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The fragmented design of intelligent transportation systems creates isolated intelligent systems. Resource competition and information gaps are fierce and widespread, worsening traffic issues and degrading overall service levels. Therefore, empowered by advanced technologies, an evolution toward an autonomous transportation system (ATS) is observed. This evolution aims to develop a collaborative and sustainable ecosystem, prompting interoperability within the cloud-edge-device continuum. It can, accordingly, dismantle internal resource barriers and achieve a systematic balance between demand and supply with less human intervention. Despite the promising vision of an ATS, it encounters three key challenges: disparate data, deficient models, and conflicting interests in supporting autonomous and personalized mobility. Hence, as an innovative solution, a trustworthy, private, and equal-serving framework called TPE is designed. It seamlessly integrates blockchain, federated learning, and large-scale models to deploy a trustworthy operating environment, process private data for globally shareable knowledge, and develop a foundation model for personalized adaptation, respectively. Consequently, ATSs empowered by TPE can serve diverse user groups both privately and equally.

Abstract Structural information deficits about aging bridges have led to several avoidable catastrophes in recent years. Data-driven methods for bridge vibration monitoring enable frequent, accurate structural assessments; however, the high costs of widespread deployments of these systems make important condition information a luxury for bridge owners. Smartphone-based monitoring is inexpensive and has produced structural information, i.e., modal frequencies, in crowdsensing applications. Even so, current methods cannot extract spatial vibration characteristics with uncontrolled datasets that are needed for damage identification. Here we present an extensive real-world study with crowdsourced smartphone-vehicle trips within motor vehicles in which we estimate absolute value mode shapes and simulate damage detection capabilities. Our method analyzes over 800 trips across four road bridges with main spans ranging from 30 to 1300 m in length, representing about one-quarter of bridges in the United States. We demonstrate a bridge health monitoring platform compatible with ride-sourcing data streams that check conditions daily. The result has the potential to commodify data-driven structural assessments globally.

Abstract Human mobility is a key driver of infectious disease spread. Recent literature has uncovered a clear pattern underlying the complexity of human mobility in cities: $$r \cdot f$$ r · f , the product of distance traveled r and frequency of return f per user to a given location, is invariant across space. This paper asks whether the invariant $$r\cdot f$$ r · f also serves as a driver for epidemic spread, so that the risk associated with human movement can be modeled by a unifying variable $$r\cdot f$$ r · f . We use two large-scale datasets of individual human mobility to show that there is in fact a simple relation between r and f and both speed and spatial dispersion of disease spread. This discovery could assist in modeling spread of disease and inform travel policies in future epidemics—based not only on travel distance r but also on frequency of return f.

Abstract Rooftop photovoltaics (RPVs) are crucial in achieving energy transition and climate goals, especially in cities with high building density and substantial energy consumption. Estimating RPV carbon mitigation potential at the city level of an entire large country is challenging given difficulties in assessing rooftop area. Here, using multi-source heterogeneous geospatial data and machine learning regression, we identify a total of 65,962 km2 rooftop area in 2020 for 354 Chinese cities, which represents 4 billion tons of carbon mitigation under ideal assumptions. Considering urban land expansion and power mix transformation, the potential remains at 3-4 billion tons in 2030, when China plans to reach its carbon peak. However, most cities have exploited less than 1% of their potential. We provide analysis of geographical endowment to better support future practice. Our study provides critical insights for targeted RPV development in China and can serve as a foundation for similar work in other countries.

