Yihong Wang

Yihong Wang
Delft University of Technology | TU · Department of Transport & Planning

MS in Transport & Planning

About

7
Publications
2,057
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109
Citations
Introduction
Yihong Wang is currently doing a PhD in the department of Transport & Planning at the Delft University of Technology. He has been working on travel demand modeling using new big data sources, such as mobile phone data and smart card data.

Publications

Publications (7)
Article
A location choice model explains how travellers choose their trip destinations especially for those activities which are flexible in space and time. The model is usually estimated using travel survey data; however, little is known about how to use smart card data (SCD) for this purpose in a public transport network. Our study extracted trip informa...
Article
New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviou...
Article
This study uses mobile phone data to understand mobility patterns in a country, with limited mobility data, in order to give advice about decisions on how to design the national and regional road network. Our method consists of three parts: (1) filtering mobile phone traces to derive mobility patterns, (2) building an adapted formulation of the gra...
Article
Full-text available
In a multimodal public transport network, transfers are inevitable. Planning and managing an efficient transfer connection is thus important and requires an understanding of the factors that influence those transfers. Existing studies on predicting passenger transfer flows have mainly used transit assignment models based on route choice, which need...
Conference Paper
Full-text available
In traditional travel demand models, socio-demographic and socio-economic information is used to segment population and thus better explain the heterogeneity of travel behavior. However, such information is generally absent in anonymous mobile phone data due to privacy reasons. This gap should be bridged if we aim to replace traditional survey data...
Conference Paper
Full-text available
Due to the scarcity of mobility data in Senegal, mobile phone data provided by the D4D Challenge is used for optimization of the national and regional road network in Senegal. We first applied a filtering algorithm to estimate interdepartmental origin-destination trip matrices (OD matrices) of sampled users in 2013. We name these matrices relative...

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Projects

Project (1)
Project
To estimate, explain and predict travel demand using new big data.