Low-fi simulation of bridge team communication

The paper compares the pedagogical advantages of two simulation types used for training verbal maritime communication. It tests the hypothesis that “low-fi” simulation using online web conference software, specifically ‘chat’ or messaging, is a valid complementary tool for the instruction of Maritime English and full-mission simulation. Based on a series of exercises involving multi-national and multi-lingual students, various linguistic patterns are determined and compared to audio recordings of full-mission simulation. Probability of superiority effect sizes is computed to quantify the observed effects. The findings corroborate the proximity of language patterns found in low-fi and full-mission simulation. Pedagogical implications are discussed, and future research possibilities are outlined.     

High-speed trains subject to abrupt braking

The dynamic response of high-speed train subject to braking is investigated using the moving element method. Possible sliding of wheels over the rails is accounted for. The train is modelled as a 15-DOF system comprising of a car body, two bogies and four wheels interconnected by spring-damping units. The rail is modelled as a Euler–Bernoulli beam resting on a two-parameter elastic damped foundation. The interaction between the moving train and track-foundation is accounted for through the normal and tangential wheel–rail contact forces. The effects of braking torque, wheel–rail contact condition, initial train speed and severity of railhead roughness on the dynamic response of the high-speed train are investigated. For a given initial train speed and track irregularity, the study revealed that there is an optimal braking torque that would result in the smallest braking distance with no occurrence of wheel sliding, representing a good compromise between train instability and safety.     

Estimating the agglomeration benefits of transport investments: some tests for stability

The case for including agglomeration benefits within transport appraisal rests on an assumed causality between access to economic mass and productivity. Such causality is justified by the theory of agglomeration, but is difficult to establish empirically because estimates may be subject to sources of bias from endogeneity and confounding. The paper shows that conventional panel methods used to address these problems are unreliable due to the highly persistent nature of accessibility measures. Adopting an alternative approach, by applying semiparametric techniques to restricted sub-samples of the data, we find considerable nonlinearity in the relationship between accessibility and productivity with no positive effect to be discerned over broad ranges of the data. A key conclusion is that we are unable to distinguish the role of accessibility from other potential explanations for productivity increases. For transport appraisal, this implies that the use of conventional point elasticity estimates could be highly misleading.     

A genetic algorithm-based method for improving quality of travel time prediction intervals

The transportation literature is rich in the application of neural networks for travel time prediction. The uncertainty prevailing in operation of transportation systems, however, highly degrades prediction performance of neural networks. Prediction intervals for neural network outcomes can properly represent the uncertainty associated with the predictions. This paper studies an application of the delta technique for the construction of prediction intervals for bus and freeway travel times. The quality of these intervals strongly depends on the neural network structure and a training hyperparameter. A genetic algorithm–based method is developed that automates the neural network model selection and adjustment of the hyperparameter. Model selection and parameter adjustment is carried out through minimization of a prediction interval-based cost function, which depends on the width and coverage probability of constructed prediction intervals. Experiments conducted using the bus and freeway travel time datasets demonstrate the suitability of the proposed method for improving the quality of constructed prediction intervals in terms of their length and coverage probability.