Infrastructure Planning Research Articles

Quantification and validation of uncertainties in subsoil models

AbstractIn infrastructure planning and construction, modeling the subsoil and its associated uncertainty is a fundamental task of geotechnical engineers. However, probabilistic methods and tools for quantifying and displaying the uncertainty of the subsoil models are rarely used in practice where deterministic interpolation dominates. In digital planning using Building Information Modeling (BIM), the probabilistic approach supports creating a discipline model in which the uncertainties of the spatial layer structure are statistically quantified to evaluate the georisks in the design and execution of civil constructions. This article presents a case study using a combination of Sequential Gaussian Simulation (SGSIM) and Sequential Indicator Simulation (SISIM) to account for uncertainties in soil layer geometry. In a case study at the Munich Town Hall, a geostatistical approach is applied and validated based on 70 bore logs, whereby the probabilities for the occurrence of a particular layer are spatially quantified. The case study illustrates the methodology‘s great potential and benefits compared to the conventional deterministic approach based on interpolation procedures.

The contribution of remote sensing and geographic information systems to the morphometric study of six watersheds in the tindouf region (extreme south-western Algeria)

Understanding the hydrological system and improving water resource management requires a thorough analysis of the morphometric characteristics of watersheds. This study seeks to examine the hydrological and morphometric features of the water drainage network in the Tindouf region's numerous watersheds. A digital elevation model (DEM), which is an essential digital asset in GIS, was created using a 30 m resolution SRTM satellite image. The DEM model was then used to produce a map of the drainage network for the basins in the study area. Additionally, various hydrological and geomorphological features were extracted. This research is significant in promoting economic development and improving the quality of life of residents by establishing better resource allocation and water management, determining appropriate areas for urban development, infrastructure planning, tourism, and entertainment.

Planning and Construction Strategies for Infrastructure in Traditional Villages of the Grand Canal from the Perspective of Villager Needs

The traditional villages along the Jiangsu section of the Grand Canal are facing the threat of decline and disappearance due to the decline of the canal transportation function and the advancement of urbanization, and it is urgent to study the protection and renewal of these villages. Based on the needs of villagers, this study aims to explore the planning and construction strategies of traditional village infrastructure in the Jiangsu section of the Grand Canal. Through a literature review, field investigation, and qualitative and quantitative analysis methods, this paper conducts in-depth research and data analysis on the status quo of six types of infrastructure in 29 traditional villages, including road traffic, water supply and drainage, garbage sanitation, energy utilization, postal communication, and ecological disaster prevention. The research results show that, although the infrastructure construction has made some progress, there are still problems such as unreasonable planning, low construction quality, and the lack of cultural characteristics, which cannot fully meet the actual needs of villagers. Through a regional comparative analysis, it is found that the infrastructure condition of the Jiangnan Canal section is better than that of the Huaiyang Canal section and Middle Canal section, but they all face challenges of protection and renewal. Based on the needs of villagers, this study puts forward corresponding protection and optimization strategies, which provide theoretical guidance and practical methods for the sustainable development of traditional village infrastructure, and it has important reference significance for the infrastructure protection and development of traditional villages in other regions.

AI-Enhanced Prediction of Pavement Crack Propagation: A Study Using Traffic Load, Environmental and Material Data

This study develops an AI-based predictive model for forecasting pavement crack propagation by integrating traffic load, environmental conditions, and material property data. Traditional pavement management systems often struggle to accurately predict crack growth due to the complex interactions between these influencing factors. By leveraging data from various sources, including sensor-based traffic metrics, meteorological data, and material composition tests, this study identifies significant variables contributing to crack initiation and progression. The proposed model utilizes a blend of machine learning algorithms, including Random Forest and neural networks, with a cross-validation approach to ensure robustness. Results indicate that the model achieves high prediction accuracy, with an RMSE of 1.2 mm/year and an R-squared value close to 0.93. The findings support the use of AI-enhanced models as reliable tools for road infrastructure planning and maintenance, promising reductions in maintenance costs and improved pavement durability.

Road Traffic Infrastructure Construction and Air Pollution Based on the Perspective of Spatial Spillover

Road traffic infrastructure construction is widely regarded as a solution to urban congestion and air pollution. Given the frequent use of non-highway roads for inter-city travel, it is plausible that such infrastructure projects could also improve air quality in neighboring regions. However, the spatial spillover effects of these projects on air pollution remain underexplored. This paper investigates the spatial impacts of road traffic infrastructure construction on urban air pollution. Using a spatial panel regression model, we analyze data from 273 cities in a period spanning 2008 to 2021. Our findings show that road infrastructure construction significantly reduces local air pollution, with results robust to concerns about endogeneity. Furthermore, we identify notable spatial spillover effects, primarily concentrated in adjacent cities. These findings suggest that the accessibility between central cities and their neighboring areas plays a critical role in shaping traffic patterns, air quality, and sustainable urban development. As a result, coordinated regional planning for road infrastructure could serve as an effective strategy to mitigate urban air pollution and promote sustainable growth.

