Smart Communities Rising Up with Digital Twin: Transforming the interconnection between travelers, open data and city planners
The recently emerging trend of digital twin technology and high-performance computing is creating a revolutionary paradigm shift in the coming years. For smart city and community mobility applications, the pairing of the virtual and physical world allows analysis of data and monitoring of systems, evaluating different improvement strategies, and planning the future by using simulations. A long-term goal of Smart Community Digital Twin (SCDT) is to create sustainable urban systems that benefit the citizens and societies at large.
Our long-term goal of ASU Trans+AI lab aims to bring together experts from academia, industry, municipalities, and nonprofit organizations from large and small metropolitan areas to develop an Open data hub and Open-source simulation framework for transportation-focused Open-SCDT applications. We hope to deliver rapid prototyping of SCDT and enabling smarter multimodal policy decisions for transforming the livability, sustainability, and resilience of community. A successful SCDT in our project will enable both: (1) integration of a variety of emerging technologies and practitioner expertise from community stakeholders; and (2) integration of large-scale agent-based simulators of urban landscapes at the city and regional scales. Designers and engineers can make use of our integrated models for quick, inexpensive prototyping of new ideas, which further provides potential for creating new forms of citizen engagement by communities, and new approaches to city operations and management by city planners.
Please join us at smartcityplanning.slack.com to volunteer, or provide comments to our ASU Trans+AI team members.
Web portal and open datasets for state-by-state multimodal transportation infrastructure data from OSM
To facilitate a better understanding of multimodal transportation infrastructure in the U.S., our ASU team provides a web-based interface to visualize multimodal transportation networks, state by state, for both rail and highway modes. https://asu-trans-ai-lab.github.io/web/index.html#/ You can select either Bing or OSM as the base map. We also provide open data set of rail and highway networks from #OpenStreetMap, in #GMNS format, at https://github.com/asu-trans-ai-lab/Integrated_modeling_GMNS/tree/main/examples/United_States_network . For system-level planning, detailed information, such as company ownership capacity and commodity demand, is still needed for rail networks along this line.
By simply uploading node.csv and link.csv at https://asu-trans-ai-lab.github.io/index.html#/, you can easily create custom online maps for any GMNS network files. You can also visualize POI.csv, agent.csv and demand.csv from the grid2demand package.
Sample node and link files for the above Arizona State University campus can be found at here. You can contribute your data set for other beautiful university campuses. If you are interested in large-scale network modeling, please fetch the state-by-state transportation network data in the United States, in four different layers (motorway, trunk, primary and secondary roads). The network data are converted from Openstreet map to GMNS format using the OSM2GMNS python package our team develop.
Categories of OpenStreetMap road network: Source: https://wiki.openstreetmap.org/wiki/Map_features
| Motorway | A restricted access major divided highway, normally with 2 or more running lanes plus emergency hard shoulder. Equivalent to the Freeway, Autobahn, etc. |
|---|---|
| Trunk | The most important roads in a country's system that aren't motorways. (Need not necessarily be a divided highway.) |
| Primary | The next most important roads in a country's system. (Often link larger towns.) |
| Secondary | The next most important roads in a country's system. (Often link towns.) |
Our team is devoted to developing enterprise grade open-source tools for transportation modeling, in a broader context of computational transportation science. Please visit our website for integrated Analysis, Modeling and Simulation (AMS) and related FHWA AMS data hub effort.
Using ASU research computing facility, we also create an entire U.S. driving network from OpenStreetMap with 20 million nodes. The multi-modal network of ASU Tempe Campus can be found here (Walk: red; Bike: green)
