The study focuses on estimating and predicting time-varying origin-destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flo...
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Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a hi...
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Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, ...
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ISBN:
(纸本)9781538670255
Nowadays a large amount of data is collected from sensor devices across the cyber-physical networks. Accurate and reliable primary delay predictions are essential for rail operations management and planning. However, very few existing `big data' methods meet the specific needs in railways. We propose a comprehensive and general data-driven Primary Delay Prediction System (PDPS) framework, which combines General Transit Feed Specification (GTFS), Critical Point Search (CPS), and deep learning models to leverage the data fusion. Based on this framework, we have also developed an open source data collection and processing tool that reduces the barrier to the use of the different open data sources. Finally, we demonstrate an advanced deep learning model, the novel ConvLSTM Encoder-Decoder model with CPS for better primary delay predictions.
Traffic control optimization is a challenging task for various traffic centres in the world and majority of approaches focus only on applying adaptive methods under normal (recurrent) traffic conditions. But optimizin...
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Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly deman...
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Computer Supported Cooperative Work (CSCW) in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.
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