All-in-One Urban Mobility Mapping*

The All-in-One Urban Mobility application (AIO) is a multi-platform application with various uses. The general purpose is to provide its users with a holistic view of their surroundings, including other vehicles, pedestrians, and bicycles. These subjects are tracked anonymously; no private information is used, simply location an identifier (such as obstacle, bicyclist, etc).

All of these locations are displayed on a mobile app that runs on a smart phone. These locations, however, are tracked one of two ways:

1. Via a GPS-enabled smart phone

2. Via infrastructure, using cameras.

The smart phone method is simple, and pings the device regularly to track its location. The infrastructure method, on the other hand, utilizes computer vision, machine learning, object detection and trilateration to pinpoint an object or person's location on the map, even if it does not have the app! These all come together to show commuters what's around them and assist them in making decisions that might save lives.


Screenshot of AIO application 





                Custom icons for AIO application

AIO application home screen.                                             Custom icons used in AIO application.


Infrastructure camera detection and corresponding mapInfrastructure camera recognizes a pedestrian in its field of view and places the object on the map as a pedestrian.


AIO stalled vehicle example

Each vehicle is using a GPS-enabled device to show its location. The vehicle on the left was originally in the primary user's blind spot with the hill blocking the primary driver's view. With the AIO application, the primary user was able to stay behind the stalled vehicle until the vehicle traveling in the opposite direction passed.


*Joint project with Dr. Alex V. Samoylov (Transportation System Planning, Georgia Tech Research Institute) and Gary McMurry (Mechanical Engineering Georgia Tech Research Institute)


Connected Autonomous Vehicles*

Due to scientific and technological breakthroughs, the deployment of massive autonomous vehicles on public roadway systems is on the horizon, which will undoubtedly revolutionize the transportation ecosystem in the near future. Though autonomous vehicles hold a lot of promises, some major hurdles, such as safety risks or efficiency weaknesses, must be first overcome. For example, if an autonomous vehicle runs into a heavy storm, the GPS system might be affected and the sensing capabilities might be decreased. To keep its driving safety, this vehicle needs to obtain substantial situational awareness by taking advantage of the real-time data from the transportation infrastructure or other vehicles. This project involves connected autonomous vehicles and investigation on the large-scale fleet management/coordination in extreme/complex urban driving scenarios. 


       see-through pedestrian detection   upcoming electrical truck   electric truck in rear vehicle's view

Examples of the see-through algorithm in action. Pedestrian detection with see-through activated (left). Vehicles approaching blocked lane, which would be unknown to the rear vehicle without see-through (middle). The vehicle blocking the road coming into the view of the rear vehicle, who has already begun a safe pass with the information gained from the see-through application (right).

5th Street Testbed

5th Street testbed on UTC's campus.


 *Joint project with Dr. Alex V. Samoylov (Transportation System Planning, Georgia Tech Research Institute) and Gary McMurry (Mechanical Engineering Georgia Tech Research Institute)

Smart Urban Connectivity Powered by Mobility-on-Demand Public Transportation*

In order to confront the unprecedented challenges of rapid urbanization and to achieve our vision of smart cities, we begin a fundamental and timely research on smart urban connectivity. More specifically, we propose to design a collaborative mobility-on-demand shared-ride public transportation system to improve mobility and accessibility for all the people including special-needs travelers such as the elderly and disabled. An intelligent connectivity control and management center will be developed for such a transportation system to provide real-time (1) demand and service management; (2) decision making for vehicle route and mobility; and (3) personalized trip planning. Active transportation (e.g., biking and walking) will be promoted in personalized trip planning based on traveler’s health conditions and physical capabilities as well as location and environmental parameters. To support the required ubiquitous connectivity for sensors, Internet of Things (IoT), and Internet of People, a reliable infrastructure for public communications is needed. For this purpose, we will investigate citywide hyper-dense small cells. Chattanooga is the perfect city to pilot the proposed project, due to the citywide fiber optics network.

Smart cities consisting of urban residents, urban environmental systems, and various physical infrastructures and cyber-infrastructures are highly complex and dynamic. All of these constituent components have intricate interdependencies, interrelations, and interactions which impose a huge amount of challenges on understanding and building smart cities. Innovative solutions to these real-world challenges will be proposed and developed using computationally-intensive data analytics, graph analytics, simulation and modeling, optimization, operations research, and urban planning.

*Joint project with Dr. Craig Tanis (Computer Science) 


Intelligent Urban Planning*

Our proposed research is intended to facilitate smart decision and policy making that directly impacts the future of a health-informed approach to urban planning and development. We propose to investigate: (1) the exploration of mass sensing in the urban planning process that yields a more ubiquitous observation of key urban indicators; (2) population health monitoring and assessment to study the impact of the proposed project on human wellbeing; and (3) urban planning practices that address smart land use and active living promotion. We plan to build a holistic view of the city, infrastructure, environment, transportation, and improved health status of community residents. At the same time, we will try to demonstrate the fundamental interdependencies, interactions, and inter-relationships among the constituents of this holistic view, examining the relative costs of infrastructure for active living and their resultant ROI in terms of improved health status among community residents.

*Joint project with Dr. Gregory Heath (Health and Human Performance) and Dr. James Newman (Computational Engineering)