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Advancing water security in Africa with new high-resolution discharge data

VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. Performance metrics show strong model reliability, with R² of 0.5–0.9, NSE of 0.6–0.9, and KGE of 0.5–0.8 at the continental scale. The total average annual discharge for Africa is quantified at 3271.4 km³·year−1, with contributions to oceanic basins: 1000.0 km³·year−1 to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³·year−1 to the South Atlantic, largely from the Congo River; 214.7 km³·year−1 to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³·year−1 to the Indian Ocean, with inputs from rivers such as the Zambezi. The dataset is valuable for stakeholders and researchers to understand water availability, its temporal and spatial variations that affect water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience strategies.

COST OPTIMIZATION METHOD FOR INFORMATIONAL INFRASTRUCTURE DEPLOYMENT IN STATIC MULTI-CLOUD ENVIRONMENT

Context. In recent years, the topic of deploying informational infrastructure in a multi-cloud environment has gained popularity. This is because a multi-cloud environment provides the ability to leverage the unique services of cloud providers without the need to deploy all infrastructure components inside them. Therefore, all available services across different cloud providers could be used to build up information infrastructure. Also, multi-cloud offers versatility in selecting different pricing policies for services across different cloud providers. However, as the number of available cloud service providers increases, the complexity of building a costoptimized deployment plan for informational infrastructure also increases. Objective. The purpose of this paper is to optimize the operating costs of information infrastructure while leveraging the service prices of multiple cloud service providers. Method. This article presents a novel cost optimization method for informational infrastructure deployment in a static multicloud environment whose goal is to minimize the hourly cost of infrastructure utilization. A genetic algorithm was used to solve this problem. Different penalty functions for the genetic algorithm were considered. Also, a novel parameter optimization method is proposed for selecting the parameters of the penalty function. Results. A series of experiments were conducted to compare the results of different penalty functions. The results demonstrated that the penalty function with the proposed parameter selection method, in comparison to other penalty functions, on average found the best solution that was 8.933% better and took 18.6% less time to find such a solution. These results showed that the proposed parameter selection method allows for efficient exploration of both feasible and infeasible regions. Conclusion. A novel cost optimization method for informational infrastructure deployment in a static multi-cloud environment is proposed. However, despite the effectiveness of the proposed method, it can be further improved. In particular, it is necessary to consider the possibility of involving scalable instances for informational infrastructure deployment.

The Impact of Agglomeration on CO2 Emissions in China's Transport Sector: A Spatial Econometric Analysis

The long-term processes of urbanization and industrialization have led to the agglomeration of population and industry, fostering economic development while introducing opportunities and challenges for carbon reduction in transport. This paper integrates the Stochastic Impacts by Regression on Population, Affluence, and Technology Model with the Spatial Durbin Model to assess the effects of population agglomeration and industrial agglomeration on transport carbon dioxide emissions. The empirical results show that a 1% increase in population agglomeration decreases local transport carbon dioxide emissions by 1.7065% and generates a spillover effect of 1.0542% in surrounding areas. In contrast, industrial agglomeration increases regional transport carbon dioxide emissions by an average of 0.3309% without significant spillover effects. Furthermore, economic agglomeration exhibits an N-shaped relationship with transport carbon dioxide emissions, reflecting the dual influences of the "economic effect" and the "congestion effect". Mechanism analysis reveals that both types of agglomeration can modulate the impact of infrastructure development on transport carbon dioxide emissions, suggesting that effective infrastructure planning can help alleviate the negative environmental impacts. This study provides a spatial mode for understanding the synergistic effects of population planning, industrial development, and environmental improvement, offering significant reference value for policymakers in the decision-making related to low-carbon transport development.

An advanced spatial decision model for strategic placement of off-site hydrogen refueling stations in urban areas

