Repeatability & reproducibility: Implications of using GPS data for freight activity chains

As transport modellers we are interested in capturing the behaviour of freight vehicles that includes the locations at which vehicles perform their activities, the duration of activities, how often these locations are visited, and the sequence in which they are visited. With disaggregated freight behaviour data being scarce, transport modellers have identified vehicle tracking and fleet management companies as ideal third party sources for GPS travel data. GPS data does not provide us with behavioural information, but allows us to infer and extract behavioural knowledge using a variety of processing techniques. Many researchers remain sceptical as specific human intervention, referred to as ‘expert knowledge’, is often required during the processing phase: each GPS data set has unique characteristics and requires unique processing techniques and validation to extract the necessary behavioural information. Although much of the GPS data processing is automated through algorithms, human scrutiny is required to decide what algorithmic parameters as considered ‘best’, or at least ‘good’. In this paper we investigate the repeatability and reproducibility (R&R) of a method that entails variable human intervention in processing GPS data. More specifically, the judgement made by an observer with domain expertise on what clustering parameters applied to GPS data best identify the facilities where commercial vehicles perform their activities. By studying repeatability we want to answer the question ‘if the same expert analyses the GPS data more than once, how similar are the outcomes?’, and with reproducibility we want to answer the question ‘if different experts analyse the same GPS data, how similar are their outcomes?’ We follow two approaches to quantify the R&R and conclude in both cases that the measurement system is accurate. The use of GPS data and the associated expert judgements can hence be applied with confidence in freight transport models.     

Repeatability of ice-tank tests with broken ice

This study investigates the repeatability of ice-tank tests with broken ice. Ice-tank test campaigns normally do not perform multiple repetitions of tests with the same initial conditions. Therefore, the repeatability of ice-tank tests with broken ice is not well understood. Data from two test campaigns are analysed. The first test campaign studied the interaction between a 4-legged structure with a vertical waterline and several broken and intact level ice conditions. In the second test campaign, a ship hull geometry was tested. We analyse selected test cases from each test campaign. The ice-tank tests are reproduced using a 3-D discrete element method (DEM) model. Each analysed test case is simulated 20 times. The only difference between each simulation is the initial position of the ice floes. The numerical simulation results show that changes in the initial floe positions can cause large changes in the statistical properties of the ice load. Often, a single random interaction event can be identified that is responsible for the change in the results. Such interaction events can cause additional floe accumulation ahead of the structure, thereby influencing the load statistics for a large portion of the interaction length. The observed events occur both in the numerical simulations and in the physical ice-tank tests. This result indicates that ice-tank tests with broken ice have a poor repeatability; a change in an uncontrolled condition, such as the exact initial floe positions, can lead to a large variation in the experimental results.