<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kii, Masanobu</style></author><author><style face="normal" font="default" size="100%">Goda, Yuki</style></author><author><style face="normal" font="default" size="100%">Vichiensan, Varameth</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Moeckel, Rolf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assessment of Spatiotemporal Peak Shift of Intra-Urban Transportation Taking a Case in Bangkok, Thailand</style></title><secondary-title><style face="normal" font="default" size="100%">Sustainability</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.mdpi.com/2071-1050/13/12/6777</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">6777</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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’ 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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uttam Dwivedi</style></author><author><style face="normal" font="default" size="100%">Zhiling Guo</style></author><author><style face="normal" font="default" size="100%">Hiroyuki Miyazaki</style></author><author><style face="normal" font="default" size="100%">Mohamed Batran</style></author><author><style face="normal" font="default" size="100%">Ryosuke Shibasaki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of Population Distribution Map and Automated Human Settlement Map Using High Resolution Remote Sensing Images</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Geoscience and Remote Sensing Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://ieeexplore.ieee.org/abstract/document/8517827</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miyazaki, H.</style></author><author><style face="normal" font="default" size="100%">Kuwata, K.</style></author><author><style face="normal" font="default" size="100%">Ohira, W.</style></author><author><style face="normal" font="default" size="100%">Guo, Z.</style></author><author><style face="normal" font="default" size="100%">Shao, X.</style></author><author><style face="normal" font="default" size="100%">Xu, Y.</style></author><author><style face="normal" font="default" size="100%">Shibasaki, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an automated system for building detection from high-resolution satellite images</style></title><secondary-title><style face="normal" font="default" size="100%">2016 4th International Workshop on Earth Observation and Remote Sensing Applications (EORSA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Guo, Zhiling</style></author><author><style face="normal" font="default" size="100%">Shao, Xiaowei</style></author><author><style face="normal" font="default" size="100%">Xu, Yongwei</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Ohira, Wataru</style></author><author><style face="normal" font="default" size="100%">Shibasaki, Ryosuke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mdpi.com/2072-4292/8/4/271</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">271</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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%.</style></abstract><work-type><style face="normal" font="default" size="100%">Journal Article</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weng, Qihao</style></author><author><style face="normal" font="default" size="100%">Gamba, Paolo</style></author><author><style face="normal" font="default" size="100%">Mountrakis, Giorgos</style></author><author><style face="normal" font="default" size="100%">Pesaresi, Martino</style></author><author><style face="normal" font="default" size="100%">Lu, Linlin</style></author><author><style face="normal" font="default" size="100%">Kemper, Thomas</style></author><author><style face="normal" font="default" size="100%">Heinzel, Johannes</style></author><author><style face="normal" font="default" size="100%">Xian, George</style></author><author><style face="normal" font="default" size="100%">Jin, Huiran</style></author><author><style face="normal" font="default" size="100%">Miyazaki, Hiroyuki</style></author><author><style face="normal" font="default" size="100%">Xu, Bing</style></author><author><style face="normal" font="default" size="100%">Quresh, Salman</style></author><author><style face="normal" font="default" size="100%">Keramitsoglou, Iphigenia</style></author><author><style face="normal" font="default" size="100%">Ban, Yifang</style></author><author><style face="normal" font="default" size="100%">Esch, Thomas</style></author><author><style face="normal" font="default" size="100%">Roth, Achim</style></author><author><style face="normal" font="default" size="100%">Elvidge, Christopher D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Urban Observing Sensors</style></title><secondary-title><style face="normal" font="default" size="100%">Global Urban Monitoring and Assessment through Earth Observation</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Remote Sensing Applications Series</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1201/b17012-6</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">CRC Press</style></publisher><pages><style face="normal" font="default" size="100%">49-80</style></pages><isbn><style face="normal" font="default" size="100%">978-1-4665-6449-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">doi:10.1201/b17012-6</style></notes></record></records></xml>