Accessible Routes Integrating Data from Multiple Sources

Abstract

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

Adaptive personalized routing for vulnerable road users

“…Therefore, the more up‐to‐date sidewalk information, the better the quality of paths provided by VRUPOD. Several studies (e.g., [14, 15]) are currently underway to provide frequent and valuable updates on sidewalk conditions, which will significantly benefit VRUPOD. As a result of the dynamic weights of the sidewalk parameters, VRUPOD prevents long detours if the trip has progressed for a longer time. Specifically, the weights assigned to parameters such as length increases as the user approaches the destination to prevent very long detours.…”

“…Other developmental projects in this regard include the collective tagging Android application for volunteered geographic information on accessibility [12], and IncluSet, which integrates accessibility data from multiple sources [13,14]. Although the above studies are promising, several concerns persist in validating and maintaining accessibility data.…”

Accessible Path Finding for Historic Urban Environments: Feature Extraction and Vectorization From Point Clouds

“…Another possibility is to combine dataset coming from different sources, to compute optimal routes. This is the case of (Luaces et al, 2021), where authors used data from Open Street Map (OSM), added information by processing MLS point clouds, detected obstacles using crowdsourcing and identified accessibility issues analysing social networks data. These data were then used to produce an application able to compute optimal routes considering different needs.…”

Pedestrian-Accessible Infrastructure Inventory: Enabling and Assessing Zero-Shot Segmentation on Multi-Mode Geospatial Data for All Pedestrian Types

“…There is a lack of understanding of the potential of various geospatial technologies, especially the recent ones such as mobile LiDAR, to fulfill the needs of creating pedestrian-accessible infrastructure inventory. This recognition has led to studies exploring the fusion of multiple types of data for pedestrian infrastructure inventory [ 18 , 19 , 20 , 21 ].…”