The strategic placement of hydrogen refueling stations (HRSs) is crucial for the successful adoption of hydrogen fuel cell vehicles (HFCVs) and the promotion of sustainable urban transportation. However, existing spatial decision models using Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) often stop at generating suitability maps and rely on simplistic or arbitrary site placement methods, such as fixed service radii, without optimizing spatial distribution that overlook inherent uncertainties, limiting the effectiveness of the decision-making process. This study develops an advanced spatial decision model to handle uncertainty and optimize HRS placement in Prague, Czechia. The model integrates multiple methodologies: (i) Utilizing 21 criteria across accessibility, environmental, infrastructural, and socioeconomic dimensions, with criteria weights prioritized using the Fuzzy Analytic Hierarchy Process (FAHP) to manage uncertainty in expert judgments. GIS suitability analysis identified optimal areas, with 18.13% of Prague classified as highly suitable for HRS deployment. (ii) Implementing Fuzzy C-Means (FCM) clustering to optimize site distribution and address uncertainty in HRS placement, proposing 10 optimal locations validated by a silhouette score of 0.68. (iii) Evaluating model performance through sensitivity analysis, revealing responsiveness to criteria variations. To evaluate and rank the proposed HRS locations, we integrated a Genetic Algorithm (GA) with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), optimizing the selection process by exploring a wider solution space. Additionally, accessibility analysis assessed emergency response coverage, ensuring efficient response times. This multi-methodological framework ensures a robust, data-driven approach to site selection, optimizing accessibility, minimizing environmental impact, and promoting sustainable urban transportation. It advances strategic infrastructure planning, sets a precedent for integrating advanced analytic techniques to handle uncertainty and automate site selection in spatial decision-making, and is adaptable to diverse urban contexts.

Process evaluation of an urban piped water supply infrastructure improvement programme in Uvira, Democratic Republic of the Congo

Major investments in water supply infrastructure will be required to achieve Sustainable Development Goal (SDG) 6. Safely managed water services are also central to global cholera and diarrhoeal diseases prevention strategies. However, evidence remains scarce on how to efficiently improve piped water services in complex settings where infrastructure investments are most needed. We conducted a process evaluation of a large-scale water supply infrastructure improvement programme in Uvira, Democratic Republic of the Congo, in parallel to a pragmatic trial. Considering three evaluation domains–context, implementation, and population response–, we assessed the validity of the programme’s theory of change and underlying assumptions. Information sources included construction works documentation, operational and billing records from the water utility, and household surveys. The evaluation covers the period 2014–2021. Trial results are not within the scope of this manuscript. The programme did not achieve expected improvements in the water supply service during the evaluation period. Out of 16 assumptions underlying the theory of change, six remained valid, seven partially valid, and three turned out to be invalid. Contextual challenges included extreme flooding in 2020 and the Covid-19 pandemic, which disrupted construction works. Issues related to electricity supply and the rise of Lake Tanganyika emphasise the need for cross-sectoral approaches and consideration of climate change in the planning of water supply infrastructure. Implementation challenges underlined the importance of capacity strengthening alongside infrastructure improvements. Population response elements suggest that affordability and informal practices such as tap sharing should be taken into account. The programme was a good example of early engagement with researchers and provides unique insights into the implementation of large-scale infrastructure improvements in a complex, low-income setting. Pragmatic evaluation approaches should be adopted for the generation of scientific evidence from complex programmes in order to optimise future infrastructure investments contributing to progress towards SDG6.

The legality of roads in Chile, 1842–1969: A historical overview

Currently, no academic work examines the history of the legality of roads in Chile during its independent existence as a sovereign country. Addressing this gap in the literature, this paper focuses specially on the period from 1842 to 1969, when different actors articulated a set of guiding ideas about the duties of the state and the legal powers of the administrative authority in terms of planning, construction and management of road infrastructure that would allow connectivity between population centers and across regions, according to the ideas and resources available at their historical time. This historical overview of Chilean “road law” is done in the light of insights and questions of contemporary intellectual history and institutional history. In this regard, it is argued that the evolution of road infrastructure norms and institutions during the period under study can be divided into three historical regimes, based on their fundamental legislative milestones, guiding ideas, institutional settings, and strategies of state action: from 1842 to 1887, a period of a decentralized “minimal road state” with precarious roads characterized by both material and juridical uncertainty; from 1887 to 1920, the emergence of a “proto-developmentalist road state” intent on strengthening its grip on the nationwide road infrastructure; and from 1920 to 1969, a period of a “techno-developmentalist road state” that created a nationwide paved road network for the new technology of mobile vehicles.

1D-2D hydrodynamic and sediment transport modelling using MIKE models

A comprehensive modeling framework utilizing hydrodynamic and sediment transport models (MIKE Hydro River, MIKE 21 FM, MIKE 21C) was applied to the 33.57 km stretch of the Banshadhara River from Banshadhara bridge, Gunupur to Kashinagar town. Daily discharge, water levels, and sediment data from 2013 to 2022 of Gunupur and Kashinagar gauge stations was used as model input, and calibration–validation of models. Additionally, satellite remote sensing data used for determined Manning’s roughness (n) values as well compared the model outputs. Calibration procedures ensured high accuracy, with MIKE Hydro River achieving 98.3% agreement between observed and simulated water levels. 1D sediment transport model showing significant variability in total bed load values ranging from 0.3 to 3.83 m^3/s. The Wilcock & Crowe gravel bed load formula accurately predicted bed load transport within the expected magnitude, albeit tending to overestimate transport, indicative of supply-limited conditions. 2D MIKE 21 FM simulations highlighted flow dynamics, with rapid flood arrival at Kashinagar and varying current speeds along the river, especially at bends. MIKE 21C, focused on the central portion, indicated erosion processes at river bends with high discharge. Sediment transport model accuracy was approximately 86.7%, capturing general trends despite occasional discrepancies. Over a 10-year simulation, erosion trends and potential river course shifting were observed, particularly near bends. The analysis provides valuable insights for river management, flood risk mitigation, water resource management, infrastructure planning, and environmental preservation in the Banshadhara River basin and similar environments.

