Digital Twin solutions for Mobility and Safety
Digital Twins Research Directions
We focus on five research directions to be better characterized
by the insights, design, implementation and deployment of digital twin solutions.

Integrating real-time data with digital simulations, the digital twin in mobility enhances urban transportation networks, optimizing traffic flow and public transit efficiency. It provides a dynamic model to test and implement smart city solutions for sustainable and congestion-reduced urban living.


Digital twin with advanced algorithms analyze patterns from countless sensors across the city, enabling emergency services to respond proactively to incidents before they escalate.


A digital twin in energy management revolutionizes the grid by simulating energy consumption and distribution, leading to more resilient and efficient power systems. It allows for the seamless integration of renewable energy sources, predicting fluctuations and balancing supply with demand in real-time.


A digital twin facilitates advanced transportation planning by simulating the effects of extreme weather, enabling cities to optimize routes and schedules for improved safety and reliability. It serves as a crucial tool for adaptive traffic management, ensuring that transportation infrastructure remains resilient in the face of climate-induced challenges.


The digital twin in equity ensures fair access to transportation resources, simulating various socio-economic scenarios to identify and bridge gaps in digital inclusion. It aids policymakers in crafting targeted interventions that promote equal opportunities in education, healthcare, and economic participation within the cyber-physical framework.


Digital twin technology in transportation systems enables the optimization of traffic flow and public transit, leveraging real-time data and simulations to minimize emissions and energy use. This fosters a sustainable and efficient urban mobility framework, reducing the environmental footprint of city transportation networks.

Events & News
January 2022
December 2024
Jan 2022
Dec 2024
Understand Digital Twins for Transportation Systems Workshop
We held a Digital Twins workshop at the 103rd TRB Annual Meeting, Washington D.C. We invited 10 speakers across academic, industry and agency to present and discuss their work and opinion. A total of 140 people attended the workshop.
Simulating the Future of Automated Driving: Unveiling Human Factors Issues and Providing HCI Insights
Thursday, March 14, 2024 | 2:00pm - 3:00pm ET | In-person
RH 460 C2SMART Lab, 6 MetroTech Center, Brooklyn
Dr. Andreas Riener discussed the intersection of human factors and computer interaction in the evaluation of automated driving systems. This included simulations and the study of human-vehicle cooperation, emphasizing user experience.
Driving Simulators and What You Should Know Before Using Them
Hosted by Professor Linda Boyle
Tuesday, March 12, 2024 | 4:00pm - 5:00pm ET | In-person
RH 460 C2SMART Lab, 6 MetroTech Center, Brooklyn
Presented by Chantal Himmels, a Ph.D. candidate with BMW. The talk covered the role of driving simulators in automotive research, their validity, and their application in drawing real-world inferences.
Research Seminar Series: AI for Urban Transportation Digital Twins with Dr. Sharon Di, Columbia University
Friday, February 2, 2024 at 12:00pm - 1:00pm
370 Jay St, Room 1201
Brooklyn, NY 11201
Dr. Xuan (Sharon) Di discussed enhancing transportation digital twins through the 'brain'—utilizing AI to process sensor data for traffic optimization. She outlined applying physics-informed deep learning for traffic analysis and mean field games to model the interaction of autonomous and human-driven vehicles.
Leveraging Passively-collected Mobility Data in Generating Spatially-heterogeneous Synthetic Population
Friday, January 5, 2024 | 1:00pm - 2:00pm ET | In-person
RH 460 C2SMART Lab, 6 MetroTech Center, Brooklyn
Prateek Bansal, Assistant Professor, National University of Singapore. Conventional population synthesis methods rely on household travel survey (HTS) data. However, the synthesized population suffers from a low spatial heterogeneity issue due to high data aggregation and low sampling rates of HTS data. Passively collected (PC) data from smartphone devices or transit smart cards have the potential to overcome the limitations of HTS data, thanks to the continuous collection of mobility patterns at a high spatial resolution for a large proportion of the population. However, the mismatched spatial resolution, sampling rate, and attribute information make the fusion of HTS and PC data challenging. This study presents a novel cluster-based data fusion method that exploits the benefits of both HTS and PC data to generate a synthetic population with high spatial heterogeneity. As the number of the value combinations for spatial attributes (e.g., home and work locations) in PC data is much larger than that in HTS data, clustering is adopted to deal with the high-dimensionality issue and link spatial attributes in the two data sources. The data fusion problem is then formulated as tractable multiple low-dimensional optimization subproblems. The properties of the proposed method are analytically derived. The application of the proposed approach is demonstrated using the HTS and LTE/5G cellular signaling data from Seoul, South Korea.
NY Statewide Behavioral Equity Impact Decision Support Tool with Replica
Thursday, September 7, 2023| 12:00pm - 1:00pm ET | Webinar
NYU's Xiyuan Ren, 4th year PHD candidate, and Prof. Joseph Chow.

