Scroll through and explore these exciting next-generation application in various stages of development. All these applications use next-gen technologies like software-defined networking, local cloud and gigabit to end-user and are transforming how we live, work, learn and play. If you have an application you would like to include in this growing list, please following the instruction below and join the US Ignite community.
DC electricity locally generated by solar energy sources can reduce energy losses by as much as 30% or more.
See the latest network monitoring/troubleshooting and testing technologies - using inline microprobes and virtual test agents. JDSU would display live demonstrations of JDSU's innovative PacketPortal technology using Laptop based applications and an external network switch, equipped with PacketPortal 1G Ethernet SFProbes. This would be used to display live remote traffic monitoring.
The application demonstrates impacts of cyber attacks in a power grid, and how the wide-area communications helps re-stabilize the grid.
ParaDrop is a virtualized platform for home WiFi gateways that allows diverse third-party services (e.g., home automation) to be easily installed and managed. - We will show how developers can install different applications and services on a home WiFi gateway to: 1) Sense the home environment by collecting temperature and humidity information from home deployed WiFi sensors; 2) View a home security camera that stores its data privately in the home gateway; and 3) Operate a media transcoding service that allows us to stream news channels to the home users over WiFi paths.
We are developing tools to help city planners make smarter decisions around real-time dynamic scheduling of public transportation.
Integrating 'Structured Network Packet Knowledge'™ with 'Dynamic Analytical Variables'™ up until now has not been readily available to the Vehicle (Packet) Traffic System.
Description: Our second autonomous vehicle pilot will build upon the “lessons learned” from our GCTC 2015 “First Mile/Last Mile” study (GCTC Action Cluster SMOOTH). The City of Columbus was recently named one of the seven finalists in the US Department of Transportation Smart City Challenge. Our GCTC 2016 project will, therefore, concentrate on a scalable and replicable low speed automated shuttle solution for use in the Smart City of Columbus. This automated shuttle solution will use a small (two-seater) electric driverless vehicle with a scalable and replicable software, hardware control and decision making architecture. The eventual aim of the project is pilot deployment in an urban driving environment with low speed vehicles and intersections at/near the outdoor shopping area of Easton Town Center in Columbus. The scalable and replicable approach will enable the easy adaptation of the same system to other parts of the City of Columbus and to other similar pilot deployment sites in other cities in the US. Challenges: The City of Columbus is a typical mid-sized city in the U.S. with a steadily growing population. Indeed, the population of Central Ohio where Columbus is located is projected to increase by over 500,000 until 2050. Planning and developing a smart regional transportation system is an important challenge and issue to be addressed. Columbus does have a bus system but it is not easily accessible especially by the people who need it most due to the first-mile and last-mile problem. Being one of the seven finalists in the U.S. Department of Transportation Smart City Challenge, the City of Columbus has identified the use of automated electric shuttles as a potential solution to this challenge. The use of electric shuttles will also introduce a beneficial environmental footprint. The SmartShuttle project is on the development and use of low speed automated electric shuttles in a typical deployment site, the Easton Town Center outdoor shopping area. It is expected that the SmartShuttle concept will be easily scaled and replicated for use in other deployment sites in the city and in other cities. Major Requirements: • Develop technical expertise • Build relationships • Address state legislation • Address local legislation • Do proof-of-concept demo • Scale and replicate for other deployments We will continue to develop the technical expertise and build the required relationships/partnerships with the private sector, at OSU, other academic and research entities, and with state and federal organizations. New legislation allowing testing of autonomous vehicles with manufacturer license plates has been accepted in Ohio. We will use it for pilot deployments and implementations within Columbus. We estimate a base budget for the SmartShuttle proof-of- concept” pilot deployment at $300,000 to $400,000 with a cost-sharing approach between government and industry. If we are awarded the USDOT $40M “Smart City Challenge” we may extend the proof-of-concept testing in Easton Town Center to an actual deployment, with deployments in other parts of the city to follow. Performance Targets/ Key Performance Indicators (KPIs): Improvements will be calculated on a per smart shuttle basis and will be extrapolated based on the number of vehicles planned in actual deployments. A 20% improvement in the traffic jams in the Easton Town Center area and other SmartShuttle deployment sites as the automated electric vehicles will replace a certain portion of road traffic. A 50% improvement in solving the first-mile and last-mile problem in the deployment sites. A 20% reduction in air pollution due to traffic in the deployment sites. Measurement Methods: The traffic jam improvement will be determined and extrapolated for a larger deployment. The number of people in bus stops using the on-demand automated shuttles will be recorded. The tons of CO2 reduction based on reduced emissions from drive alone vehicles replaced by the electric shuttles will be used for air pollution reduction computation. Standards/Interoperability: There are currently no established standards especially for low speed automated