Nicholas Bolten

Yuxiang Zhang, Nicholas Bolten, Sachin Mehta et al.

Inferring the full transportation network, including sidewalks and cycleways, is crucial for many automated systems, including autonomous driving, multi-modal navigation, trip planning, mobility simulations, and freight management. Many transportation decisions can be informed based on an accurate pedestrian network, its interactions, and connectivity with the road networks of other modes of travel. A connected pedestrian path network is vital to transportation activities, as sidewalks and crossings connect pedestrians to other modes of transportation. However, information about these paths' location and connectivity is often missing or inaccurate in city planning systems and wayfinding applications, causing severe information gaps and errors for planners and pedestrians. This work begins to address this problem at scale by introducing a novel dataset of aerial satellite imagery, street map imagery, and rasterized annotations of sidewalks, crossings, and corner bulbs in urban cities. The dataset spans $2,700 km^2$ land area, covering select regions from $6$ different cities. It can be used for various learning tasks related to segmenting and understanding pedestrian environments. We also present an end-to-end process for inferring a connected pedestrian path network map using street network information and our proposed dataset. The process features the use of a multi-input segmentation network trained on our dataset to predict important classes in the pedestrian environment and then generate a connected pedestrian path network. Our results demonstrate that the dataset is sufficiently large to train common segmentation models yielding accurate, robust pedestrian path networks.

Nicholas Bolten, Amirhossein Amini, Yun Hao et al.

People with limited mobility in the U.S. (defined as having difficulty or inability to walk a quarter of a mile without help and without the use of special equipment) face a growing informational gap: while pedestrian routing algorithms are getting faster and more informative, planning a route with a wheeled device in urban centers is very difficult due to lack of integrated pertinent information regarding accessibility along the route. Moreover, reducing access to street-spaces translates to reduced access to other public information and services that are increasingly made available to the public along urban streets. To adequately plan a commute, a traveler with limited or wheeled mobility must know whether her path may be blocked by construction, whether the sidewalk would be too steep or rendered unusable due to poor conditions, whether the street can be crossed or a highway is blocking the way, or whether there is a sidewalk at all. These details populate different datasets in many modern municipalities, but they are not immediately available in a convenient, integrated format to be useful to people with limited mobility. Our project, AccessMap, in its first phase (v.1) overlayed the information that is most relevant to people with limited mobility on a map, enabling self-planning of routes. Here, we describe the next phase of the project: synthesizing commonly available open data (including streets, sidewalks, curb ramps, elevation data, and construction permit information) to generate a graph of paths to enable variable cost-function accessible routing.