Simulating infrastructure networks in the Yangtze River Delta (China) using generative urban network models

This paper explores the urban-geographical potential of simulation approaches combining spatial and topological processes. Drawing on Vértes et al.'s (2012) economical clustering model, we propose a generative network model integrating factors captured in traditional spatial models (e.g., gravity models) and more recently developed topological models (e.g., actor-oriented stochastic models) into a single framework. In our urban network-implementation of the generative network model, it is assumed that the emergence of inter-city linkages can be approximated through probabilistic processes that speak to a series of contradictory forces. Our exploratory study focuses on the outline of the infrastructure networks connecting prefecture-level cities in the highly urbanized Yangtze River Delta (China). Possible hampering factors in the emergence of these networks include distance and administrative boundaries, while stimulating factors include a measure of city size (population, gross domestic product) and a topological rule stating that the formation of connections between cities sharing nearest neighbors is more likely (i.e., a transitive effect). Based on our results, two wider implications of our research are discussed: (1) it confirms the potential of the proposed method in urban network simulation in that the inclusion of a topological factor alongside geographical factors generates an urban network that better approximates the observed network; (2) it allows exploring the differential extent to which driving forces influence the structure of different urban networks. For instance, in the Yangtze River Delta, transitivity plays a less important role in the Internet-network formation; GDP and boundaries more strongly affect the rail network; and distance decay effects play a more prominent role in the road network

Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of Cities

For decades, from design theory to urban planning and management, from social sciences to urban environmental science, cities have been probed and analyzed from the partial perspective of single disciplines. The digital era, with its unprecedented data availability, is allowing for testing old theories and developing new ones, ultimately challenging relatively partial models. Our community has been in the last years providing more and more compelling evidence that cities are complex systems with emergent phenomena characterized by the collective behavior of their citizens who are themselves complex systems. However, more recently, it has also been shown that such multiscale complexity alone is not enough to describe some salient features of urban systems. Multilayer network modeling, accounting for both multiplexity of relationships and interdependencies among the city's subsystems, is indeed providing a novel integrated framework to study urban backbones, their resilience to unexpected perturbations due to internal or external factors, and their human flows. In this paper, we first offer an overview of the transdisciplinary efforts made to cope with the three dimensions of complexity of the city: the complexity of the urban environment, the complexity of human cognition about the city, and the complexity of city planning. In particular, we discuss how the most recent findings, for example, relating the health and wellbeing of communities to urban structure and function, from traffic congestion to distinct types of pollution, can be better understood considering a city as a multiscale and multilayer complex system. The new challenges posed by the postpandemic scenario give to this perspective an unprecedented relevance, with the necessity to address issues of reconstruction of the social fabric, recovery from prolonged psychological, social and economic stress with the ensuing mental health and wellbeing issues, and repurposing of urban organization as a consequence of new emerging practices such as massive remote working. By rethinking cities as large-scale active matter systems far from equilibrium which consume energy, process information, and adapt to the environment, we argue that enhancing social engagement, for example, involving citizens in codesigning the city and its changes in this critical postpandemic phase, can trigger widespread adoption of good practices leading to emergent effects with collective benefits which can be directly measured

A review of urban computing for mobile phone traces

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.BMW GroupAustrian Institute of TechnologySingapore. National Research FoundationMassachusetts Institute of Technology. School of EngineeringMassachusetts Institute of Technology. Dept. of Urban Studies and PlanningSingapore-MIT Alliance for Research and Technology (Center for Future Mobility

Size and spatial and functional structure of aggregate daily mobility networks in functional urban areas: Integrating adjacent spaces at several scales

People's daily movements, as aggregated in spatial units, shape mobility flows that can be modelled by geospatial networks. The structure of these networks both reflects and influences how cities are lived in, perceived and planned. In this study, location tracking data from mobile phones were used to investigate the functional and spatial structures of daily mobility networks and how these networks change as they grow in size. A case study was performed on 81 Spanish functional Urban Areas (FUAs). The results of this study show that the friction of the space and the average length of the aggregate daily mobility are constant and do not depend on the area and demographic size of the FUAs. The average length of the aggregate daily mobility is associated with the increase in complexity that occurs as mobility networks grow, which is reflected in an increase in the number of levels of interaction and the proportion of local flows that integrate adjacent spaces across different scales. The method used provides quantitative indicators that reveal the similarities and differences of daily mobility, as well as the interactions that occur in FUAs according to their size. This information is very useful in urban and mobility planning

Transport impacts on atmosphere and climate: Land transport

Emissions from land transport, and from road transport in particular, have significant impacts on the atmosphere and on climate change. This assessment gives an overview of past, present and future emissions from land transport, of their impacts on the atmospheric composition and air quality, on human health and climate change and on options for mitigation. In the past vehicle exhaust emission control has successfully reduced emissions of nitrogen oxides, carbon monoxide, volatile organic compounds and particulate matter. This contributed to improved air quality and reduced health impacts in industrialised countries. In developing countries however, pollutant emissions have been growing strongly, adversely affecting many populations. In addition, ozone and particulate matter change the radiative balance and hence contribute to global warming on shorter time scales. Latest knowledge on the magnitude of land transport's impact on global warming is reviewed here. In the future, road transport's emissions of these pollutants are expected to stagnate and then decrease globally. This will then help to improve the air quality notably in developing countries. On the contrary, emissions of carbon dioxide and of halocarbons from mobile air conditioners have been globally increasing and are further expected to grow. Consequently, road transport's impact on climate is gaining in importance. The expected efficiency improvements of vehicles and the introduction of biofuels will not be sufficient to offset the expected strong growth in both, passenger and freight transportation. Technical measures could offer a significant reduction potential, but strong interventions would be needed as markets do not initiate the necessary changes. Further reductions would need a resolute expansion of low-carbon fuels, a tripling of vehicle fuel efficiency and a stagnation in absolute transport volumes. Land transport will remain a key sector in climate change mitigation during the next decades

Non-Employment Activity Type Imputation from Points of Interest and Mobility Data at an Individual Level: How Accurate Can We Get?

Human activity type inference has long been the focus for applications ranging from managing transportation demand to monitoring changes in land use patterns. Today’s ever increasing volume of mobility data allow researchers to explore a wide range of methodological approaches for this task. Such data, however, lack reference observations that would allow the validation of methodological approaches. This research proposes a methodological framework for urban activity type inference using a Dirichlet multinomial dynamic Bayesian network with an empirical Bayes prior that can be applied to mobility data of low spatiotemporal resolution. The method was validated using open source Foursquare data under different isochrone configurations. The results provide evidence of the limits of activity detection accuracy using such data as determined by the Area Under Receiving Operating Curve (AUROC), log-loss, and accuracy metrics. At the same time, results demonstrate that a hierarchical modeling framework can provide some flexibility against the challenges related to the nature of unsupervised activity classification using trajectory variables and POIs as input