shuttles. Interoperability of shuttles from different vendors using different automation and operation architectures is a serious concern. We will develop a unified and hence interoperable architecture for this purpose. An NSF EAGER project for the NIST GCTC call has been proposed for this purpose. Replicability, Scalability, and Sustainability: The automated shuttles used will have a scalable and unified automation solution, making the results easily transferrable to other vehicle types and other pilot deployment. This will enable the scaling and replication of the automated shuttle solution in Easton Town Center to other parts of Columbus (downtown area, OSU campus) and to other cities. Project Impacts: We will be providing a full mobility solution to people who could not easily commute to work due to the first-mile, last-mile problem increasing their chances of getting a job. There will be a reduction in traffic jams, which will increase the quality of life of those people who are affected by it. There will be an improvement in air quality and heavily populated area due to the use of electric vehicles. Columbus and Ohio will experience economic growth due to the new jobs created by the automated shuttle industry including development, operation and maintenance. Demonstration/Deployment Phases: Phase I Demonstration June 2016: The demonstration in June 2016 will use a street legal and automated two seat neighborhood electric vehicle. This vehicle is already equipped with 4G connectivity by Verizon. This demo will show its architecture and its automated driving capabilities. Phase II Demonstration June 2017: The aim of the June 2017 proof-of-concept demo is to use the same automated neighborhood electric vehicle in the Easton Town Center outdoor shopping area within a mixed traffic environment with other vehicles and pedestrians. Success of the Smart City Challenge application of the City of Columbus will have an important impact on this second demo as the Easton Town Center will have a fleet of autonomous electric vehicles operating in a loop in that case. This will ease the demo deployment which is aimed at the inner loop in the Easton Town Center area. The City of Portland is also a Smart City Challenge finalist. If the City of Portland is successful in its Smart City Challenge proposal, the results will be transferred to a chosen site in Portland. The cities of Greenville, Boston and Washington D.C. are also interested in deploying the results of the SmartShuttle technical cluster and will have the chance to do so by working with the cluster leads and the manufacturer of the automated vehicle.
Description: Our second autonomous vehicle pilot will build upon the “lessons learned” from our 2015 “First Mile/Last Mile” study and will have (a) a demonstration using a street legal neighborhood electric vehicle with on-demand ordering capability, (b) a pilot operation in a chosen pedestrian area on the OSU main campus, (c) demonstration of socially acceptable collision avoidance Challenges: The population of Central Ohio is projected to increase by over 500,000 by 2050. Planning and developing a smart regional transportation system is an important challenge and issue to be addressed. The Ohio State University campus is an excellent testbed to try new modes of transportation like on-demand automated shuttles. These on-demand automated shuttles will also work in pedestrian zones in campuses like the Ohio State University campus. Automated shuttles operating close to large pedestrian groups creates the important problem of socially acceptable collision avoidance with pedestrian groups. Major Requirements: We will continue to develop the technical expertise and build the required relationships/partnerships with the private sector, at OSU, other academic and research entities, and with state and federal organizations. We will also sponsor legislation that will be necessary for future pilots and implementations to occur beyond the Ohio State University core campus. We estimate a base budget for the SMOOTH(2) “Proof of Concept” pilot at $300,000 to $400,000 with a cost-sharing approach between government and industry. If we are awarded the USDOT $40M “Smart City Challenge” we may increase the scope of the pilot deployment in the Ohio State University campus. Performance Targets/ Key Performance Indicators (KPIs): A 20% increase in use of automated electric vehicles in transportation and a reduction of CO2 tons of emissions. Calculations will be made based on one electric vehicle we automate and will use to replace a fossil fuel powered vehicle. This impact will be extrapolated to the number of automated electric vehicles that the City of Columbus plans to implement in its Smart City Challenge proposal plan. Measurement Methods: Measurement will be based on the usage of a fossil fueled vehicle in the OSU campus route(s) chosen for the pilot test. Standards/Interoperability: We will be compatible with the National Architecture and interconnect to multiple transportation venues accordingly. Replicability, Scalability, and Sustainability: Our solution will be based on model based design for ease of replicability, scalability and sustainability. Project Impacts: Safely deploying electric, autonomous vehicles will have a significant impact on lowering CO2 emissions and radically transform the global transportation system. We will be providing a first-mile and last-mile solution to the residents of the Ohio State University campus (replicable in the city and elsewhere). Demonstration/Deployment Phases: Phase I Pilot/Demonstration June 2016: A proof-of-concept AV demo in the Ohio State University campus is planned. New legislation allowing autonomous vehicle testing in the state of Ohio has passed. We are working on how it will affect university campus deployments as the university is not a manufacturer. Phase II Pilot/Demonstration June 2017: A proof-of-concept demo deployment in a pedestrian only zone of the Ohio State University campus is planned. Socially acceptable collision avoidance will be demonstrated.
