Searched for: author%3A%22Luo%2C+D.%22
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document
Degeler, V. (author), Heydenrijk-Ottens, L.J.C. (author), Luo, D. (author), van Oort, N. (author), van Lint, J.W.C. (author)
We perform an analysis of public transport data from The Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location and automated fare collection data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised machine learning techniques,...
journal article 2020
document
Yap, M.D. (author), Luo, D. (author), Cats, O. (author), van Oort, N. (author), Hoogendoorn, S.P. (author)
Minimizing passenger transfer times through public transport (PT) transfer synchronization is important during tactical planning and real-time control. However, there are computational challenges for solving this Timetable Synchronization Problem (TSP) for large, real-world urban PT networks. Hence, in this study we propose a data-driven,...
journal article 2019
document
Degeler, V. (author), Heydenrijk-Ottens, L.J.C. (author), Luo, D. (author), van Oort, N. (author)
We perform analysis of public transport data from March 2015 from The<br/>Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location (AVL) and automated fare collection (AFC) data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised...
conference paper 2018
document
Yap, M.D. (author), Luo, D. (author), Cats, O. (author)
For large urban networks and hubs, optimizing transfer synchronization becomes computationally challenging. The objective of this paper is therefore to develop a generic, data-driven methodology to determine the key line/direction-combinations to synchronize based on passenger flows. We developed an approach to detect communities of directional...
conference paper 2018
Searched for: author%3A%22Luo%2C+D.%22
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