Problem: The objective of the AERIS research program is to generate and acquire environmentally-relevant real-time transportation data, and use these data to create actionable information that support and facilitate “green” transportation choices by transportation system users and operators. Our team chose Paramics microsimulation capabilities to support the modeling phase of the project.The expectation of the project was to develop and test the different AERIS application concepts in simulated conditions in order to determine if and what potential benefits could be gained by the use of environmental data from connected vehicles in improving the environmental measures of the simulated area, such as fuel consumption, carbon dioxide, particulates, etc. While the concepts and results of the modeling were designed to represent any location of the US, a representative corridor based model was used representing a typical mainline arterial in southern California. The model had a well-coordinated, fixed time signal system, with the majority of vehicles on the mainline and light side street traffic. This was a suitable choice for many of the signal operations concepts of the AERIS project, such as signal priority and approach profile modeling. The main constraint of the project was that very little previous work had been demonstrated in using real-time environmental data in practice for the improvement of traffic operations. Also, as many of the AERIS concepts were new to the practice, the majority of connected vehicle applications and technology had to be developed from scratch in order to model in the Paramics environment. That being said, theoretical data was available from previous university research on which to build the applications and as a good starting point for the modeling process. This has been, and continues to be an exciting project because we are using the Paramics capabilities to design and implement never before used technology in practice.
Challenges: The model that was used by the AERIS team for the Eco-Signal Operations microsimulation phase of the project was already built and calibrated before the project began in early 2013, so there was no need to use the powerful tools Paramics offers, such as the Estimator program. That being said, the number one reason that Paramics was chosen for this exercise was the Programmer functionality suite application programming interface (API) that can be used to modify or extend the existing capabilities of the program. Since the majority of the technology of the AERIS program does not exist or has been re-imagined, this was absolutely vital to the success of the program. There were two main components that were developed in the Programmer API interface: the individual application components and the environmental monitoring component. While Paramics does provide a useful pollution monitoring plugin, the AERIS goals required a custom made real-time environmental plugin, which would simulate connected vehicle information being communicated at at least once a second to the infrastructure. This presented a problem in that the majority of environmental modeling suites available are all “offline,” meaning that they were database driven, “after the fact” programs. The team made use of the Programmer’s ability to store and access variables in a vehicle’s memory at any requested moment, in real-time. This, combined with other commands in the API, allowed the team to build a novel, real-time version of the US governmental emissions model, called MOVES. The individual AERIS applications also made use of the limitless commands and override commands in the Programmer interface to mimic the needed connected vehicle features. This was helpful, because vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies are not expressly available in most modeling environments. Since Paramics was used in the signal operations applications phase of the project, one of the key pluses of the Programmer API suite was the interface with the signal system. Commands allow the querying, analysing, and override of the signal system in real-time. This feature was absolutely vital to the success of the modeling. Additionally, this information can also be customized on a vehicle-by-vehicle basis and stored in each vehicle’s memory. One last feature of connected vehicles that is better than conventional ITS technologies is that the real-time location of each vehicle is available to every other vehicle, as well as to the infrastructure. Paramics API features allowed us to design novel systems to mimic this technology in simulation very accurately and easily. In addition, one unique application that involves signal timing optimization utilized the command line operation of the Processor application of the suite. The optimization was using a specially built C++ interface, developed in house, that continually called and implemented the command line simulation in a loop to optimize the signal timings and test each of the options in practice. Without this special command line feature, the speed and accuracy of the process would not have been possible.
Conclusion: The model was used to communicate with partners, stakeholders, and the practicing community in a number of ways. The number one way was with the generation of data from the modeling runs. As previously discussed, the Programmer suite MOVES API that we built gave a detailed list of environmental impacts from the real-time simulation, which we built in to actionable reporting, such as cost benefit analyses, charts, and individual person-cost savings statistics. These were the results ultimately desired by the stakeholders, to better understand if there is a benefit in the technology worth pursuing. This data was combined with the mobility results, such as travel time, delay, etc., which Paramics easily produces natively. Some applications of the model for the signal operations phase looked very good in the Modeller suite and could be shown to clients in meetings and made in to videos for public presentations. These included showing the signal timing changing in real-time with priority requests, as well as modifying acceleration and deceleration profiles in real-time for the environmental optimization of approach trajectories. One other feature we took advantage of was the ability to export detailed shapefiles and information to ESRI standards, so that we can import in to ArcGIS. This allowed the team to build heat maps and other visualizations as needed from the simulation results. The AERIS project is ongoing and applications continue to be updated and improved as the team learns more from modeling sensitivity analyses. The results of the modeling are also helping the team to suggest future research to stakeholders and other members of the connected vehicle community which will be implemented in future phases of AERIS research.
Name: Sean Fitzgerel
Location: United States of America (Various)
Company: Booz Allen Hamilton