The main objective of this project is to develop a large-scale, open-source, agent-based model of the study are in NYC using MATSim, an activity-based, extendable, multi-agent simulation framework implemented in Java, and calibrate it based on public sector data from the New York metropolitan area. These data include household travel survey and BPM update data from NYMTC, LION (Linear Integrated Ordered Network) GIS data from NYCDCP, TIMS (Traffic Information Management System) and the MIM (Midtown in Motion) real time data from NYCDOT, and transit schedules from GTFS (General Transit Feed Specification) data from MTA NYCT (Metropolitan Transportation Authority New York City Transit). The researchers will also incorporate other emerging data sources like NYC TLC taxi data, social media data from different sources and other emerging partner companies that collect probe vehicle data at an unprecedented rate.
This data-driven approach to validate and calibrate the proposed agent-based model will be one of the first attempts to develop a highly dynamic simulation tool that incorporates big data into microscopic simulation.
In addition, the research team will select one sub-area in lower Manhattan and downtown Brooklyn to develop using an open-source microscopic traffic simulation framework (i.e., SUMO (Simulation of Urban MObility). SUMO allows microscopic modeling of intermodal traffic systems including road vehicles, public transport and pedestrians.
The Need for a Novel Open-Source Transportation Model
Transportation modeling requires substantial transportation data and time-consuming effort for operation and transportation management and policy decision analysis. Large-scale transportation model development is based on various transportation data include traffic volume, bicycle volume, pedestrian volume, signal timings plan, geometry, crash data, travel times and speeds. More importantly, emerging transportation alternatives and technologies, such as connected and autonomous vehicles, electric vehicles, and ride sharing, cannot be readily modeled using existing commercial software tools. In fact, the unprecedented growth of the transportation sector introduces new challenges that make it infeasible to depend on proprietary simulation tools.
Thus, there is a need for an open-source large-scale microscopic transportation model that will cover all New York City (NYC) area, and will eventually be as large as the NYMTC (New York Metropolitan Transportation Council) New York Best Practice Model. Although NYMTC’s NYBPM predicts changes in future travel patterns in response to changes in the demographic profiles and transportation systems in the region, it is limitated in not only modeling daily dynamic recurring and non-recurring congestion and operation, but also developing more detailed transportation control and aiding in planning policy decisions.
In addition, databases currently available in the New York City public sector have not coherently provided dynamic summary and information for a practical transportation planning, control and policy application. Therefore, it is critical to explore additional city-wide large-scale transportation activity models using easily accessible open-source data and programs to support time-sensitive planning, operational control and policy implementation in NYC.
Building on the development of the testbed in previous years, this project will further enhance its applicability and aim to transfer knowledge for using the testbed to local agencies and other members of the C2SMART consortium. The objectives for this phase include:
- Use of direct extensions using the base model, such as automated and connected taxis and dockless bikeshare. Different deployment scenarios will be evaluated to provide decision support capabilities to transportation agencies
- Develop a new integrated web-based data analytics toolbox to allow researchers to analyze extensive output of MATSim
- Modification of traffic flow model in MATSim to improve consistency with SUMO and connected vehicle technology in a new model instance
- Test and implement multimodal travel capability using the R5 routing engine adopted by BEAM for a new model instance
- Publish a web interface for the virtual testbed
- Open a beta testing period and invite participants from local agencies to evaluate and provide feedback
- Host webinars to share lessons learned in procedures for developing MATSim models and integrating with SUMO with students and PIs at consortium member universities, which will support the expansion of the testbed to other cities
Director of the C2SMART Center
Kaan Ozbay is a Co-Principal Investigator on this project.
Deputy Director of the C2SMART center
Joseph Chow is a Principal Investigator on this project.