Description: This project will extend the previously developed Transit-Hub App (http://thub.isis.vanderbilt.edu) with a multi-modal transit planning services that will include park and ride park integration as well as car share service integration. The unique innovation will be dynamic multi-horizon look ahead optimization algorithm that will address individual constraints as well as global city scale criteria. Challenges: 1. Multi-domain data and service integration. Each service has a different protocol and we must be able to map these different protocols to create a combined service. 2. Predictive analytics at different time scales. 3. Solving distributed optimization problem across different time scales. 4. A resilient computing platform that can integrate applications across edge nodes and cloud computing nodes to provide an open platform that can be used by others for building different solutions. Major Requirements: We will use the Siemens City-Hub SDK to provide an integrative environment in which data from different ride share services and parking services will be integrated. Extend the current transit application to create an open platform that will provide REST API for other applications to query trip planning services. Implement a scalable multi-level optimization approach that will be used to plan services for each individual user. Deploy the service in form of an updated application and an analytics dashboard that can be accessed by citizens in Nashville.The service may be made accessible via smart kiosks across Nashville as well. Key Performance Indicators (KPIs): Provide a single solution for planning trips with public transportation, including public parking availability for first/last mile Increase in number of people using the public transit system. Measurement Methods: We will count the number of people using the transit using the data collected by the apps as well as the data collected by the automated passenger counters. Standards/Interoperability: This solution will be based on standard middleware technology and an open platform for integrating IoT services called city-hub SDK (for cloud https://github.com/siemens/cityhub-sdk) and CHARIOT (for edge nodes.) We will also provide augmented feed in GTFS format for other application developers. Replicability, Scalability, and Sustainability: This project is aiming to create an open platform that can be replicated and can scale to other cities as well. We are committed to using open standards and protocols wherever possible to replicate the efforts in other cities as well. Project Impacts: This project will provide better contextual services for people using the public transit and shared vehicle services in Nashville. We expect that overtime this effort will lead to reduction in congestion and increase in utilization of shared transportation services. Demonstration : Phase I Pilot/Demonstration June 2016: - Integration of parking data analytics and transit hub. Phase II Deployment June 2017: - This will showcase the full platform and the results collected from people using it.
This project aims to address urban transportation and logistics issues by developing a shared vehicle platform called the Persuasive Electric Vehicle (PEV) Platform. The Boston, MA-based team will prototype an autonomous tricycle designed to navigate on bike lanes and reduce transportation delays, noise, emissions, and delivery times for packages and goods ordered online. The PEV platform concept will improve urban transportation through autonomy-enabled vehicle sharing and improve urban logistics by autonomously delivering packages. The PEV platform prototype will use routing algorithms to autonomously navigate the bike lanes. The potential impact of PEV fleets will be demonstrated in simulation studies involving (1) a realistic urban environment; (2) autonomous and manual navigation of the PEV platform; and (3) quantification of the potential impact of PEV fleets.
The project aims to develop individualized traffic-, event-, and context-aware travel planning simulation for users of public transit services. The resulting intelligent service hub will be accessed via smart applications and will support real-time notifications and alerts to help riders track their routes and potential delays, as well as facilitate information flow (dynamic route demand) from potential riders to transit service providers. A critical aspect of this work is an integrated decision support tool, which allows riders to plan future trips and enables city officials to assess and improve the efficacy of transit options based on models generated from ridership data. This work integrates several data streams generated from sensors including automated vehicle locators, personal smart phones, and beacons. The intelligent service hub will be staged and tested in Nashville, Tennessee. Traffic congestion in this rapidly growing metropolis has nearly doubled in the past decade. It is further challenged by multiple limitations in deploying the types of public mass transportation networks as seen in larger cities. A planned future innovation will be transitioning the system to a hardware device with which all users can interact.
