Abstract
AbstractWhile individual data are key for epidemiology, social simulation, economics, and various other fields, data owners are increasingly required to protect the personally identifiable information from data. Simple data de-identification or ‘data masking’ measures are limited, because they both reduce the utility of the dataset and are not sufficient to protect individual confidentiality. This paper provides detail on the creation of a synthetic trip data in transportation, with the Smart Card data as the case study. It discusses and compares two machine learning methods, a Generative Adversarial Network and a Bayesian Network for modelling and generating this dataset. The synthetic data retain important utility of the real dataset, e.g., the origin, destination, and time of travel, while each data point does not represent a real trip in the original dataset. The synthetic dataset can be used in various applications, including microsimulation of public transport systems, analysing travel behaviours, model predictive control of transit flows, or evaluation of transport policies.
Funder
FRDF Grant, University of Auckland
University of Auckland
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Ahmed G, Malick RAS, Akhunzada A, Zahid S, Sagri MR, Gani A (2021) An approach towards IoT-based predictive service for early detection of diseases in poultry chickens. Sustainability 13(23):13396. ISSN 2071-1050. https://doi.org/10.3390/su132313396. https://www.mdpi.com/2071-1050/13/23/13396
2. Axhausen KW, Gärling T (1992) Activity-based approaches to travel analysis: conceptual frameworks, models, and research problems. Transp Rev 12(4):323–341. ISSN 0144-1647. https://doi.org/10.1080/01441649208716826
3. Badu-Marfo G, Farooq B, Patterson Z (2020) A differentially private multi-output deep generative networks approach for activity diary synthesis. arXiv preprint arXiv:2012.14574
4. Bengio Y, Thibodeau-Laufer É, Alain G, Yosinski J (2014) Deep generative stochastic networks trainable by backprop. arXiv preprint arXiv:1306.1091 [cs]
5. Bouman PC, Kroon LG, Schöbel A, Vervest PHM (2017) Passengers, crowding and complexity: models for passenger oriented public transport. PhD thesis, OCLC: 990177422