Infrastructure Engineering - Research Publications
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ItemTransferable supervised learning model for public transport service load estimation(Springer Science and Business Media LLC, 2023-07-28)Detailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.
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ItemNo Preview AvailableA Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction(Institute of Electrical and Electronics Engineers, 2022-06-02)Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.
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ItemReal-time road safety optimization through network-level data management(SPRINGER, 2023-07)Abstract With the increasing connectedness of vehicles, real-time spatio-temporal data can be collected from citywide road networks. Innovative data management solutions can process the collected data for the purpose of reducing travel time. However, a majority of the existing solutions have missed the opportunity to better manage the collected data for improving road safety at the network level. We propose an efficient data management framework that uses network-level data to improve road safety for citywide applications. Our framework uses a graph-based data structure to maintain real-time network-level traffic data. Based on the graph, the framework uses a novel technique to generate driving instructions for individual vehicles. By following the instructions, inter-vehicular spacing can be increased, leading to an improvement of road safety. Experimental results show that our framework improves road safety, measured based on the time to collision between vehicles, from the state-of-the-art traffic data management solutions by a large margin while achieving lower travel times compared with the solutions. The framework is also readily deployable for large-scale real-time applications due to its low computation costs.