One of the transportation objectives within many Smart Cities initiatives is the use of connected, intelligent transportation technologies to improve transportation safety and mobility, and reduce environmental impacts, resulting in more sustainable transportation systems. However, the challenges and expense of in-field measurement of many performance metrics (e.g.,CO2 emissions from each vehicle), in real-world roadway environments limits the ability of purely field data based studies to determine the benefits and impacts of such technology in real-world traffic scenarios. These limitations highlight the need to build data-driven models, enabling performance metric estimation based on real-time field data, while still leveraging a modeling environment. Thus, the proposed research advances the ongoing development of a real-time, data-driven, transportation simulation tool for a connected infrastructure environment, capable of estimating two environmental performance measures: energy consumption and CO2 emissions.
The development of the real-time, connected, simulation consists of three primary components: 1) development and implementation of an architecture for the collection of dynamic real-time traffic data such as signal phasing and timing (SPaT) and aggregate volume count data; 2) development of a real-time traffic simulation model driven by the connected data; and 3) estimation of energy and environmental performance measures using the developed real-time simulation model. The proposed research focuses on the last two components, leveraging a case study on the North Avenue Smart Corridor in Atlanta, Georgia, which is equipped with connected infrastructure technologies. As part of a pilot study sponsored by City of Atlanta, an initial real-time data-driven simulation model has been developed for 2.3 miles of the North Ave corridor that includes 15 signalized intersections. The simulation integrates real-time SPaT and aggregate vehicle count data sent from the connected technologies embedded in the North Avenue Smart Corridor. The developed architecture then utilizes simulated vehicle trajectories as input for dynamically estimating environmental performance measures.
The current research proposal is focused on verifying and calibrating the traffic simulation model using volume counts, Bluetooth travel time, and other data, as well as understanding the sensitivity of the model to the variability in the streaming data as well as potential missing or inaccurate data. Additional research will entail assessing a reliability metric for the environmental performance measures –energy consumption and CO2 emissions. The developed real-time, connected infrastructure, data-driven simulation tool is expected to aid decision makers with better insights of environmental impact of the connected technologies.