One of the enduring challenges in statewide transportation planning is that consistent population travel data remains scarce, particularly for underserved and rural communities with inequity issues. This is changing with the availability of large-scale ICT data. A one-year project was initiated to develop a behavioral equity impact decision support tool based on NY statewide synthetic population data provided by Replica Inc. First, a NY statewide model choice model is developed to deterministically fit heterogeneous coefficients for trips along each census block-group OD pair, called group-level agent-based mixed (GLAM) logit. Considerations were made for four population segments, six trip modes, and twelve attributes. Second, a decision support tool for statewide mobility service region design was proposed. The tool is based on an assortment optimization problem with agent-specific coefficients and linear constraints, which can be efficiently solved through linear or quadratic programming (depending on variant). The decision support tool is applied to optimize service regions with one of the three objectives: (1) maximizing the total revenue; (2) maximizing the total change of consumer surplus; (3) minimizing the disparity between disadvantaged and non-disadvantaged communities.
Digital Twin Technology: Interactions Between Transportation, Civil Infrastructure Systems: Phase 2
Monday, August 14, 2023| 12:00pm - 1:00pm ET | Webinar
Presented by UTEP Professors Dr. Kelvin Cheu and Dr. Ruimin Ke, and PHD Candidate Talha Azfar

A digital twin (DT) is a realistic digital model of a physical system or process, with sensors that measure real-world parameters and a simulation engine that replicates the behavior of the system or process. Realistic digital geographical models of real-world locations provide baseline information for digital twin applications. This project explores DT as a tool for the management of civil infrastructure. The real physical systems selected for this concept were a road network of the roundabout at a parking garage, a pedestrian bridge, and an interdisciplinary research building on the University of Texas, El Paso campus. Using the campus as a living lab, we developed DT models based on transportation networks, structural modeling, and LiDAR scans. The transportation network and 3D model of the campus were combined for traffic simulation and real-time sensing at a roundabout, while the digital model of a pedestrian bridge was made for structural simulations with provisions for strain and tilt sensors. Vehicles were detected and tracked from video on an edge computing device and visualized in the DT environment. DT models were analyzed considering various scenarios, demonstrating practical applications for real-world infrastructure. This presentation focuses on providing valuable insights into DT development, implementation challenges, and potential applications for civil infrastructure projects.
One-to-Many Simulator Interface with Virtual Test Bed for Equitable Tech Transfer
Monday, July 17, 2023| 12:00pm - 1:00pm ET | Webinar
Presented by NYU's Hai Yang and University of Washington's Professor Jeff Ban

After five years of R&D, researchers have a number of independent simulation tools to evaluate different algorithms. A broad API was developed to handle interfacing any simulation with a multi-agent demand simulator. This was tested on the existing MATSim-NYC (which will be enhanced to include freight and parcel delivery activities) and aBEAM implementation, BEAM-NYC, for three use cases in electric transit, freight, and traffic. Each of these use cases considers equity impacts on different population segments (by income level, having disabilities, age level). The project jointly conducts some of the case studies in NYC and Seattle, enabling deeper insights of evaluated cases and promoting tech transfer and collaborations to broader communities (including agencies, the industry, and the public).
Summer Series: Route Choice, Spatiotemporal Behavior: Modeling Travel Behavior, Public Transit Data
Thursday, June 15, 2023| 3:00pm - 4:00pm ET | Webinar
Presented by Professor Marcela A. Munizaga (University of Chile) and the Transportation Research Board Subcommittee AEP30(2)/AEP40 on Route Choice and Spatio-Temporal Behavior