Given that the effectiveness of COVID-19 vaccines and other therapies is greatly limited by the continuously emerging variants, non-pharmaceutical interventions have been adopted as primary control strategies in the global fight against the COVID-19 pandemic. However, implementing strict interventions over extended periods of time is inevitably hurting the economy. Many countries are faced with the dilemma of how to take appropriate policy actions for socio-economic recovery while curbing the further spread of COVID-19. With an aim to solve this multi-objective decision-making problem, we investigate the underlying temporal dynamics and associations between policies, mobility patterns, and virus transmission through vector autoregressive models and the Toda-Yamamoto Granger causality test. Our findings reveal the presence of temporal lagged effects and Granger causality relationships among various transmission and human mobility variables. We further assess the effectiveness of existing COVID-19 control measures and explore potential optimal strategies that strike a balance between public health and socio-economic recovery for individual states in the U.S. by employing the Pareto optimality and genetic algorithms. The results highlight the joint power of the state of emergency declaration, wearing face masks, and the closure of bars, and emphasize the necessity of pursuing tailor-made strategies for different states and phases of epidemiological transmission. Our framework enables policymakers to create more refined designs of COVID-19 strategies and can be extended to other countries regarding best practices in pandemic response.

The energy transition is increasingly being discussed and implemented to cope with the growing environmental crisis. However, great challenges remain for effectively harvesting and utilizing solar energy in cities related to time and location-dependant supply and demand, which needs more accurate forecasting- and an in-depth understanding of the electricity production and dynamic balancing of the flexible energy supplies concerning the electricity market. To tackle this problem, this article discusses the development of solar cities over the past few decades and proposes a refined and enriched concept of a sustainable solar city with six integrated modules, namely, land surface solar irradiation, three-dimensional (3D) urban surfaces, spatiotemporal solar distribution on 3D urban surfaces, solar photovoltaic (PV) planning, solar PV penetration into different urban systems, and the consequent socio-economic and environmental impacts. In this context, Geographical Information Science (GIScience) demonstrates its potent capability in building the conceptualized solar city with a dynamic balance between power supply and demand over time and space, which includes the production of multi-sourced spatiotemporal big data, the development of spatiotemporal data modelling, analysing and optimization, and geo-visualization. To facilitate the development of such a solar city, this article from the GIScience perspective discusses the achievements and challenges of (i) the development of spatiotemporal big data used for solar farming, (ii) the estimation of solar PV potential on 3D urban surfaces, (iii) the penetration of distributed PV systems in digital cities that contains the effects of urban morphology on solar accessibility, optimization of PV systems for dynamic balancing between supply and demand, and PV penetration represented by the solar charging of urban mobility, and (iv) the interaction between PV systems and urban thermal environment. We suggest that GIScience is the foundation, while further development of GIS models is required to achieve the proposed sustainable solar city.

Abstract Over the last 10 years, ride-hailing companies (such as Uber and Grab) have proliferated in cities around the world. While generally beneficial from an economic viewpoint, having a plurality of operators that serve a given demand for point-to-point trips might induce traffic inefficiencies due to the lack of coordination between operators when serving trips. In fact, the efficiency of vehicle fleet management depends, among other things, density of the demand in the city, and in this sense having multiple operators in the market can be seen as a disadvantage. There is thus a tension between having a plurality of operators in the market, and the overall traffic efficiency. To this date, there is no systematic analysis of this trade-off, which is fundamental to design the best future urban mobility landscape. In this paper, we present the first systematic, data-driven characterization of the cost of non-coordination in urban on-demand mobility markets by proposing a simple, yet realistic, model. This model uses trip density and average traffic speed in a city as its input, and provides an accurate estimate of the additional number of vehicles that should circulate due to the lack of coordination between operators—the cost of non-coordination. We plot such cost across different cities—Singapore, New York (limited to the borough of Manhattan in this work), San Francisco, Vienna and Curitiba—and show that due to non-coordination, each additional operator in the market can increase the total number of circulating vehicles by up to 67%. Our findings could support city policy makers to make data supported decisions when regulating urban on-demand mobility markets in their cities. At the same time, our results outline the need of a more proactive government participation and the need for new, innovative solutions that would enable a better coordination of on-demand mobility operators.

We present a novel metric for measuring relative connection between parts of a city using geotagged Twitter data as a proxy for co-occurrence of city residents. We find that socioeconomic similarity is a significant predictor of this connectivity metric, which we call "linkage strength": neighborhoods that are similar to one another in terms of residents' median income, education level, and (to a lesser extent) immigration history are more strongly connected in terms of the of people who spend time there, indicating some level of homophily in the way that individuals choose to move throughout a city's districts.

