Smart Cities Research - 2019
Smart Cities Innovation Hub: Phase 1, Connected and Autonomous Vehicle Readiness Index
Pls: Charlie Santo, Sabya Mishra, Eddie Jacobs, Mihalis Golias, Lan Wang, Carmen Astorne-Figari
The readiness index will provide 1) a relative measure of how suited a community, city, or region is to CAV operations across multiple factors and operational scenarios, and 2) a measure development methodology in which the factors and operational scenarios can be changed to meet specific and unique community or municipal challenges. A readiness index provides a means of targeting research, planning, development, engineering, and management efforts in an intelligent way, specifically addressing the needs of a community with respect to readiness for CAV technology. Also, in much the same manner as a report card, it provides a readily accessible way for city leaders and the public to understand where their city is with respect to being able to reap the benefits of CAV technology. This could be used as a means of engendering public support for improvement projects. To our knowledge, such an index does not currently exist. As the demand and need for CAV technology grows over the coming years, the development of this index will position both the University of Memphis and the City of Memphis as innovation leaders in CAV technology integration. The CAVRI will serve as a model for readiness indices related to other smart city innovation domains, which will be developed in future phases on the Smart City Innovation Hub's work.
Efficient Crowdsourced Data Collection for Quality of Life in Smart Urban Communities
PIs: Junaid Khan, Eddie Jacobs and Land Wang
In this project, we propose a crowd-sensing approach allowing smart connected devices
and share collected data from urban neighborhoods with a city. More specifically, the city provides sensor packages to citizens who volunteer to install them near their homes. In return, they get financial rewards whenever data is obtained and used by the city. Because collecting data from a large number of devices in close proximity can result in redundant information, we plan to develop a platform where only a number of sensors are dynamically selected as \delegates", e.g., based on their data quality and city's changing needs, to share data with the city. This approach not only reduces the consumption of energy, bandwidth and other scarce resources, but also provides high quality sensory information from the urban neighborhoods. Our research will address the following questions: (i) what are the criteria to select sensor delegates dynamically? (ii) how to fuse data from neighboring sensors? (iii) how to ensure the privacy of citizens? We will develop a novel ranking system for sensor selection that considers past data quality, i.e., timeliness, completeness, and accuracy, as well as relevance to the city's specific data needs. High quality sensors with sufficient computing resources will initiate a self-organization process where they contend to become \delegates" in the neighborhood for a given data collection session.
Decentralized Estimation and Prediction of the State of Traffic in Space and Time
PI: Bonny Banerjee
With increase in urban population all over the world, demand for urban transportation
is on the rise creating congestion, impacting individual productivity and draining
valuable resources. A number of large corporations have been working on leveraging
the Internet of Things/Everything to develop multi-tiered centralized solutions for
smart urban transportation systems to make the lives of citizens and tourists in a
city more enjoyable and productive. A crucial element of any such solution is the
data analytics component that is responsible for analyzing city-wide traffic patterns
in near real-time, and anticipating and minimizing potential issues that could impact
traffic flow or disruptions in service.
The goal of this project is to develop artificial intelligence algorithms that will exploit the data from the myriad of data sources, such as sensors (e.g. radars, loop detectors), smartphones and humans, to discover the issues causing abnormal traffic situations. Once the causes are learned, abnormal situations can be predicted, and the concerned authorities as well as individual travelers can be intimated. The algorithms will eventually automate the monitoring process; they will monitor the state of traffic from all available sources round-the-clock with minimal manpower/supervision and cost/maintenance. The challenge in this project is to develop computationally efficient algorithms for open-ended large-scale real-world Big Data from multiple asynchronous static and dynamic sources, some of which might turn on and off at different times of the day. Success in this endeavor will significantly benefit the society at large and, in particular, the City of Memphis by improving emergency response, fostering economic development and encouraging tourism.
Identification of stationary and wireless charging stations for battery operated electric vehicles in smart cities
PI: Sabya Mishra
This research proposes to develop an enhanced planning framework to decide the optimal
charging infrastructure in the era of autonomous and alternative-fuel vehicles. The project's goal will be
accomplished through (i) modeling traveler behavior in the presence of connected autonomous vehicles
(CAVs) and alternative powertrains (gasoline and electric), (ii) developing network equilibrium with mixed vehicles, (iii) modeling and high performance simulation of transportation systems. The project outcomes will enable planners and policymakers in making informed decisions and for devising plans and policies that is not only optimal from road network perspective but also from the perspective of power grids, transmission losses and energy efficiency. If successful, implementation of the project in a region with one million population is expected to reduce the overall energy consumption by more than 10% with an estimated cost savings of $30 billion, assuming an average gasoline price of $2 and electricity cost of 25 cents per unit. Additional benefits include spatial and temporal estimation of power requirements, thereby reducing the chances of power failures, emission reduction, improved air quality and health benefits.
Learning from Informal Neighborhood Networks to Inform Intermediate Smart Transportation Interventions
PIs: Charlie Santo, Sabya Mishra, Eddie Jacobs, Mihalis Golias, Lan Wang, Carmen Astorne-Figari, Andrew Guthrie
This research aims to leverage complex social network structures and novel incentive mechanisms to enable peer-to-peer ridesharing services. These services will not only enhance the mobility options of the transportation-disadvantaged populations, but reduce the environmental and financial burdens of transportation. Through understanding and utilizing the relationship between social connectivity and ridesharing, our approach is fundamentally different from traditional carpooling approaches and will address the low utilization of existing carpooling programs. These solutions will be developed by a bottom-up process that learns from the travel behavior of low income and transit dependent residents. Given the lack of viable transit option, many carless low-income residents have found other ways to solve their transportation challenges organically, often through neighborhood-based social networks. Current census data show that even among households that do not own a vehicle, 20 percent drive alone to work—meaning they find a private car to borrow or rent—and another 12 percent commute via carpool. Community-engaged research will reveal alternative informal "organic" approaches to getting around are in use. The research team will study the formation and impact of organic networks using cell phone app-based survey/activity monitoring, develop a simulation of this process and predict the effects of 'seeding' other organic networks.