Y. (Yihong) Wang

Profile

Yihong Wang is currently doing a PhD in the department of Transport & Planning at the Delft University of Technology. His research interest is in travel demand modelling using new big data sources, such as mobile phone data and smart card data. He has an MSc degree in Transport & Planning (2015) at the Delft University of Technology and a BSc degree in Transportation (2013) at the Shanghai Jiao Tong University.

Research

Road network design in a developing country using mobile phone data: An application to Senegal.
Using metro smart card data to model location choice of after-work activities: An application to Shanghai
Using mobile internet usage data to understand travelers’ preferences for different types of trip destination.
Improving the accuracy of detecting activity purpose from mobile phone data.

Research themes

  • Travel demand modelling

Teaching assistant in the Master course: Transportation and Spatial Modelling.

Teaching assistant in the Bachelor course: Inleiding Civiele Techniek.

Yihong Wang, Gonçalo Correia, Erik de Romph, H.J.P. Timmermans (2017). Using metro smart card data to model location choice of after-work activities: An application to Shanghai. Journal of Transport Geography, 63, 40-47.

Yihong Wang, Gonçalo Correia, Bart van Arem, H.J.P. Timmermans, Erik de Romph (2017). Understanding multiday activity patterns based on mobile internet usage behaviour. NetMob 2017.

Yihong Wang, Gonçalo Correia, Erik de Romph (2015). National and regional road network optimization for Senegal using mobile phone data. Data for Development (D4D) Challenge, NetMob 2015.

Using new big data sources to inform travel demand models

Description: In recent years, various new big data sources are getting popular for mobility research. Despite its potential advantages such as being less expensive and large in sample size, people are still doubting if such data can really complement or replace traditional travel survey data because the information of these data is usually limited. For example, activity purpose of the trips is typically missing, and personal attributes are also unavailable mainly due to privacy reasons. In this project, I will try to answer the following research questions by conducting several case studies: For travel demand modelling, can we completely replace traditional travel survey data with the new big data? What changes should be made to the existing travel demand models if they are fed the new big data? Can these new big data bring new insights on human mobility behaviour that has never been revealed in survey data?

Reported by TU Delta for winning the Transport Prize in the D4D Challenge.

Ir. Yihong Wang

PhD candidate