We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its “eyes,” which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its “brain,” the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.

This paper introduces PAVE (Pedestrian Awareness Via Edge analytics), a scalable real-time video analytics system that uses street cameras to enhance pedestrian safety while preserving their privacy. PAVE processes live camera streams on an edge server to track pedestrians and vehicles in real-time, predict vehicles' trajectories, and identify danger zones where pedestrians are present. The coordinates of these zones are sent to pedestrians' mobile devices via a custom iOS app, which locally determines if they are at risk without sharing any data with the edge server, hence preserving privacy. Moreover, anonymized metadata, including real-time location and speed/direction of pedestrians and vehicles, are visualized on a public map. PAVE's effectiveness was validated through deployment on the NSF COSMOS testbed, processing live video from cameras in diverse urban environments. Live field tests show that PAVE can alert at-risk pedestrians ~0.9 s before a vehicle reaches them. Through extensive profiling, we show that optimizing memory/compute configuration per pipeline stage can reduce latency by up to 10× compared to the default operating system configurations.

We showcase the workflow of PAVE (Pedestrian Awareness Via Edge analytics), a scalable system for real-time video analytics that leverages street cameras to improve pedestrians' safety while maintaining their privacy. PAVE distributes computation across edge servers and end-user mobile devices. Cameras' live streams are processed at the edge to forecast vehicles' trajectories and detect danger zones. Pedestrians' mobile devices then locally determine if the user is inside a danger zone and trigger timely alerts via a custom iOS app. In addition, anonymized metadata, such as pedestrian and vehicle positions, speeds, and directions, are aggregated and displayed on a public map for broader situational awareness. We evaluated PAVE's performance through implementation on the NSF COSMOS testbed's edge server while processing real-time video stream from cameras in diverse urban environments. Live field tests at an intersection in New York City show that PAVE can alert at-risk pedestrians about 0.9 s before a vehicle reaches them. With low-latency cameras, this lead time extends to around 1.6 s which is within the 1–2 s window pedestrians typically need to react.

Recent advances in Visual Language Models (VLMs) have significantly enhanced video analytics. VLMs capture complex visual and textual connections. While Convolutional Neural Networks (CNNs) excel in spatial pattern recognition, VLMs provide a global context, making them ideal for tasks like complex incidents and anomaly detection. However, VLMs are much more computationally intensive, posing challenges for large-scale and real-time applications. This paper introduces EdgeCloudAI, a scalable system integrating VLMs and CNNs through edge-cloud computing. Edge-CloudAI performs initial video processing (e.g., CNN) on edge devices and offloads deeper analysis (e.g., VLM) to the cloud, optimizing resource use and reducing latency. We have deployed EdgeCloudAI on the NSF COSMOS testbed in NYC. In this demo, we will demonstrate EdgeCloudAI's performance in detecting user-defined incidents in real-time.

Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS’s inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, many outdoor environments are already instrumented with hardware such as street cameras. In this work, we explore the idea of repurposing existing street cameras for outdoor navigation. Our community-driven approach considers both technical and sociotechnical concerns through engagements with various stakeholders: BLV users, residents, business owners, and Community Board leadership. The resulting system, StreetNav, processes a camera’s video feed using computer vision and gives BLV pedestrians real-time navigation assistance. Our evaluations show that StreetNav guides users more precisely than GPS, but its technical performance is sensitive to environmental occlusions and distance from the camera. We discuss future implications for deploying such systems at scale.

Social distancing is an effective public health tool to reduce the spread of respiratory pandemics such as COVID-19. To analyze compliance with social distancing policies, we design two video-based pipelines for social distancing analysis, namely, automated video-based social distancing analyzer (Auto-SDA) and bird’s eye view social distancing analyzer (B-SDA). Auto-SDA is designed to measure social distancing using street-level cameras. To avoid privacy concerns of using street-level cameras, we further develop B-SDA, which uses bird’s eye view cameras, thereby preserving pedestrian’s privacy. We used the COSMOS testbed deployed in West Harlem, New York City (NYC), to evaluate both pipelines. In particular, Auto-SDA and B-SDA are applied on videos recorded by two of COSMOS cameras deployed on the 2nd floor (street-level) and 12th floor (bird’s eye view) of Columbia University’s Mudd building, looking at 120th St. and Amsterdam Ave. intersection, NYC. Videos are recorded before and during the peak of the pandemic, as well as after the vaccines became broadly available. The results represent the impact of social distancing policies on pedestrians’ social behavior. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians failed to adhere to the social distancing policies, whereas this percentage increased to 65% after the vaccines’ availability. Moreover, after the lockdown, 0%–20% of the pedestrians were affiliated with a social group, compared to 10%–45% once the vaccines became available. The results also show that the percentage of face-to-face failures has decreased from 42.3% (prepandemic) to 20.7% (after the lockdown).

Blind and low-vision (BLV) people use GPS-based systems for outdoor navigation assistance, which provide instructions to get from one place to another. However, such systems do not provide users with real-time, precise information about their location and surroundings which is crucial for safe navigation. In this work, we investigate whether street cameras can be used to address aspects of navigation that BLV people still find challenging with existing GPS-based assistive technologies. We conducted formative interviews with six BLV participants to identify specific challenges they face in outdoor navigation. We discovered three main challenges: anticipating environment layouts, avoiding obstacles while following directions, and crossing noisy street intersections. To address these challenges, we are currently developing a street camera-based navigation system that provides real-time auditory feedback to help BLV users avoid obstacles, know exactly when to cross the street, and understand the overall layout of the environment. We close by discussing our evaluation plan.

Crowded metropolises present unique challenges to the potential deployment of autonomous vehicles. Safety of pedestrians cannot be compromised and personal privacy must be preserved. Smart city intersections will be at the core of Artificial Intelligence (AI)-powered citizen-friendly traffic management systems for such metropolises. Hence, the main objective of this work is to develop an experimentation framework for designing applications in support of secure and efficient traffic intersections in urban areas. We integrated a camera and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with an Eclipse sensiNact data platform provided by Kentyou. We use this pipeline to collect and analyze video streams in real-time to support smart city applications. In this demo, we present a video analytics pipeline that analyzes the video stream from a COSMOS' street-level camera to extract traffic/crowd-related information and sends it to a dedicated dashboard for real-time visualization and further assessment. This is done without sending the raw video, in order to avoid violating pedestrians' privacy.

Social distancing can reduce infection rates in respiratory pandemics such as COVID-19, especially in dense urban areas. Hence, we used the PAWR COSMOS wireless edge-cloud testbed in New York City to design and evaluate two different approaches for social distancing analysis. The first, \textbf{Auto}mated video-based \textbf{S}ocial \textbf{D}istancing \textbf{A}nalyzer (\textbf{Auto-SDA}), was designed to measure pedestrians compliance with social distancing protocols using street-level cameras. However, since using street-level cameras can raise privacy concerns, we also developed the \textbf{B}ird's eye view \textbf{S}ocial \textbf{D}istancing \textbf{A}nalyzer (\textbf{B-SDA}) which uses bird's eye view cameras, thereby preserving pedestrians' privacy. Both Auto-SDA and B-SDA consist of multiple modules. This demonstration illustrates the roles of these modules and their overall performance in evaluating the compliance of pedestrians with social distancing protocols. Moreover, we demonstrate applying Auto-SDA and B-SDA on videos recorded from cameras deployed on the 2nd and 12th floor of Columbia's Mudd building, respectively.