Pedestrian network attributes-- Datasets

Title: Attributing pedestrian networks with
semantic information based on multi-source spatial dataAbstract: The
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<i> </i>walkway, indoor path<i>,</i> 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.