Brief Description:

The Project entails the dual deployment of Argonne National Labs’ Array
of Things (AoT, v.2) and Georgia Tech Research Institute’s Campus Array Node (CAN, v.1). In combination, these two sensor networks will aid multi-modal transit from the perspective of both operators and users.
Partnering with the City of Atlanta, Georgia Tech Research Institute, and Argonne National Labs, and focusing on issues related to multi-modal transit, the Project focuses on the on-the-ground interoperation of two Internet of Things systems—AoT focused on environmental sensing and CAN focused on traffic/public safety monitoring—and how to display real-time data for public transit operators, pedestrians, and officials in meaningful ways that inform efficient, effective, and comfortable use of various mobility options.
From the engineering and infrastructural considerations to the political and community issues, the Project is a case study of a cyber-physical system that will result in a model of how to proceed with issues of interoperable Internet of Things systems, from technical standards to community engagement to areas of future work.

Major Requirements:

The Project will proceed in two phases (see “Demonstration/Deployment Phases”) that focus on a use case of multi-modal transit. During Phase 1, the campus testbed will provide an example transit system where riders/users switch between different transit options (e.g. riding city rails, riding a bus or multiple buses, and walking). This testbed will be scaled up and out of the campus to include local neighborhoods and a more general public (i.e. not just members of the university campus). As such, the following requirements repeat in both phases, but only Phase 1 will be mentioned below.
1.a: Establish communication, storage, and distribution standards that integrates both IoT systems; establish data center.
1.b: Establish permission from various partners and bodies (e.g. vetting of privacy measures through IRB; coordination with campus facilities, transit, planning, and governance bodies; establish MOUs for data storage and access across partnering groups).
1.c: Establish clear roles for various partners during Phase 1 in order to mitigate further negotiations during Phase 2.
2. Install the CAN and AoT systems, including concerns of power, maintenance, and connectivity.
3. Develop user-facing displays (e.g. kiosk and/or mobile displays) for combined data stream: one for bus operators and one for multi-modal transit users.
4. Evaluate use cases of multi-modal transit through data analysis (i.e. analyze interval consistency on campus circuit buses in order to increase predictability) and empirical observation (i.e. study multi-modal users and their wait times at stops based on available information). Combine data analytics and empirical observations with user feedback to iterate on system.
5. Create procedures for scaling up/out from testbed.

Key Performance Indicators (KPIs):

Increase consistency of campus circuit buses by 20% by mitigating back-ups (operator KPI, KPI-1)
2. Decrease wait time for riders by 20% by increasing informed decision-making (rider KPI, KPI-2)

Measurement Methods:

Consistency can be measured by the arrival interval of each bus. Measuring this interval provides a proxy for how long each rider must wait, as well as a proxy of the quality of service (i.e. the ability to predict the next bus on a set schedule). Beyond knowing where other buses are on campus (enabled by the existing NextBus application), consistency requires predictive feedback about traffic, pedestrian, and weather-related slow downs. (KPI-1)
2. In combination with (1), decreasing wait time can be measured by how frequently a bus arrives. However, this measurement assumes riders/users wait until the bus arrives. A multi-modal system that gives accurate and consistent information includes encouraging informed decision-making about multi-modal options (e.g. choosing to walk, bike, take an adjacent line, or wait on a particular bus line to reach a destination). As such, informed decision-making can be measured through empirical observation at bus stops and whether individuals wait, walk, or choose other options. (KPI-2)
3. Building on (2), the reduction of wait time is also perceptual, meaning it is not solely based on an objective measure of wait time. As such, an additional survey of how people perceived their wait will be performed. This information provides a layer of understanding how Smart Cities/IoT systems work at the individual experiential scale. (KPI-2)


As the Project focuses on the interoperation of two IoT systems (i.e. CAN and AoT), findings and outcomes will focus on how these systems combine to help meaningful decision-making. In other words, the Project fundamentally requires the development of communication standards for these systems in the hopes of adding additional systems.

Replicability, Scalability, and Sustainability:

As the AoT system (v.1) has already been deployed in Chicago and is planned to be deployed in multiple cities worldwide, a deployment of the AoT system (v.2) in Atlanta is already a case study of replicability. Beyond Atlanta, these other sites provide comparisons.
Central to the research is building a scalable and sustainable test case. This means formalizing the types of partnership and technical specifications that can be scalable as the scalability and sustainability extend beyond the technical features of a system. Partnering with the City of Atlanta and GTRI during Phase 1, the Phase 2 deployment focuses exclusively on building capacity for continued growth of the system.

Project Impacts:

The Campus Array Node (CAN) system has been newly developed by GTRI. With considerable testing (i.e. the Project), the CAN system can be a developed into a licensable product.
As the focus of the project is on multi-modal transit with an emphasis on walking, the Project has health and public safety impacts. By giving individuals information that encourages walking rather than waiting, the Project combats sedentary behavior. By giving measurements of pedestrian traffic, the Project aims to impact public safety through informed decision- making about comfort.


Phase I Pilot/Demonstration June 2016:

A pilot focuses on deploying the AoT & CAN systems on the Georgia Tech campus. These two systems will be placed on stationary locations as well as attached to moving vehicles (e.g. campus buses as stand-ins for public transit). The use of these sensors on vehicles will be a primary technical accomplishment of the project. This pilot will test:
• technical feasibility of mobile sensors and data collection
• development of scalable metrics of community involvement
• standards of interoperation of the CAN & AoT systems as IoT
model systems
• establishment of a network of working partnerships for further
deployment and development

Phase II Deployment June 2017:

After the pilot project, the Project will seek to scale the system to a local neighborhood and deploy the mobile sensors on limited public vehicles (potentially municipal vehicles).

Team Information: Team Lead:
Jennifer Clark, [email protected] Margaret Loper, [email protected]

Questions or interested?

We’re here to help and advise.

Contact us