All established models in transportation engineering that estimate the numbers of trips between origins and destinations from vehicle counts use some form of a priori knowledge of the traffic. This paper, in contrast, presents a new origin–destination flow estimation model that uses only vehicle counts observed by traffic count sensors; it requires neither historical origin–destination trip data for the estimation nor any assumed distribution of flow. This approach utilises a method of statistical origin–destination flow estimation in computer networks, and transfers the principles to the domain of road traffic by applying transport-geographic constraints in order to keep traffic embedded in physical space. Being purely stochastic, our model overcomes the conceptual weaknesses of the existing models, and additionally estimates travel times of individual vehicles. The model has been implemented in a real-world road network in the city of Melbourne, Australia. The model was validated with simulated data and real-world observations from two different data sources. The validation results show that all the origin–destination flows were estimated with a good accuracy score using link count data only. Additionally, the estimated travel times by the model were close approximations to the observed travel times in the real world.

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A Bell 206B main rotor gearbox was run at high load under test conditions in the Helicopter Transmission Test Facility operated by the Defence Science and Technology Organisation (DSTO) of Australia. The test succeeded in initiating and propagating pitting damage in one of the planet gear support bearings. Vibration acceleration signals were recorded periodically for the duration of the test. The time domain vibration signals were converted to angular domain to minimise the effects of speed variations. Auto-Regressive Moving-Average (ARMA) models were fitted to the vibration data and a change detection problem was formulated in terms of the Generalised Likelihood Ratio (GLR) algorithm. Two different forms of the GLR algorithm in window-limited online form were applied. Both methods succeeded in detecting a change in the vibration signals towards the end of the test. A companion paper submitted by the University of New South Wales outlines the corresponding diagnosis and prognosis algorithms applied to the vibration data.

Map-matching of trajectory data has widespread applications in vehicle tracking, traffic flow analysis, route planning, and intelligent transportation systems. Map-matching algorithms snap a set of trajectory points observed by a satellite navigation system to the most likely route segments of a map. However, due to the unavoidable errors in the recorded trajectory points and the incomplete map data, map-matching algorithms may match points to incorrect segments, leading to map-matching errors. Identification of these map-matching errors in the absence of ground truth can only be achieved by visual inspection and reasoning. Thus, the identification of map-matching errors without ground truth is a time-consuming and mundane task. Although research has focused on improving map-matching algorithms, to our knowledge no attempts have been made to automatically classify and identify the residual map-matching errors. In this work, we propose the first method to automatically identify map-matching errors in the absence of ground truth, i.e., only using the recorded trajectory points and the map-matched route. We have evaluated our method on a public dataset and observed an average accuracy of 91% in automatically identifying map-matching errors, thus helping analysts to significantly reduce manual effort for map-matching quality assurance.

This work is motivated by the high rate of private car use by commuters in Australian cities. Considering the various benefits of public transport (PT), it is imperative to analyze the shortcomings of the PT networks in comparison to car travel. For this purpose, this work focuses on analyzing the travel time difference between PT and car journeys. This paper presents a methodology to analyze the relative mobility of a PT network in comparison to car travel. The key component of this methodology is the use of two complementary relative mobility metrics that are developed to determine the portions of PT journeys that are inefficient (time-wise) in comparison to car trips. The first metric is the point-to-point relative mobility used to perform spatial distribution analysis, and the second is the PT line relative mobility that evaluates the performance of the PT routes. A demonstration of the methodology is presented by using a case study from Melbourne (Australia). The results of the case study show some inefficient segments of the PT services in several areas, and within the Port Melbourne area in particular. Therefore, this information can be used to assist decision makers to prioritize PT improvements to encourage mode shifts to PT.