@article {129,
	title = {Assessment of Spatiotemporal Peak Shift of Intra-Urban Transportation Taking a Case in Bangkok, Thailand},
	journal = {Sustainability},
	volume = {13},
	year = {2021},
	pages = {6777},
	abstract = {Reducing congestion has been one of the critical targets of transportation policies, particularly in cities in developing countries suffering severe and chronic traffic congestions. Several traditional measures have been in place but seem not very successful. This paper applies the agent-based transportation model MATSim for a transportation analysis in Bangkok to assess the impact of spatiotemporal transportation demand management measures. We collect required data for the simulation from various data sources and apply maximum likelihood estimation with the limited data available. We investigate two demand management scenarios, peak time shift, and decentralization. As a result, we found that these spatiotemporal peak shift measures are effective for road transport to alleviate congestion and reduce travel time. However, the effect of those measures on public transport is not uniform but depends on the users{\textquoteright} circumstances. On average, the simulated results indicate that those measures increase the average travel time and distance. These results suggest that demand management policies require considerations of more detailed conditions to improve usability. The study also confirms that microsimulation can be a tool for transport demand management assessment in developing countries.},
	issn = {2071-1050},
	doi = {10.3390/su13126777},
	url = {https://www.mdpi.com/2071-1050/13/12/6777},
	author = {Kii, Masanobu and Goda, Yuki and Vichiensan, Varameth and Miyazaki, Hiroyuki and Moeckel, Rolf}
}
@proceedings {99,
	title = {Development of Population Distribution Map and Automated Human Settlement Map Using High Resolution Remote Sensing Images},
	year = {2018},
	doi = {10.1109/IGARSS.2018.8517827},
	url = {https://ieeexplore.ieee.org/abstract/document/8517827},
	author = {Uttam Dwivedi and Zhiling Guo and Hiroyuki Miyazaki and Mohamed Batran and Ryosuke Shibasaki}
}
@proceedings {89,
	title = {Development of an automated system for building detection from high-resolution satellite images},
	year = {2016},
	doi = {10.1109/EORSA.2016.7552806},
	author = {Miyazaki, H. and Kuwata, K. and Ohira, W. and Guo, Z. and Shao, X. and Xu, Y. and Shibasaki, R.}
}
@article {87,
	title = {Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods},
	journal = {Remote Sensing},
	volume = {8},
	year = {2016},
	pages = {271},
	type = {Journal Article},
	abstract = {In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22\% and the CNN method is also competitive with an overall accuracy of 96.30\%.},
	issn = {2072-4292},
	url = {http://www.mdpi.com/2072-4292/8/4/271},
	author = {Guo, Zhiling and Shao, Xiaowei and Xu, Yongwei and Miyazaki, Hiroyuki and Ohira, Wataru and Shibasaki, Ryosuke}
}
@inbook {64,
	title = {Urban Observing Sensors},
	booktitle = {Global Urban Monitoring and Assessment through Earth Observation},
	series = {Remote Sensing Applications Series},
	year = {2014},
	note = {doi:10.1201/b17012-6},
	month = {2014},
	pages = {49-80},
	publisher = {CRC Press},
	organization = {CRC Press},
	isbn = {978-1-4665-6449-7},
	url = {http://dx.doi.org/10.1201/b17012-6},
	author = {Weng, Qihao and Gamba, Paolo and Mountrakis, Giorgos and Pesaresi, Martino and Lu, Linlin and Kemper, Thomas and Heinzel, Johannes and Xian, George and Jin, Huiran and Miyazaki, Hiroyuki and Xu, Bing and Quresh, Salman and Keramitsoglou, Iphigenia and Ban, Yifang and Esch, Thomas and Roth, Achim and Elvidge, Christopher D.}
}
