Abstract
Insights about service improvement in a transit network can be gained by studying transit service reliability. In this paper, a general procedure for constructing a transit service reliability diagnostic (Tsrd) diagram based on a Bayesian network is proposed to automatically build a behavioural model from Automatic Vehicle Location (AVL) and Automatic Passenger Counters (APC) data. Our purpose is to discover the variability of transit service attributes and their effects on traveller behaviour. A Tsrd diagram describes and helps to analyse factors affecting public transport by combining domain knowledge with statistical data. © 2014 Springer International Publishing.
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CITATION STYLE
Tran, V. T., Eklund, P., & Cook, C. (2014). Learning diagnostic diagrams in transport-based data-collection systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8502 LNAI, pp. 560–566). Springer Verlag. https://doi.org/10.1007/978-3-319-08326-1_61
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