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Digital Twins (DTs) are gaining interest among urban planners and city authorities, but a notable gap exists in fully understanding and effectively applying them to complex and multifaceted transport planning processes. This review is an in-depth exploration of the promising concept of DTs and its implications for transport planning—its current applications and conceptualisations within the domain, focusing on their distinction from traditional transport models and identifying critical areas for further DT development. The review was conducted per PRISMA guidelines to identify 136 relevant studies from 2000 to 2024. Over three-quarters of the articles were published after 2021, indicating a growing focus on digital twins in transport planning. A classical five-component definition of DT, consisting of physical, digital, data model, service and connection components, was adapted and used to synthesise key components of the DTs presented in the studies. Simulations were the most common “data model,” and 31% of studies do not present insight into what “services” could be derived from proposed DTs, indicating a top-down approach to DT development. A prevalent misapplication of the DT concept was observed where most studies conflated basic digital models or shadows as DTs without fully meeting the criteria of real-time data integration or bi-directional interaction with physical systems. Interestingly, mature DTs with real-time data and bidirectional interaction were associated with smaller geographical systems and shorter time-range predictions. This suggested that maturity in transport planning DTs depends not only on technological capabilities but also on the spatiotemporal dimension of the system. The review concludes by suggesting directions towards a revised DT maturity, highlighting the level of maturity needed for “usable DTs”, and advocating the need for a bottom-up approach to DT development rather than the top-down one currently adopted by most studies.

During the COVID-19 pandemic, individuals depended on risk information to make decisions about everyday behaviors and public policy. Here, we assessed whether an interactive website influenced individuals’ risk tolerance to support public health goals. We collected data from 11,169 unique users who engaged with the online COVID-19 Event Risk Tool ( https://covid19risk.biosci.gatech.edu/ ) between 9/22/21 and 1/22/22. The website featured interactive elements, including a dynamic risk map, survey questions, and a risk quiz with accuracy feedback. After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in events that could spread COVID-19, especially for high-risk large events. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events but underestimate the risk of large events. Importantly, even participants who voluntarily sought information about COVID risks tended to misestimate exposure risk, demonstrating the need for intervention. Participants from liberal-leaning counties were more likely to use the website tools and more responsive to feedback about risk misestimation, indicating that political partisanship influences how individuals seek and engage with COVID-19 information. Lastly, we explored temporal dynamics and found that user engagement and risk estimation fluctuated over the course of the Omicron variant outbreak. Overall, we report an effective large-scale method for communicating viral exposure risk; our findings are relevant to broader research on risk communication, epidemiological modeling, and risky decision-making.

In March 2017, an overpass on I–85 in Atlanta caught fire and collapsed, disrupting traffic for 43 days while the Georgia Department of Transportation (GDOT) rebuilt the bridge. During this time, transit ridership increased as commuters reacted to the changes in travel time, thanks in part to concerted efforts to expand service by the Metropolitan Atlanta Rapid Transit Authority (MARTA). Ridership declined after GDOT restored service but remained higher than pre-disaster levels, requiring further research to understand how long the effect lasted.Multiple linear regression models are used to investigate the relationship between 2019 ridership and origins and destinations affected by the bridge collapse. Travel time matrices from the Atlanta Regional Commission’s (ARC) Activity Based Model (ABM) are used to identify Traffic Analysis Zones (TAZs) with notable service impacts and choose comparable regions. The weighted trip counts from the ARC’s 2010 and 2019 onboard transit surveys map transit trips to origin and destination TAZs. When controlling for MARTA’s service quantity, residential and employment population, and the percent of households without access to a vehicle (choice riders), the models found a significant relationship between the region impacted by the bridge collapse and an increase in MARTA rail trips and MARTA trips by patrons who could have used a vehicle. A significant increase in choice rail ridership from the impacted TAZs, those most likely to have switched during the network disruption of 2017, suggests that the bridge collapse’s impact on MARTA riders lasted until at least the fall of 2019, over two years after the inciting network disruption.

Sidewalk infrastructure presence is a key indicator of pedestrian safety and walkability for neighborhoods in cities throughout the United States. The existence and condition of sidewalk infrastructure, however, is not prioritized as much as motor vehicle infrastructure. Many cities lack sustained maintenance and operations programs for sidewalk infrastructure and comprehensive datasets covering the locations and distributions of sidewalk infrastructure, limiting the ability to develop such programs. This work refines prior sidewalk infrastructure network generation techniques, contributing new methods to identify sidewalk infrastructure presence.QA/QC efforts were conducted for in Atlanta’s sidewalk network by correcting errors identified in input data. Error identification and correction times were comprehensively tracked and used to estimate future labor costs. A Custom application with online access to Bing Maps Streetside and aerial imagery was developed to allow technicians to verify sidewalk presence data, which were joined to the structural sidewalk network and associated with adjacent parcels. Cost of ownership of Atlanta’s sidewalk infrastructure over an 80-year management period is then broken down by asset type and allocated in part to property owners directly adjacent to the applicable infrastructure, while remaining costs are recovered through a proportional increase in property tax millage rates. Sidewalk network estimates developed in previous Atlanta research efforts decreased sidewalk network mileage by 12% (386 miles), post-QA/QC. Regression analysis of error correction activity and labor data indicates gaps between tax parcels and misplacement of intersection centroids significantly increased QA/QC labor costs. Overall, 46% of Atlanta’s potential sidewalk links were present(i.e., along property superblock boundaries), with significant clustering in the city’s oldest neighborhoods. Hence, sidewalk repair and maintenance costs accrue disproportiona