We examined the association between meteorological (weather) conditions in a given locale and pedestrian trips frequency and duration, through the use of locative digital data. These associations were determined for seasonality, urban microclimate, and commuting. We analyzed GPS data from a broadly available activity tracking mobile phone application that automatically recorded 247,814 trips from 5432 unique users in Boston and 257,697 trips from 8256 users in San Francisco over a 50-week period. Generally, we observed increased air temperature and the presence of light cloud cover had a positive association with hourly trip frequency in both cities, regardless of seasonality. Temperature and weather conditions generally showed greater associations with weekend and discretionary travel, than with weekday and required travel. Weather conditions had minimal association with the duration of the trip, once the trip was initiated. The observed associations in some cases differed between the two cities. Our study illustrates the opportunity that emerging technology presents to study active transportation, and exposes new methods to wider consideration in preventive medicine. Keywords: Weather, Pedestrian activity, Walking, Weather conditions and active transportation, Microclimates, Spatial behavior, Mobile phones, Locative data, Emerging technology, Big data

The massive lockdown of global cities during the COVID-19 pandemic is substantially improving the atmospheric environment, which for the first time, urban mobility is virtually reduced to zero, and it is then possible to establish a baseline for air quality. By comparing these values with pre-COVID-19 data, it is possible to infer the likely effect of urban mobility and spatial configuration on the air quality. In the present study, a time-series prediction model is enhanced to estimate the nationwide NO _2 concentrations before and during the lockdown measures in the United States, and 54 cities are included in the study. The prediction generates a notable NO _2 difference between the observations if the lockdown is not considered, and the changes in urban mobility can explain the difference. It is found that the changes in urban mobility associated with various road textures have a significant impact on NO _2 dispersion in different types of climates.

Academic research is increasingly cross-disciplinary and collaborative, between and within institutions. In this context, what is the role and relevance of an individual's spatial position on a campus? We examine the collaboration patterns of faculty at the Massachusetts Institute of Technology, through their academic output (papers and patents), and their organizational structures (institutional affiliation and spatial configuration) over a 10-year time span. An initial comparison of output types reveals: 1. diverging trends in the composition of collaborative teams over time (size, faculty versus non-faculty, etc.); and 2. substantively different patterns of cross-building and cross-disciplinary collaboration. We then construct a multi-layered network of authors, and find two significant features of collaboration on campus: 1. a network topology and community structure that reveals spatial versus institutional collaboration bias; and 2. a persistent relationship between proximity and collaboration, well fit with an exponential decay model. This relationship is consistent for both papers and patents, and present also in exclusively cross-disciplinary work. These insights contribute an architectural dimension to the field of scientometrics, and take a first step toward empirical space-planning policy that supports collaboration within institutions.

Since their appearance at the end of the 19th century, traffic lights have been the primary mode of granting access to road intersections. Today, this centuries-old technology is challenged by advances in intelligent transportation, which are opening the way to new solutions built upon slot-based systems similar to those commonly used in aerial traffic: what we call Slot-based Intersections (SIs). Despite simulation-based evidence of the potential benefits of SIs, a comprehensive, analytical framework to compare their relative performance with traffic lights is still lacking. Here, we develop such a framework. We approach the problem in a novel way, by generalizing classical queuing theory. Having defined safety conditions, we characterize capacity and delay of SIs. In the 2-road crossing configuration, we provide a capacity-optimal SI management system. For arbitrary intersection configurations, near-optimal solutions are developed. Results theoretically show that transitioning from a traffic light system to SI has the potential of doubling capacity and significantly reducing delays. This suggests a reduction of non-linear dynamics induced by intersection bottlenecks, with positive impact on the road network. Such findings can provide transportation engineers and planners with crucial insights as they prepare to manage the transition towards a more intelligent transportation infrastructure in cities.

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required.