Analysis of European land transport network, MEGAs and socio-economic setting through Territorial Frames model

The paper illustrates an innovative method, Territorial Frames (TFs) model based, to analyse in the European context the relation among the spatial distribution of the main urban and productive agglomerations (MEGA), the territorial socio-economic setting and the endowment and performance features of the land transport network. The proposed model allows the spatial context to be divided into a multi-scalar sum of TFs, conceived and designed as parts of the territory, with homogeneous spatial and socio-economic characteristics, delimited by multimodal transportation corridors. The assumptions for model construction is illustrated and the final European TFs (ETFs) spatial outline is proposed. In addition, through the introduction of appropriate indices, for each ETF several analyses of correlation between the socio-economic and transport network endowment/features aspects have been carried out. The results, illustrated and discussed both in numerical and spatial terms, show a close correlation between the above aspects.

Europe as the Western Peninsula of Greater Eurasia

Will increased economic connectivity on the Eurasian supercontinent convert Europe into the western peninsula of Greater Eurasia? US geoeconomic primacy has relied on organizing the two other major economic regions of the world, Europe and Asia, into the US-led trans-Atlantic region and Indo-Pacific region. Greater Eurasia is a geoeconomic initiative by Russia and China to integrate Europe and Asia to construct a new region. Greater Eurasia is constructed by first establishing a Russian-Chinese regional partnership that decouples from US primacy, and second to integrate Europe into the new Eurasian region. The geoeconomic architecture for region-building, much like the economics of nation-building, consists of developing connectivity and dependencies with strategic industries, transportation corridors, and financial instruments.

Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels

Mechanically stabilized earth (MSE) walls rely on its weight to resist the destabilizing earth forces acting at the back of the reinforced soil area. MSE walls are a common infrastructure along national and international transportation corridors as they are low-cost and have easy-to-install precast concrete panels. The usability of such transportation corridors depends on the safety and condition of the MSE wall system. Consequently, MSE walls have to be periodically monitored according to prevailing transportation asset management criteria during the construction and serviceability life stages to ensure that their predictable performance measures are met. To date, MSE walls are monitored using qualitative approaches such as visual inspection, which provide limited information. Aside from being time-consuming, visual inspection is susceptible to bias due to human subjectivity. Manual and visual inspection in the field has been traditionally based on the use of a total station, geotechnical field instrumentation, and/or static terrestrial laser scanning (TLS). These instruments can provide highly accurate and reliable performance measures; however, their underlying data acquisition and processing strategies are time-consuming and not scalable. The proposed strategy in this research provides several global and local serviceability measures through efficient processing of point cloud data acquired by a mobile LiDAR system (MLS) for MSE walls with smooth panels without the need for installing any targets. An ultra-high-accuracy vehicle-based LiDAR data acquisition system has been used for the data acquisition. To check the viability of the proposed methodology, a case study has been conducted to evaluate the similarity of the derived serviceability measures from TLS and MLS technologies. The results of that comparison verified that the MLS-based serviceability measures are within 1 cm and 0.3° of those obtained using TLS and thus confirmed the potential for using MLS to efficiently acquire point clouds while facilitating economical, scalable, and reliable monitoring of MSE walls.

The Ecological Impact of Transportation Infrastructure

There is a long-standing debate over whether new roads unavoidably lead to environmental damage, especially forest loss, but causal identification has been elusive. Using multiple causal identification strategies, we study the construction of new rural roads to over 100,000 villages and the upgrading of 10,000 kilometers of national highways in India. The new rural roads had precisely zero effect on local deforestation. In contrast, the highway upgrades caused substantial forest loss, which appears to be driven by increased timber demand along the transportation corridors. In terms of forests, last mile connectivity had a negligible environmental cost, while expansion of major corridors had important environmental impacts.