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The node-place model has been widely used to classify and evaluate transit stations, which sheds light on individual travel behaviors and supports urban planning through effectively integrating land use and transportation development. This article adapts this model to investigate whether and how node, place, and mobility would be associated with the transmission risks and presences of the local COVID-19 cases in a city. Similar studies on the model and its relevance to COVID-19, according to our knowledge, have not been undertaken before. Moreover, the unique metric drawn from detailed visit history of the infected, i.e., the COVID-19 footprints, is proposed and exploited. This study then empirically uses the adapted model to examine the station-level factors affecting the local COVID-19 footprints. The model accounts for traditional measures of the node and place as well as actual human mobility patterns associated with the node and place. It finds that stations with high node, place, and human mobility indices normally have more COVID-19 footprints in proximity. A multivariate regression is fitted to see whether and to what degree different indices and indicators can predict the COVID-19 footprints. The results indicate that many of the place, node, and human mobility indicators significantly impact the concentration of COVID-19 footprints. These are useful for policy-makers to predict and monitor hotspots for COVID-19 and other pandemics transmission.

Although face-to-face social contact decreased during the COVID-19 pandemic, some people remained active in traveling and physically meeting as a group to gain benefits like sustaining intimacy and increasing productivity. These group-based activities indicate the necessity of some essential physical meetings despite high risks of virus infection from physical contact amid the pandemic. To balance the benefits of these meetings with the risks and consequences of infection, more should be done to understand the characteristics of physical social contacts and interactions before and amid the pandemic. Using multiday continuous smartcard data, we identify patterns of intentional group travel in the Hong Kong Metro system. These patterns serve as our proxies for physical (visible) interactions and social (invisible) contact networks among people who intentionally travel as a group (ITG). We measure the spatial centrality of ITGs and persistent group riders (PGRs), a subset of ITGs remaining active amid the pandemic, to infer social interactions among metro riders across different locales. By examining the social network formed by the ITG members across time, we found that its size and interconnections varied during the pandemic, and PGR members might be influential vertices in maintaining the networks’ topological properties. The findings could facilitate transit-usage-and-virus-spread modeling and the formulation of more effective pandemic countermeasures in transit-reliant cities.

Given its wide-ranging and long-lasting impacts, COVID-19, especially its spatial spreading dynamics has received much attention. Knowledge of such dynamics helps public health professionals and city managers devise and deploy efficient contact-tracing and treatment measures. However, most existing studies focus on aggregate mobility flows and have rarely exploited the widely available disaggregate-level human mobility data. In this paper, we propose a Personalized PageRank (PPR) method to estimate COVID-19 transmission risks based on a bipartite network of people and locations. The method incorporates both mobility patterns of individuals and their spatiotemporal interactions. To validate the applicability and relevance of the proposed method, we examine the interplay between the spread of COVID-19 cases and intra-city mobility patterns in a small synthetic network and a real-world mobility network from Hong Kong, China based on transit smart card data. We compare the recall (sensitivity), accuracy, and Spearmans correlation coefficient between the estimated transmission risks and number of actual cases based on various mass tracing and testing strategies, including PPR-based, PageRank (PR)-based, location-based, route-based, and base case (no strategy). The results show that the PPR-based method achieves the highest efficiency, accuracy, and Spearmans correlation coefficient with the actual case number. This demonstrates the value of PPR for transmission risk estimation and the importance of incorporating individual mobility patterns for efficient contact-tracing and testing.

Car dependence has been threatening transportation sustainability as it contributes to congestion and associated externalities. In response, various transport policies that restrict the use of private vehicle have been implemented. However, empirical evaluations of such policies have been limited. To assess these policies' benefits and costs, it is imperative to accurately evaluate how such policies affect traffic conditions. In this study, we compile a refined spatio-temporal resolution data set of the floating-vehicle-based traffic performance index to examine the effects of a recent nonlocal vehicle driving restriction policy in Shanghai, one of most populous cities in the world. Specifically, we explore whether and how the policy impacted traffic speeds in the short term by employing a quasi-experimental difference-in-differences modeling approach. We find that: (1) In the first month, the policy led to an increase of the network-level traffic speed by 1.47% (0.352 km/h) during evening peak hours (17:00-19:00) but had no significant effects during morning peak hours (7:00-9:00). (2) The policy also helped improve the network-level traffic speed in some unrestricted hours (6:00, 12:00, 14:00, and 20:00) although the impact was marginal. (3) The short-term effects of the policy exhibited heterogeneity across traffic analysis zones. The lower the metro station density, the greater the effects were. We conclude that driving restrictions for non-local vehicles alone may not significantly reduce congestion, and their effects can differ both temporally and spatially. However, they can have potential side effects such as increased purchase and usage of new energy vehicles, owners of which can obtain a local license plate of Shanghai for free.

