@inproceedings{1863fb03fc194586a554b653ac12e308,
title = "Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore",
abstract = "In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling.",
keywords = "GWR, hedonic model, public housing prices, taxi travel time",
author = "Yi'an Wang and Fangyi Cai and Cheng, \{Shih Fen\} and Bo Wu and Kai Cao",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 29th International Conference on Geoinformatics, Geoinformatics 2022 ; Conference date: 15-08-2022 Through 18-08-2022",
year = "2022",
doi = "10.1109/Geoinformatics57846.2022.9963833",
language = "英语",
series = "International Conference on Geoinformatics",
publisher = "IEEE Computer Society",
editor = "Shixiong Hu and Xinyue Ye and Hui Lin and Song Gao and Xinqi Zheng and Chunxiao Zhang",
booktitle = "Proceedings - 2022 29th International Conference on Geoinformatics, Geoinformatics 2022",
address = "美国",
}