Data automatically generated by the technological devices used in the operation of public transport systems provide a unique opportunity to observe travelers' behavior. These big databases allow monitoring the system from different perspectives, such as bus operations, level of service, demand, across time and space. We have worked on all of those aspects. This presentation will focus on work devoted to improving the understanding of route choice behavior among public transport passengers. By utilizing revealed preference data from passive transportation sources, we developed methodologies to model the heterogeneity in passengers' route choices, the process of generating consideration sets in intermodal route choices, and the passengers' learning process when facing new route alternatives. These methodologies were applied to the case of Santiago, Chile, where we found evidence supporting the need to adapt traditional modeling approaches, commonly used with traditional transportation data, to a modeling context with massive and disaggregated data derived from widely applied transportation technologies.
Quantifying and Visualizing City Truck Route Network Efficiency Using a Virtual Testbed
Thursday, September 7, 2022| 12:00pm - 1:00pm ET | Webinar
Presented by: Haggai Davis, III, 3rd year PHD candidate at NYU and Joseph Chow, Associate Professor at NYU and Deputy Director of C2SMART

Due to its complexities, urban freight modeling requires robust data inputs that must be combined from numerous sources which can often limit the accuracy, power, or transferability of a model. Even with proper inputs, new techniques must be developed to make use of all the information that the data contain while still being feasible in their implementation. This presentation addresses the challenges in turn by estimating FTG from public data sources, inputting the FTG into a tour creation model, and then solving an entropy maximization problem with an iterative balancing algorithm to load flows onto the tours. New York City is then used as an example to illustrate and validate the application of these techniques.
Simulation-Based Safety Evaluation Framework for CV Applications for Safety And Operational Measures
Wednesday, November 9, 2022| 1:00pm - 2:00pm ET | Webinar
Presented by Prof. Kaan Ozbay, NYU, C2SMART

Proper calibration process is key for traffic safety evaluations using simulation models. Allowing for a with and without comparison under controlled environment that is not directly testable in the field, microsimulation-based approach has drawn considerable attention for the performance evaluation of emerging technologies, including connected vehicle (CV) safety applications. Different from the traditional approaches to evaluate mobility impacts, safety evaluations of such applications demand the simulation models to be well calibrated to match real-world safety conditions. This seminar will present a novel calibration framework which combines traffic conflict techniques and multi-objective stochastic optimization to calibrate the operational and safety measures simultaneously. The conflict distribution of different severity levels categorized by time-to-collision (TTC) is applied as the safety performance measure. Simultaneous perturbation stochastic approximation (SPSA) algorithm, which can efficiently approximate the gradient of the multi-objective stochastic loss function, is used for model parameters optimization that minimizes the total simulation error of both operational and safety performance measures. A case study will be demonstrated by calibrating a microscopic simulation model to evaluate CV safety applications as a part of the NYC Connected Vehicle Pilot Deployment Program.
New Approaches and Paradigms in Traffic Flow Modeling and Control
Wednesday, August 24, 2022| 4:00pm - 5:00pm ET | Webinar
Presented by Dr. Wen-Long Jin, UC Irvine

In this talk, Dr. Wen-Long Jin will present some of his recent results on new approaches and paradigms in traffic flow modeling and control. He will first discuss traffic flow models in three types of spaces: (1) provably safe driving models for both human-driven and autonomous vehicles in the absolute space on a road, (2) bathtub models for network trip flows in a relative space with respect to individual travelers’ remaining trip distances, and (3) day-to-day traffic flow models for departure time choice in an economic space with respect to the scheduling cost. Then he will present two studies on traffic system operations and control: (1) dynamic pricing schemes for high-occupancy-toll lanes with a single or multiple bottlenecks; and (2) fleet-size management for shared mobility systems with for-hire vehicles.
Virtual Reality and simulation as a tool to investigate the safety of future mobility scenarios
Monday, August 15, 2022| 4:00pm - 5:00pm ET | Seminar
Presented by Dr. Carmelo D’Agostino, Lund University (Sweden)

