VISSIM and real-world eco-approach and departure comparison
Cite this dataset
Oswald, David; Williams, Nigel; Hao, Peng; Barth, Matthew (2021). VISSIM and real-world eco-approach and departure comparison [Dataset]. Dryad. https://doi.org/10.6086/D1VH5W
In addition to providing safety and mobility benefits, Connected and Automated Vehicles (CAVs) have the potential to reduce fuel consumption and emissions. As new CAV applications are developed, it is valuable to estimate these potential environmental benefits, typically using vehicle activity data and emissions models. To date, most researchers in the U.S. have used the MOVES vehicle emissions model, developed and maintained by the U.S. Environmental Protection Agency (EPA). However, because MOVES uses a binning approach, it is likely underestimating the true energy and emissions savings that occur when CAV applications smooth traffic flow. To illustrate this problem, we measure and model the fuel consumption and CO2 emissions for a real-world CAV application: Eco-Approach and Departure (EAD) at signalized intersections. First, a traffic simulation of the real-world corridor the experiments will be performed on was created. Next, the hardware needed to perform the real-world experiments was installed on the real-world corridor. Then, using the traffic simulation previously mentioned was used to mimic real-world testing of EAD in different traffic conditions. Finally, real-world EAD tests were performed on the real-world corridor where fuel consumption and carbon dioxide emissions was recorded. Real-world measurements are compared to a MOVES-based estimate, as well as to an estimate provided by the physical-based Comprehensive Modal Emissions Model (CMEM). Results show that MOVES consistently underestimates the energy and emissions benefits of the CAV application, primarily since the bin sizes in MOVES are too large to catch the nuances of traffic smoothing. On the other hand, CMEM provided a more accurate energy and emissions estimate, primarily since it uses analytical functions to model emissions and does not suffer from the same binning problem.
The data are output from PTV VISSIM via application programming interfaces (APIs). The files are in .csv format. The contents of each file include vehicle ID, vehicle speed (in mph), MOVES estimate of fuel consumption (in grams), and CMEM estimate of fuel consumption (in grams) on the basis of one simulation time step (1 Hz).
Real-world data files are in .csv format. The contents of each file include vehicle speed (in mph), air/fuel ratio, mass air flow, fuel consumption (grams), vehicle speed from gps (in mph), CMEM estimate of fuel consumption (in grams), and MOVES estimate of fuel consumption (in grams) collected every second (1Hz).