PhD Defence: Yinyi Ma
In her dissertation ‘The Use of Advanced Transportation Monitoring Data for Official Statistics’, ERIM’s Yinyi Ma uses information methods and hierarchal Bayesian networks to demonstrate the approaches to estimate transportation demand.
Yinyi Ma defended her dissertation in the Senate Hall at Erasmus University Rotterdam on Friday, 3 June 2016 at 9:30. Her supervisor was Prof.dr. L.G. Kroon and her co-supervisor was Dr. J. van Dalen. Other members of the Doctoral Committee were Dr. R. Kuik (RSM), Prof.dr.H. Mahmassani (Northwestern University), and Prof.dr. H.J. van Zuylen (TU delft).
About Yinyi Ma
Yinyi Ma was born in 1984 in Nanjing, China. She holds a bachelor degree of Transportation Engineering in China. From 2007 to 2008, she studied the Transportation Infrastructure and Logistics major at Delft University of Technology, the Netherlands, under the supervision of Prof. dr. Henk van Zuylen. In 2009, she joined the PhD program in Erasmus University, the Netherlands. Her project is funded by Statistics Netherlands (CBS). Her research interests include: transportation demand management, travel demand forecasting, transportation performance evaluation and econometrics modeling. Three of her research papers have been published in the Journal of Transportation Research Record. Some other research findings have been presented in international conferences including the Transportation Research Board, INFORMS, the World Conference on Transportation Research, and IEEE Conference on Intelligent Transportation Systems. During her study period in the Netherlands, she got a fellowship of the International Road Federation, USA. She also won a bronze medal in the Young European Arena of Research, Future Visions of Transport. In 2012, she spent four months in the Transportation Center of Northwestern University as a visiting scholar, under the supervision of Prof. Hani Mahmassani.
Thesis Abstract
Traffic and transportation statistics are mainly published as aggregated information, and are traditionally based on surveys or secondary data sources, like public registers and companies’ administrations. Nowadays, advanced monitoring systems are installed in the road network, offering more abundant and detailed transport information than surveys and secondary data sources. Usually, these rich data are applied in the field of transportation planning research. But they also seem promising to national statistics offices to update their databases and apply new methods to generate statistics. Transportation demand estimation and forecasting traffic volumes are taken as examples. Quantitative information on transportation demand is important for national and regional policy makers who want to know the number of freight vehicles traveling from origins to destinations. Traditionally, they largely extract this information from the national statistics offices. Transportation research needs the demand data to understand transportation behaviour in the road network, such as congestion and pollution. In the thesis, information methods and hierarchal Bayesian networks are used to demonstrate the approaches to estimate transportation demand. To forecast transportation demand, the hierarchal Bayesian network associated with the multi-process model is applied.
· View and download Yinyi's dissertation
Photos: Chris Gorzeman / Capital Images
