Segment-based spatial analysis for assessing road infrastructure performance using monitoring observations and remote sensing data

Road infrastructure is important to the well-being and economic health of all nations. The performance of road pavement infrastructure is sophisticated and affected by numerous factors and varies greatly across different roads. Large scale spatial analysis for assessing road infrastructure performance is increasingly required for road management, therefore multi-source factors, including satellite remotely sensed climate and environmental data, and ground-monitored vehicles observations, are collected as explanatory variables. Different from the traditional point or area based geospatial attributes, the performance of pavement infrastructure is the line segment based spatial data. Thus, a segment-based spatial stratified heterogeneitymethod is utilized to explore the comprehensive impacts of vehicles, climate, properties of road and socioeconomic conditions on pavement infrastructure performance. Segment-based optimal discretization is applied on discretizing segment-based pavement data, and a segment-based geographical detector is utilized to assess the spatial impacts of variables and their interactions. Results show that the segment-based methods can more reasonably and accurately describe the characteristics of line segment based spatial data and assess the spatial associations. The two major categories of factors associated with pavement damage are the variables of traffic vehicles and heavy vehicles in particular, and climate and environmental conditions. Meanwhile, the interactions between the explanatory variables in these two categories have much more influence than the single explanatory variables, and the interactions can explain more than half of the pavement damage. This study highlights the great potential of remote sensing based large scale spatial analysis of road infrastructures. The approach in this study provides new ideas for spatial analysis for segmented geographical data. The findings indicate that the quantified comprehensive impacts of variables are practical for wise decision-making for road design, construction and maintenance

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Performance Measures to Assess Resiliency and Efficiency of Transit Systems

Transit agencies are interested in assessing the short-, mid-, and long-term performance of infrastructure with the objective of enhancing resiliency and efficiency. This report addresses three distinct aspects of New Jersey’s Transit System: 1) resiliency of bridge infrastructure, 2) resiliency of public transit systems, and 3) efficiency of transit systems with an emphasis on paratransit service. This project proposed a conceptual framework to assess the performance and resiliency for bridge structures in a transit network before and after disasters utilizing structural health monitoring (SHM), finite element (FE) modeling and remote sensing using Interferometric Synthetic Aperture Radar (InSAR). The public transit systems in NY/NJ were analyzed based on their vulnerability, resiliency, and efficiency in recovery following a major natural disaster

Assessing sustainable development in industrial regions towards smart built environment management using Earth observation big data

This thesis investigates the sustainability of nationwide industrial regions using Earth observation big data, from environmental and socio-economic perspectives. The research contributes to spatial methodology design and decision-making support. New spatial methods, including the robust geographical detector and the concept of geocomplexity, are proposed to demonstrate the spatial properties of industrial sustainability. The study delivers scientific decision-making advice to industry stakeholders and policymakers for the post-construction assessment and future planning phases. The research has been published in prestigious geography journals, demonstrating its success

ASSESSING RESILIENCE OF INFRASTRUCTURES TOWARDS EXOGENOUS EVENTS BY USING PS-INSAR-BASED SURFACE MOTION ESTIMATES AND MACHINE LEARNING REGRESSION TECHNIQUES

Abstract. Technologically advanced strategies in infrastructural maintenance are increasingly required in countries such as Italy, where recovery and rehabilitation interventions are preferred to new works. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) techniques have been employed in recent years, achieving reliable outcomes in the identification of infrastructural instabilities. Nevertheless, using the InSAR survey exclusively, it is not feasible to recognize the reasons for such vulnerabilities, and further in-depth investigations are essential.The primary purpose of this paper is to predict infrastructural displacements connected to surface motion and the related causes by combining InSAR techniques and Machine Learning algorithms. The development and application of a Regression Tree-based algorithm have been carried out for estimating the displacement of road pavement structures detected by the Persistent Scatterer InSAR technique.The study area is located in the province of Pistoia, Tuscany, Italy. Sentinel-1 images from 2014 to 2019 were used for the interferometric process, and a set of 29 environmental parameters was collected in a GIS platform. The database is randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation, the model is trained, validated, and the Goodness-of-Fit is evaluated. Also, with the Test set, the Predictive Performance of the model is assessed. Lastly, we applied the model onto a stretch of a two-lane rural road that crosses the area. Results show that the suggested procedure can be used for supporting decision-making processes on planning road maintenance by National Road Authorities

Robust geographical detector

Geographical detector (GD) is a method to measure spatial associations using a power of determinant (PD) value that compares the variance of data within spatial zones and in the whole study area. Recent studies have implemented GD in diverse fields, such as environmental and socio-economic issues. Spatial data discretization is an essential stage for determining zones using explanatory variables. However, the spatial data discretization process has been sensitive to the GD results. To address this issue, this article proposes a Robust Geographical Detector (RGD) to overcome the limitations of the sensitivity in spatial data discretization and estimate robust PD values of explanatory variables using a B-value. The RGD determines spatial zones with numerical interval breaks using an optimization algorithm of variance-based change point detection. In this study, RGD is implemented in a nationwide case study exploring potential factors of nitrogen dioxide (NO2) density in industrial regions across Australia, where data on both NO2 and potential factors are sourced from satellite images and remote sensing products using Google Earth Engine. Results show that RGD can effectively explore the maximum PD values of spatial associations between response and explanatory variables due to the optimization algorithm-based spatial zones. In addition, RGD-based PD values are generally higher, more robust, and more stable than GD-based PD values since RGD can guarantee the increment of PD values with the increase of interval numbers, which is a challenge in previous GD models. Finally, RGD could provide a more reliable interpretation of PD as RGD finds optimal intervals-based spatial zones determined by potential factors. This study demonstrates that the developed RGD model can provide robust and reliable solutions to explore spatial associations and identify geographical factors

The role of earth observation in an integrated deprived area mapping “system” for low-to-middle income countries

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups

Mass-Market Receiver for Static Positioning: Tests and Statistical Analyses

Nowadays, there are several low cost GPS receivers able to provide both pseudorange and carrier phase measurements in the L1band, that allow to have good realtime performances in outdoor condition. The present paper describes a set of dedicated tests in order to evaluate the positioning accuracy in static conditions. The quality of the pseudorange and the carrier phase measurements let hope for interesting results. The use of such kind of receiver could be extended to a large number of professional applications, like engineering fields: survey, georeferencing, monitoring, cadastral mapping and cadastral road. In this work, the receivers performance is verified considering a single frequency solution trying to fix the phase ambiguity, when possible. Different solutions are defined: code, float and fix solutions. In order to solve the phase ambiguities different methods are considered. Each test performed is statistically analyzed, highlighting the effects of different factors on precision and accurac