@inbook{3ec04935cd3a4017b8bcdccee9d6f796,
title = "{\textquoteleft}big data{\textquoteright}: Pedestrian volume using google street view images",
abstract = "Responding to the widespread growing interest in walkable, transit-oriented development and healthy communities, many recent studies in planning and public health are concentrating on improving the pedestrian environment. There is, however, inadequate research on pedestrian volume and movement. In addition, the method of data collection for detailed information about pedestrian activity has been insufficient and inefficient. Google Street View provides panoramic views along many streets of the U.S. and around the world. This study introduces an image-based machine learning method to planners for detecting pedestrian activity from Google Street View images. The detection results are shown to resemble the pedestrian counts collected by field work. In addition to recommending an alternative method for collecting pedestrian count data more consistently and subjectively for future research, this study also stimulates discussion of the use of {\textquoteleft}big data{\textquoteright} for planning and design.",
keywords = "Google Street View, Pedestrian count, Walkability",
author = "Li Yin and Qimin Cheng and Zhenfeng Shao and Zhenxin Wang and Laiyun Wu",
note = "Publisher Copyright: {\textcopyright} 2017, Springer International Publishing Switzerland.",
year = "2017",
doi = "10.1007/978-3-319-40902-3\_25",
language = "English",
series = "Springer Geography",
publisher = "Springer",
pages = "461--469",
booktitle = "Springer Geography",
address = "Germany",
}