Traffic accidents are among the leading causes of death for people aged 5–35 worldwide, causing transport externalities and thus unsustainability. At the same time, the introduction of almost fully connected and automated vehicles (CAVs) (levels 3–4 of SAE) is already a reality. Although CAVs go in the direction of smart mobility, their sustainability is still questionable because their deployment in open traffic introduces unexplored risks. Indeed, while technological progress is rapidly being pursued, there remain significant issues related to the development and integration of CAVs with physical and digital infrastructure and to their user acceptance on shared roads. The main reason is a general perception that they are not safe and thus may introduce inequality. In this context, neither the actual accident-based nor proactive methods for road safety analysis can be applied when CAVs interact with conventional users. This is primarily due to the lack of knowledge about the influence of the digital and physical infrastructure in the interactions among vehicles in mixed traffic conditions. In this framework, the use of simulation and virtual reality, combined with validation on real world scale, represents the only approach to provide the basis for new computational methods for infrastructure safety assessment in future mobility scenarios based on a rigorous scientific approach. The use of simulation at different levels combined with new Surrogate Measures of Safety (SMoS) can address the problem of the safety evaluation of the interaction between conventional vehicles and CAVs. The seminar will present how virtual reality and simulation are being used as a tool to replace naturalistic observations in the real-world and their pros and cons, in three different research projects on CAVs safety among which is a European Research Council Grant.
Developing a Multiscale Vehicle-traffic-demand (VTD) Simulation Platform
Presented by UW’s Prof Jeff Ban and Ohay Angra

In this seminar, we will talk about the motivation, design framework, and initial implementation of a multi-scale transportation simulation platform. The platform simulates the movements of connected/automated vehicles (Unity), traffic flow dynamics (SUMO), and demand generation on a large-scale transportation network (MATSim). We focus on the SUMO-MATSim development details and the current challenges of integrating the different simulation tools. We will also mention how the multi-scale simulation platform may be used for conducting research for emerging technologies and systems in transportation.
Joint Traffic Analysis and Modeling Workshop and Mid-Year Meeting
TRB Traffic Simulation Committee is joining with the Highway Capacity and Quality of Service Committee to host a “Joint Traffic Analysis and Modeling Workshop and Mid-Year Meeting” at Raleigh, North Carolina from August 6th thru August 8th.

The Workshop will be on the first day, with the goal to find synergistic ground for traffic analysis and modeling using the Highway Capacity Manual (HCM) and Simulation. How can HCM and Simulation methods, tools, and research be right-sized for holistic consideration, and practical application to solve today's transportation problems and prepare us for the challenges of tomorrow.

Please checkout the call for abstract for presentation at the Workshop, the deadline is Apr-30-2024.

The Workshop will be followed by individual committee mid-year meetings.

