Smart City: Chattanooga, TN Collaborating and Applying Data to Address the City's Needs

Kevin Comstock, Smart Cities Director, City of Chattanooga, TN
Kevin Comstock, Smart Cities Director, City of Chattanooga, TN

Kevin Comstock, Smart Cities Director, City of Chattanooga, TN

Chattanooga has a long history of public and private partners uniting to tackle our most urgent problems - cleaning up the “dirtiest city in America,” reenergizing our downtown, delivering the nation’s fastest internet. In planning for today’s most pressing issues, we are asking how do we ensure Chattanoogans can move safely and efficiently. How can we preserve our natural resources and be resilient in the face of natural disasters? How can we improve the health and well-being of all Chattanoogans? Recognizing technology and innovation’s unique roles in meeting these challenges, multiple partners from across Chattanooga have formed the Chattanooga Smart Community Collaborative (CSCC). This award-winning group has representatives from local government, The University of Tennessee at Chattanooga (UTC), the Electric Power Board (EPB), Erlanger Health Systems, and The Enterprise Center of Chattanooga all working to identify and bring research, innovation, and economic opportunities to the region.

The “Crown Jewel” of that collaboration is the Martin Luther King Urban Corridor Testbed. The City of Chattanooga and the Center for Urban Informatics and Progress (CUIP) at UTC, designed, implemented, and tested a series of sensors and transportation control technologies on a 1.2-mile urban corridor. The goal is to establish a “sandbox” where the CSCC partners can focus on the key tenets established by the group regarding mobility, energy, and health.

In early 2018, the Smart City Division of the Chattanooga Department of Transportation (CDOT) and UTC established a pilot project utilizing the testbed that identified the first of two objectives for the corridor.

  The public and private partners that have united within the CSCC are harnessing the power of collaboration and cutting edge technology to address issues of mobility, energy, and health 

The first objective is to understand how people move within the urban corridor, a key performance indicator for the city. To support the mobility metric the city previously invested in a closed loop traffic control system in 2011 and equipped the corridor with video sensors to monitor and collect data on vehicular movement during a recent “right-sizing” project. Throughout 2018, CUIP added RADAR, LIDAR, acoustic, air-quality sensors, and CCTV cameras. In the first quarter of this year, the system was turned on and began collecting data. During the initial operational phase of the testbed, more than 230,000 discreet objects were detected by the technology. Cars, trucks, buses, pedestrians, and bicyclist make up those objects giving CUIP and Smart City staff at CDOT tremendous data on the corridor.

The second objective is to understand relational proximity within the corridor. Termed frequently as “near-miss” data, the city is interested in how we can utilize the data provided to a better understanding of how pedestrians, bicyclist, motorist, and transit all interact within the corridor. Those interactions define how the public uses the corridor and provide actionable data on solutions. Our team at CUIP is working on a “vectorial analysis” algorithm to apply over the video feeds from the test bed and our other 72 cameras at signalized intersections across Chattanooga to review potential “near-miss” events and to then apply them to a vulnerable road user safety program. That work, coupled with previous studies, provides tremendous insights into the safety of our network.

One of those previous studies includes a National Science Foundation-funded research initiative where CUIP used weather, traffic, and 911 data to create a predictive model. The executive summary indicates, “This paper investigated the potential correlations between traffic accident occurrence, weather occurrences, and roadway geometrics. When considered individually, the features used in our study reflected as being inconsequential when considering an accident occurrence. However, with the results from our machine learning, reflecting a high accuracy in accident prediction, we concluded that many features are not only correlated with accident occurrence but are also correlated with each other.”

Currently, the Smart City Division is actively working on data exchanges with external programs such as the US Department of Energy Vehicle Technology Office “Digital Twin” program with Oak Ridge (ORNL) and Renewable Energy (NREL) National Laboratories.

CDOT is also utilizing “crowd-sourced” transportation data to support the data gathered through our transportation network. Those data are leading CDOT to decision points towards the region’s federally mandated 2045 Regional Transportation Plan. That plan identifies congested corridors, freight management targets as well as air-quality initiatives for the region. Use of the data quantifies these targets and helps us to evaluate in near real-time the impacts of disruptions or treatments applied.

In Chattanooga, each of these programs is directly linked to actual citizen needs. The public and private partners that have united within the CSCC are harnessing the power of collaboration and cutting edge technology to address issues of mobility, energy, and health for all Chattanoogans.

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