Attributing pedestrian networks with semantic information based on multi-source spatial data

Abstract

Xue Yanga, Kathleen Stewartb, Mengyuan Fangc & Luliang Tangc*a School of Geography and Information Engineering, China University of Geosciences, Wuhan, Chinab Department of Geographical Sciences, University of Maryland, College Park, MD, USAc State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaXue Yang received the Ph.D. degree from Wuhan University, Wuhan, China, in 2018. She is currently an associate Professor with China University of Geosciences, Wuhan. Her research interests include intelligent transportation system, spatiotemporal data analysis, and information mining. Homepage: http://grzy.cug.edu.cn/yangxue1/zh_CN/index.htmEmail: Kathleen Stewart is currently a Professor in the Department of Geographical Sciences and Director of the Center for Geospatial Information Science. She works in the area of geographic information science with a particular focus on geospatial dynamics. She is interested in mobility and spatial access, often in a big geospatial data context and using approaches that lie in the expanding field of spatial data science. Homepage: https://geog.umd.edu/facultyprofile/stewart/kathleenEmail: Mengyuan Fang received Bsc degree from Wuhan University, Wuhan, China, 2014. He is currently a Ph.D candidate at the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University. His research addresses the issue of traffic congestion detection and prediction using big trace data.Email: Luliang Tang received the Ph.D. degree from Wuhan University, Wuhan, China, in 2007. He is currently a Professor with Wuhan University. His research interests include space–time GIS, GIS for transportation, and change detection. Homepage: http://www.lmars.whu.edu.cn/index.php/js/298.htmlEmail: CONTACT Luliang Tang tll@whu.edu.cnABSTRACTThe lack of associating pedestrian networks, i.e. the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

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