Name
Abstract | ||
|---|---|---|
Nowadays, biking is flourishing in many Western cities. While many roads are used for both cars and bicycles, buffered bike lanes are marked for the safety of cyclists. In many cities, segregated paths are built up to have physical separation from motor vehicles. These types of biking ways are regarded as attributes in geographic information system (GIS) data. This information is required and important in the service of route planning, as cyclists may prefer certain types of bikeways. This paper presents a framework for generating networks of bikeways with attribute information from the data collected on the collaborative street view data platform Mapillary. The framework consists of two layers: The first layer focuses on constructing a bikeway road network using Global Positioning System (GPS) information of Mapillary images. Mapillary sequences are classified into walking, cycling, driving (ordinary road), and driving (motorway) trajectories based on the transportation mode with a trained XGBoost classifier. The bikeway road network is then extracted from cycling and driving (ordinary road) trajectories using a raster-based method. The second layer focuses on extracting attribute information from Mapillary images. Cycling-specific information (i.e., bicycle signs/markings) is extracted using a two-stage detection and classification model. A series of quantitative evaluations based on a case study demonstrated the ability and potential of the framework for extracting bikeway road information to enrich the existing OSM cycling road data. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.compenvurbsys.2021.101632 | COMPUTERS ENVIRONMENT AND URBAN SYSTEMS |
Keywords | DocType | Volume |
Bikeway road network, Cycling routing, Collaborative street view data, Transportation mode identification, Object detection | Journal | 88 |
ISSN | Citations | PageRank |
0198-9715 | 1 | 0.40 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuan Ding | 1 | 1 | 1.07 |
| Hongchao Fan | 2 | 17 | 7.44 |
| Jianya Gong | 3 | 541 | 57.06 |