Please check out the Event Webpage for Agenda, to submit your abstract, and other details
Ongoing and Complete Projects
Click each project to visit it's website
GPU-Accelerated Simulation for Reducing Congestion
Developing AI-driven simulators for realistic and cost-effective congestion reduction evaluation and strategy testing.
This project addresses a crucial challenge in congestion reduction schemes: the validation of proposals with realistic driver behavior models. Leveraging advancements in artificial intelligence and simulator design, it proposes a two-stage approach to develop an efficient simulator capable of generating complex models of human behavior at intersections through Goal-Conditioned Multi-Agent Reinforcement Learning (GC-MARL) and imitation learning. The goal is to create an open-source platform for evaluating congestion mitigation strategies under realistic traffic conditions, allowing for diverse road layouts, speed limits, and traffic control settings. This research aims to enhance the study of transportation system safety and congestion by integrating realistic agent models into existing simulators like SUMO or AIMSUN, enabling rapid evaluation of safety and congestion interventions and significantly reducing the cost of applying reinforcement learning for congestion reduction.
Micro-mobility Testbed
Enhance urban cyclist safety by mapping high-risk areas in NYC, using connected bikes to inform safety improvements.
This project focuses on enhancing cyclist safety in urban settings by identifying and mapping high-risk areas across New York City. Equipping traditional bicycles with portable cameras and ultrasonic sensors transforms them into connected bikes capable of capturing interactions between cyclists and vehicles. This data is then used to create a dashboard highlighting high-risk zones, enabling policymakers and urban planners to target rider safety improvements effectively. Additionally, the project can be extended into a micromobility testbed for exploring potential digital twin applications, offering insights into safer urban mobility solutions.
ATEAM Traffic Incident Testbed
Create an AI testbed for traffic incident management, integrating data for predictive analysis and potential digital twin applications.
This project establishes a testbed for managing traffic incidents, leveraging AI-driven approaches for enhanced efficiency. It integrates diverse data sources and types through advanced fusion technologies, enabling the estimation and impact prediction of different traffic incidents on mobility systems. Additionally, the platform is designed with the capability to integrate with dynamic traffic assignment models or macroscopic simulation models (e.g., MATSim-NYC), laying the groundwork for macroscopic level digital twin applications. This connection aims to improve incident and mobility management, showcasing the testbed's potential in transforming traffic systems analysis and response strategies.
Traffic Digital Twin with FDNY
Partner with FDNY to create a traffic digital twin using SUMO and AI to improve emergency vehicle response times in urban areas.
This project creates a traffic digital twin (TDT) with the FDNY to tackle declining emergency vehicle response times in congested urban settings. Leveraging the Simulation of Urban Mobility (SUMO) and AI, it simulates traffic to evaluate interventions and improve decision-making. The project involves calibrating traffic simulations for Harlem, using AI to predict traffic states from real data, analyzing emergency and non-emergency vehicle interactions, and testing solutions in a virtual environment. This innovative approach aims to optimize emergency responses through cost-effective, simulated traffic management strategies.
Multiscale Urban Traffic Control for Automated Cities
This research aims to enhance congestion reduction and safety, measuring performance across mobility, sustainability, and safety metrics, and assessing impacts on various user groups for broader US deployment.
The UW team has been developing an integrative multiscale modeling and control framework for urban traffic control, partially supported by previous C2SMART UTC funds. Previous research, however, focused only on vehicular traffic. The proposed research will be based on the existing work, which will be expanded to consider multimodal traffic (especially non-motorized road users such as pedestrians and bicyclists) and explore the opportunities to test/validate the models using real-world connected and automated vehicle (CAV) testbeds.
BQE Smart Urban Roadway
A-WIM testbed enhances transportation accuracy and safety through advanced sensors and digital twin technology.
A cutting-edge A-WIM system testbed in collaboration with local transportation authorities, enhancing the precision of weight measurements across transportation networks. By integrating environmental considerations and employing advanced sensors, the project ensured the system's accuracy and readiness for autonomous enforcement applications. Further innovation saw the integration of weighing sensors with thermal visual recorders, streamlining data collection and facilitating autonomous enforcement. The BQE testbed can emerge as a versatile digital twin platform, advancing interactions with vehicle and road factors such as optimizing traffic management and improving road safety. This fusion of real infrastructure with virtual modeling has the potential to elevate operational efficiency and sustainability, marking an improvement in the smart infrastructure system.
FloodNet Digital Twin
FloodNet NYC implements sensors for enhanced flood management
This project focuses on implementing the FloodNet monitoring system in NYC to enhance local flood monitoring and risk management. By deploying sensors strategically, the aim is to provide real-time flood warnings and valuable data for mitigating risks, particularly to safeguard vulnerable communities. The FloodNet system enables critical life-saving actions pre- and post-flood, including optimizing recovery efforts based on risk assessments. Additionally, the project equips the NYC Mayor's Office and local communities with tools to evaluate the effectiveness of sensor networks, aligning with the city's equity priorities. A digital twin is developed for continuous updates on environment factors such as flood risk and impact assessments, improving the city's flood response and mitigation strategies. This initiative also contributes to broader research goals, using the digital twin to create detailed risk models and inform studies on floodwater contaminants through targeted site visits and sample collection.
MATSim Agent-based Simulation
MATSim-NYC offers rapid evaluation of transportation technologies and policies through dynamic, multi-agent simulation.