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Intermodal Transport Data-Sharing Programme for Hong Kong

Globally, cities are the source of over 70 percent of the world's greenhouse gas emissions. Cities are also the engines of the global economy, concentrating more than half the world’s population, and they are where the middle class is rapidly expanding. Indeed, by the year 2050, two-thirds of the world will be urban, with cities accommodating an additional 2.5 billion people over today’s total. Nearly all of this urban growth will occur in developing countries. This concentration of people and assets also means that the impacts of natural disasters, exacerbated by the changing climate, may be even more devastating, both in terms of human lives lost and economic livelihoods destroyed. These effects will disproportionately burden the poor. Earth is on a trajectory of warming more than 1.5 degrees Celsius unless important decarbonizing steps are taken.Often urban policymakers prescribe integration as the solution to steering urbanization towards decarbonization to achieve greater global and local environmental benefits. However, little is known about the struggles—and successes—that cities in developing countries have in planning, financing, and implementing integrated urban solutions. The main objective of this report is to understand how a variety of developing and emerging economies are successfully utilizing horizontal integration—across multiple infrastructure sectors and systems—at the metropolitan scale to deliver greater sustainability. This report explores how integrated planning processes extending well beyond city boundaries have been financed and implemented in a diverse group of metropolitan areas. From this analysis, the report derives models, poses guiding questions, and presents three key principles to provoke and inspire action by cities around the world.

International audience; Our work aims to develop novel technologies for building an efficient data infrastructure as a backbone for a human behavior management system. Our infrastructure aims at facilitating behavior modeling, discovery, and exploitation, leading to two major outcomes: a behavior data management back-end and a high-level behavior specification API that supports mining, indexing and search, and AI-powered algorithms that provide the ability to extract insights on human behavior and to leverage data to advance human capital. We discuss the role of ML in populating and maintaining the back-end, and in exploiting it for human interest.

Based on the 2008 Xi’an city-wide household travel survey data, this manuscript investigates the jobs-housing balance and commuting efficiency in Xi’an, against the backdrop of the dramatic urban spatial/social transformations occurred in the city since the 1990s. It compares selected excess-commuting indicators of Xi’an with those in selected Chinese and foreign cities. It finds that (a) Xi’an has a short theoretical minimum commute, which indicates a good relative numerical balance of jobs with respect to housing; (b) Xi’an has a relatively low commuting efficiency as compared to most of the foreign cities and other Chinese cities—but this can be caused by differences in the size of units of analysis across studies; (c) jobs-residents ratios would not significantly reduce the average commuting distance at the subarea level; (d) job density and migrant ratio are significantly correlated to the average commuting distance at the subarea level; (e) the jobs-housing balance has bigger impacts on bus commutes than on car commutes. This manuscript re-confirms the importance of conducting separate studies of the jobs-housing balance and related issues in the Chinese context. It also shows such studies could generate new insights and policy implications.

Mobility of economically underprivileged residents in China has seldom been well profiled due to privacy issue and the characteristics of Chinese over poverty. In this paper, we identify and characterize underprivileged residents in Beijing using ubiquitous public transport smartcard transactions in 2008 and 2010, respectively. We regard these frequent bus/metro riders (FRs) in China, especially in Beijing, as economically underprivileged residents. Our argument is tested against (1) the household travel survey in 2010, (2) a small-scale survey in 2012, as well as (3) our interviews with local residents in Beijing. Cardholders' job and residence locations are identified using Smart Card Data (SCD) in 2008 and 2010. Our analysis is restricted to cardholders that use the same cards in both years. We then classify all identified FRs into 20 groups by residence changes (change, no change), workplace changes (change, no change, finding a job, losing a job, and all-time employed) during 2008-2010 and housing place in 2010 (within the fourth ring road or not). The underprivileged degree of each FR is then evaluated using the 2014 SCD. To the best of our knowledge, this is one of the first studies for understanding long- or mid-term urban dynamics using immediate "big data", and also for profiling underprivileged residents in Beijing in a fine-scale., Comment: 25 pages