses, and a further two of objective algorithms. Sixty periods of 48 hours were classified for foehn/no foehn at five Alpine foehn locations. The intra-human-classifier detection varies by about 10 percentage points (interquartile range). Experts and students are nearly indistinguishable. The algorithms are in the range of human classifications. One difficult case appeared twice in order to examine reproducibility of classified foehn duration, which turned out to be 50% or less. The classification dataset can now serve as a testbed for automatic classification algorithms, which - if successful - eliminate the drawbacks of manual classifications: lack of scalability and reproducibility