2019 Menino Survey of Mayors

The 2019 Menino Survey of Mayors represents the sixth nationally representative survey of American mayors and is based on interviews with 119 sitting mayors from 38 states. The 2019 Survey explores mayoral views on issues ranging from infrastructure and transportation priorities — including mobility and public safety — to the changing nature of work. The 2019 Survey also provides the first in-depth examination of mayors’ reactions to and expectations for the Opportunity Zones program, a significant new federal initiative to stimulate urban development. The 2019 Survey continues with the support of Citi Community Development and The Rockefeller Foundation.Citi Community Development and The Rockefeller Foundatio

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

Ehmi: Review and guidelines for deployment on autonomous vehicles

Human-machine interaction is an active area of research due to the rapid development of autonomous systems and the need for communication. This review provides further insight into the specific issue of the information flow between pedestrians and automated vehicles by evaluating recent advances in external human-machine interfaces (eHMI), which enable the transmission of state and intent information from the vehicle to the rest of the traffic participants. Recent developments will be explored and studies analyzing their effectiveness based on pedestrian feedback data will be presented and contextualized. As a result, we aim to draw a broad perspective on the current status and recent techniques for eHMI and some guidelines that will encourage future research and development of these systems

A Spatial Analysis of the Relationship between Pedestrian Crash Events and Features of the Built Environment in Downtown Atlanta

Pedestrian injuries and fatalities due to motor vehicle crashes are a significant public health concern, and the urban campus of Georgia State University poses unique challenges to pedestrian safety issues. Previous studies of the built environment have link several features to increased pedestrian crash occurrences. Once identified, these features can be modified to create a healthier environment for pedestrians. This study examines the relationship between specific features of the built environment and pedestrian crash events. Environmental audits were conducted to collect information about the built environment around Georgia State campus, and pedestrian crash data was obtained from GDOT. Geographic Information Systems (GIS) was used to create a visual representation of this data in order to establish spatial relationships between the built environment and pedestrian crash events. Results show both positive and negative correlations between certain built environment features and pedestrian crashes. GIS was established as a useful tool for evaluating the spatial distribution and relationship between the built environment and pedestrian injury within a localized area, and provides a springboard for future research that seeks to study this association on a larger scale

Energy Efficient Automatic Streetlight Controlling System using Semantic Segmentation

This study aims to develop a novel streetlight management system powered by computer vision technology mounted with the close circuit television (CCTV) camera that allows the light emitting diode (LED) streetlight to automatically light up with proper brightness by recognizing the presence of pedestrians or vehicles and reversely dimming the streetlight in their absence by semantic image segmentation from video