Monitoring deformations of infrastructure networks: A fully automated GIS integration and analysis of InSAR time-series

Ageing stock and extreme weather events pose a threat to the safety of infrastructure networks. In most countries, funding allocated to infrastructure management is insufficient to perform systematic inspections over large transport networks. As a result, early signs of distress can develop unnoticed, potentially leading to catastrophic structural failures. Over the past 20 years, a wealth of literature has demonstrated the capability of satellite-based Synthetic Aperture Radar Interferometry (InSAR) to accurately detect surface deformations of different types of assets. Thanks to the high accuracy and spatial density of measurements, and a short revisit time, space-borne remote-sensing techniques have the potential to provide a cost-effective and near real-time monitoring tool. Whilst InSAR techniques offer an effective approach for structural health monitoring, they also provide a large amount of data. For civil engineering procedures, these need to be analysed in combination with large infrastructure inventories. Over a regional scale, the manual extraction of InSAR-derived displacements from individual assets is extremely time-consuming and an automated integration of the two datasets is essential to effectively assess infrastructure systems. This paper presents a new methodology based on the fully automated integration of InSAR-based measurements and Geographic Information System-infrastructure inventories to detect potential warnings over extensive transport networks. A Sentinel dataset from 2016 to 2019 is used to analyse the Los Angeles highway and freeway network, while the Italian motorway network is evaluated by using open access ERS/Envisat datasets between 1992 and 2010, COSMO-SkyMed datasets between 2008 and 2014 and Sentinel datasets between 2014 and 2020. To demonstrate the flexibility of the proposed methodology to different SAR sensors and infrastructure classes, the analysis of bridges and viaducts in the two test areas is also performed. The outcomes highlight the potential of the proposed methodology to be integrated into structural health monitoring systems and improve current procedures for transport network management.

An ANNs-Based Method for Automated Labelling of Schematic Metro Maps

Schematic maps are popular for representing transport networks. In the last two decades, some researchers have been working toward automated generation of network layouts (i.e., the network geometry of schematic maps), while automated labelling of schematic maps is not well considered. The descriptive-statistics-based labelling method, which models the labelling space by defining various station-based line relations in advance, has been specially developed for schematic maps. However, if a certain station-based line relation is not predefined in the database, this method may not be able to infer suitable labelling positions under this relation. It is noted that artificial neural networks (ANNs) have the ability to infer unseen relations. In this study, we aim to develop an ANNs-based method for the labelling of schematic metro maps. Samples are first extracted from representative schematic metro maps, and then they are employed to train and test ANNs models. Five types of attributes (e.g., station-based line relations) are used as inputs, and two types of attributes (i.e., directions and positions of labels) are used as outputs. Experiments show that this ANNs-based method can generate effective and satisfactory labelling results in the testing cases. Such a method has potential to be extended for the labelling of other transport networks.

Using weighted multilayer networks to uncover scaling of public transport system

The public transport system is considered as one of the most important subsystems in metropolises for achieving sustainability objectives by mediating resources and travel demand. Representing the various urban transport networks is crucial in understanding travel behavior and the function of the transport system. However, previous studies have ignored the coupling relationships between multi-mode transport networks and travel flows. To address this problem, we constructed a multilayer network to illustrate two modes of transport (bus and metro) by assigning weights of travel flow and efficiency. We explored the scaling of the public transport system to validate the multilayer network and offered new visions for transportation improvements by considering population. The proposed methodology was demonstrated by using public transport datasets of Shanghai, China. For both the bus network and multilayer network, the scaling of node degree versus Population were explored at 1 km * 1 km urban cells. The results suggested that in the multilayer network, the scaling relations between node degree and population can provide valuable insights into quantifying the integration between the public transport system and urban land use, which will benefit sustainable improvements to cities.

A Model for Simulating Emergent Patterns of Cities and Roads on Real-world Landscapes

Emergence of cities and road networks have characterised human activity and movement over millennia. However, this anthropogenic infrastructure does not develop in isolation, but is deeply embedded in the natural landscape, which strongly influences the resultant spatial patterns. Nevertheless, the precise impact that landscape has on the location, size and connectivity of cities is a long-standing, unresolved problem. To address this issue, we incorporate high-resolution topographic maps into a Turing-like pattern forming system, in which local reinforcement rules result in co-evolving centres of population and transport networks. Using Italy as a case study, we show that the model constrained solely by topography results in an emergent spatial pattern that is consistent with Zipf’s Law and comparable to the census data. Thus, we infer the natural landscape may play a dominant role in establishing the baseline macro-scale population pattern, that is then modified by higher-level historical, socio-economic or cultural factors.

Resilience metrics for transport networks: a review and practical examples for bridges

Climate change, diverse geohazards and structural deterioration pose major challenges in planning, maintenance and emergency response for transport infrastructure operators. Hence, to manage these risks and adapt to changing conditions, well-informed resilience assessment and decision-making tools are required. These tools are commonly associated with resilience metrics, which quantify the capacity of transport networks to withstand and absorb damage, recover after a disruption and adapt to future changes. Several resilience metrics have been proposed in the literature, however, there is lack of practical applications and worked examples. This paper attempts to fill this gap and provide engineers and novice researchers with a review of available metrics on the basis of the main properties of resilience, i.e. robustness, redundancy, resourcefulness and rapidity. The main steps of resilience assessment for transport infrastructure such as bridges are discussed and the use of fragility and restoration functions to assess the robustness and rapidity of recovery is demonstrated. Practical examples are provided using a bridge exposed to scour effects as a benchmark. Also, an illustrative example of a systems of assets is provided and different aspects of resilience-based decision making are discussed, aiming to provide a comprehensive, yet straightforward, understanding of resilience.

Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks

We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.

Routing on Geospatial Reference Information for Transport Networks of Spain

The Spanish National Geographic Institute (IGN) published the first release of the Geographic Reference Information on Transport Networks (GRI-TN) in March 2017. Its main goal was to fulfil INSPIRE Directive requirements, as well as to become the main data source for other products developed by the IGN regarding this theme. During the years following that first release, the focus has been on updating and improving the data. This fact has encouraged new data use cases, which differ from the initially planned ones, have arisen, allowing to detect problems in the data, and highlighting the need to evolve the data model, as well as the way in which they are provided to users (not only formats, but also update frequency). One of those use cases is finding the shortest paths between different points in the area covered by the Road Transport Network. In this article, the methodology used to do it is exposed; likewise, the setbacks that have come up during the process and the current limitations of the GRI-TN datasets in order to get most accurate results.