Autonomous or driverless vehicles may have potential to improve accessibility, reduce travel costs, reduce air pollution, and change how we travel. Greenville County, SC plans to deploy automated taxi shuttles (aTaxis) as first/last mile solutions in several multi- modal environments. Automated vehicles will be evaluated under various conditions to assess life-cycle costs, performance, reliability, rider receptivity, and usage patterns. This will aid in developing a financially sustainable business model. At the GCTC EXPO, a self-driving “taxi” shuttle called Bruin1 was deployed on the lawn of the National Building Museum. Bruin1 was activated in 2014 by undergraduate engineering students at Bob Jones University (BJU), who automated a locally assembled Star EV using off-the-shelf technology and demonstrated it in various Greenville locations. In the demo, a rider calls an aTaxi via phone. The driverless aTaxi comes to the rider who boards, selects a stop, and is taken directly and quietly to the destination. The aTaxi then responds to another rider or returns to its mobility hub station. During a second pilot test is anticipated at the 1,100 acre GreenVillages planned development.
Applied Robotics for Installation and Base Operations (ARIBO) is a new approach to technology transition for the Army. It is a series of living laboratories that coordinate effort and investment to accelerate the adoption of automated technologies by placing them in useful scenarios where they can safely have a positive impact – today. ARIBO aligns objectives across government agencies and affordably leverages investments in automated ground systems for common goals. This project utilizes automated vehicle pilots to provide data designed to accelerate the adoption and commercialization of UAVs. It combined communication; controls, surveillance, and environmental modeling in autonomous unmanned vehicle systems to help save lives through fast response in warfighting and disaster scenarios. Team ARIBO, sponsored in part by the U.S. Army Tank Automotive Research, Development and Engineering Center (TARDEC), demonstrated the potential benefits of cyber-physical systems technology, programs and test beds to improve safety, sustainability, efficiency, mobility and overall quality of life. Autonomous systems and autonomy-enabled manned ground platforms are enabling capabilities that provide force multiplication to warfighting functions and to large installations. They help develop an understanding of how to leverage autonomy and autonomous.
A team based in Portland, Oregon is developing a sensor-connected “smart” corridor where transit data, traffic signalization, and air quality sensing are accessible in a data portal with visualization and analytics to reduce air pollution. The purpose is to identify the effects of traffic signal systems and rapid transit on neighborhood air quality (e.g., particulate dispersion into neighborhoods). The team will explore lower cost sensors to measure air quality and how modeling and analytics can help local government make good transportation policy choices. A first phase test-bed investigated air quality impacts of transit and transportation decision-making in a discrete, wired corridor of Portland known as the Powell Boulevard corridor, which includes a major intra-city highway, the Clinton Street bikeway, and the Division Street development. At the GCTC EXPO, the team showcased their successful test-bed research and resulting analysis related to air quality impacts of transit and transportation decision-making in the Powell Boulevard corridor.
The PA 2040 initiative utilizes wireless service to improve maintenance and transportation management and user experience on the Pennsylvania Ave corridor in Washington D.C. The foundation for the system is envisioned as a pervasive mesh Wi-Fi network. The Wi-Fi will serve to provide free internet access to the public, secure services to public safety entities, and extend connectivity for environmental sensors. In addition, the Light Sensory Network will bring advanced wireless networking, advanced sensors (e.g., video), and edge-based processing of real-time street data to the LED streetlights to solve parking, traffic, public safety, and street operations and maintenance issues on the corridor. A sampling of the capabilities includes wayfinding, parking demand management, improved maintenance response times and emergency response management. Analytics from the public Wi-Fi will also allow for greater information about the origins of corridor visitors to enable a more customized user experience with language options. The analytics could help improve overall planning and programming of the public space.
Many people in the United States do not live or work close to public transportation (e.g., bus, train, metro). They are often faced with the ‘first mile’ problem—getting to the bus stop to initiate mobility . T ransportation stops are not always close to the last point of interest (e.g., grocery store, mall, pharmacy, work). This creates the ‘last mile’ problem—how to reach your final destination after you get off the bus. While walking may be a solution for some, it is not viable for everyone (e.g., the elderly, handicapped, parents with small children). The SMOOTH project uses a network of on-demand automated vehicles linked with applications that enable passengers to schedule and track on-demand vehicles using their smartphones. SMOOTH will operate in the city of Columbus, Ohio, with the Ohio State University (OSU) Transportation Hub providing transport between selected stops within the outer campus and automated shuttle driving within the main campus.