C2SMART developed a multi-agent simulation test bed, dubbed MATSim-NYC, which is intended to be the first of a “Network of Living Labs” virtual test beds for evaluating emerging transportation technologies and policies. Unlike those primary transportation planning tools, MATSim-NYC and the rest of the Network of Living Labs is developed with the intent to be a quick-response benchmarking tool for different emerging technologies or policies. If a public agency wanted to test certain transit alignments, different congestion pricing schemes, service regions for different mobility options, this tool can provide some consistent assessment of its impact on the city. The key strengths of this tool is the combination of dynamic traffic and heterogeneous activity scheduling behavior of the population through a multi-agent simulation. This means that the tool can capture the trade-offs between traffic congestion by time of day with the route and departure time decisions of its varied travelers at a citywide level.
NYC Connected Vehicle Testbed
Testbed for autonomous vehicles enhances safety via V2V/V2I studies and aids visually impaired pedestrians.
The project has created a testbed for autonomous and connected vehicles, focusing on enhancing safety by examining the interaction between human and vehicle factors. It aims to reduce accidents through detailed studies on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. A key aspect is supporting visually impaired pedestrians with Personal Information Devices (PID) that provide traffic and intersection data.
VR Hardware-in-the-loop Testbed
Integrate real-time traffic data with VR for urban safety.
This project enhances the VR environment by incorporating hardware-in-the-loop (HIL) to simulate traffic data from real-time HIL streams. It tackles integrating real-world sensor warnings into the VR space, requiring innovative HIL interface design. The research focuses on how work zone personnel respond to these mixed-reality warnings and evaluates the effectiveness of wearable sensors and VR in real-time safety scenarios. By examining warning response times and key influencing factors, the project sets the stage for exploring digital twin applications, particularly in understanding the dynamic interactions between humans, vehicles, and road infrastructure, aiming to boost urban mobility safety and efficiency.
Digital Campus in Texas
Evaluate DT technology's role in integrating transportation with civil infrastructure using UTEP campus as a model.
This research aims to evaluate the efficacy of Digital Twin (DT) technology in understanding the interplay between transportation and other civil infrastructure systems, using the UTEP campus as a living laboratory. A DT model of the entire campus will be developed, with varying levels of complexity, focusing initially on a specific building and the transportation network. The project includes creating a baseline digital shadow for selected infrastructure, identifying and augmenting existing campus data sources with synthetic data to simulate the DT environment. Key activities include summarizing relevant literature on DT in transportation, assessing how construction project schedules affect surrounding transportation infrastructure, and developing visualizations to analyze these impacts.
Digital Campus in Texas - Phase II
Continue to enhance UTEP campus infrastructure interaction and integration.
This research aims to assess Digital Twin (DT) technology's effectiveness in understanding and enhancing the interaction between transportation and civil infrastructure systems, using the UTEP campus as a case study. The project involves creating a Smart Living Lab (SLL) on campus, focusing on a pedestrian bridge, a parking garage or signalized intersection, and a classroom, equipped with sensors for real-time data collection and simulation. The objectives include reviewing DT literature in transportation and civil infrastructure, collecting LiDAR data for digital modeling, identifying key infrastructure sites for study, developing data collection and communication methods, creating digital replicas for simulation, and analyzing the operations and interactions between various infrastructures. The findings will be shared publicly and integrated into civil engineering education at UTEP, exposing students to emerging technologies like LiDAR, BIM modeling, and traffic simulation, thereby fostering a better understanding of campus operations and contributing to the field of smart city design.
Connected Automated Transportation Simulation Platform
Develop a multiscale vehicle-traffic-demand (VTD) simulation platform for connected and automated transportation systems (CATS), addressing the lack of a comprehensive tool for testing traffic management strategies and understanding traffic dynamics at different scales.
The development of the multiscale VTD will leverage this existing platform, which will be extended to integrate Multiagent Transport Simulation (MATSim) to simulate large-scale traffic, human activities and travel demand generation processes. In addition to the development of the multiscale VTD simulation platform, we will also propose several research tasks to utilize the platform to better understand traffic flow dynamics. This will include using data-driven and AI methods to test whether the Macroscopic Fundamental Diagram (MFD) exists for homogeneous regions of the simulated network, and how to understand and better approximate complex traffic dynamics such as dynamic path travel times and/or the queuing process of a given transportation corridor.
Equitable Tech Transfer Simulator
This project develops an API to integrate simulation tools for urban transit, focusing on equitable solutions across electric transit, freight, and traffic. Utilizing MATSim-NYC and BEAM-NYC, it evaluates equity impacts on diverse populations in NYC and Seattle.
This project, after five years of R&D, aims to create a broad API that integrates various independent simulation tools for evaluating algorithms in urban transit systems, focusing on electric transit, freight, and traffic. It leverages MATSim-NYC, enhanced to include freight and parcel delivery, and BEAM-NYC for simulations in NYC and Seattle, emphasizing equity impacts across different population segments. The API will enable public agencies to apply MATSim models with local simulators for scenarios like electric bus scheduling and urban delivery, aiming for measurable social and equity-related benefits. This facilitates tech transfer and collaborations among agencies, industry, and the public, promoting cost-effective and equitable urban transportation solutions.
Digital Twin Project Locations
Completed project
Ongoing project