What Makes Local Planning Effective in a Megacity? The Overlapping Agendas and Scale Inconsistencies in Developing Buenos Aires’ Affordable Land Markets

In limited-resourced Latin-American cities, adapting urban regulations and capturing value offers great potential for developing affordable land. We explored what counts for the significant variance in local land management implementation within Buenos Aires Metropolitan Region. Analysed municipalities successfully developed high-quality instruments to expand affordable land markets. However, local planning effectiveness does not find limits in the quality of planning itself. Depending on the municipalities’ relative location within the metro area, the national-level pipeline infrastructure plans, the politically driven finance choices, and divergent administration protocols favour or hinder the capacity to implement an effective urban land development strategy at local levels.

Uptake of ecosystem service concept in urban blue-green infrastructure planning and decision-making in Russia

ABSTRACT In the scientific community, ecosystem services (ESs) are well-recognized as a promising concept to more actively consider and plan for the multitude of benefits provided by the green environment and thus promote sustainable urban development. Yet, to have an impact on decision-making, the concept must spread from academia to practice. To understand to what extent ES have been adopted in practice and decision-making in Russia, we conducted a series of interviews and an online survey with both practitioners and scientists and complemented it by document analysis. Our results showed that ES are not adequately reflected in urban decision-making and planning processes. Moreover, we found no explicit references to ES in strategic urban planning and landscape development projects, although there are few implicit references in several reviewed documents. Among the identified challenges for better uptake of the concept in practice were many disagreements associated with the lack of a unified terminology, typology, and methods. Stakeholders understand the potential of applying ES valuation in practice, which includes a necessity to develop methodological materials and different tools based on real-life cases. Finally, a list of proposals on possible ways to better include ES in urban planning and in decision-making processes was formed by the respondents.

A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.

Dynamic Adaptive Charging Network Planning Under Deep Uncertainties

Charging infrastructure is the backbone of electromobility. Due to new charging behaviors and power distribution and charging space constraints, the energy demand and supply patterns of electromobility and the locations of current refueling stations are misaligned. Infrastructure developers (charging point operators, fleet operators, grid operators, vehicle manufacturers, and real-estate developers) need new methodologies and tools that help reduce the cost and risk of investments. To this extent we propose a transport-energy-demand-centric, dynamic adaptive planning approach and a data-driven Spatial Decision Support System (SDSS). In the SDSS, with the help of a realistic digital twin of an electrified road transport system, infrastructure developers can quickly and accurately estimate key performance measures (e.g., charging demand, Battery Electric Vehicle (BEV) enablement) of a candidate charging location or a network of locations under user-specified transport electrification scenarios and constraints and interactively and continuously calibrate and/or expand their network plans as facts about the deep uncertainties about the supply side of transport electrification (i.e., access to grid capacity and real-estate and presence of competition) are gradually discovered/observed. This paper describes the components and the planning support of the SDSS and how these can be used in competitive and collaborative settings. Qualitative user evaluations of the SDSS with 33 stakeholder organizations in commercial discussions and pilots have shown that both transport-energy-demand-centric and dynamic adaptive planning of charging infrastructure planning are useful.

Infrastructure first principles for the Anthropocene

Abstract There appears to be a growing decoupling between the conditions that infrastructures were designed for and today’s rapidly changing environments. Infrastructures today are largely predicated on the technologies, goals, and governance structures from a century ago. While infrastructures continue to deliver untold value, there is growing evidence that these critical, basic, and lifeline systems appear ill-equipped to confront the volatility, uncertainty, accelerating conditions, and complexity that define them and their changing environments. Innovative and disruptive first principles are needed to guide infrastructures in the Anthropocene. Drawing from emerging infrastructure research and disciplines that appear better able to confront disruption and change, a novel set of first principles are identified: (1) Plan for complex conditions and surprise; (2) Recouple with agility and flexibility; (3) Govern for exploration and instability; (4) Build consensus as control decentralizes; (5) Restructure to engage with porous boundaries; and, (6) Cyberthreat planning is now mission critical. These principles should guide infrastructure planning recognizing the changing nature and increasingly obsolete boundaries that have defined engineered systems in the modern era.

Optimization of Facilities and Infrastructure Management in Improving the Quality of Learning at Madrasah Ibtidaiyah

The school must also be able to maintain and care for the existing facilities and infrastructure. Thus, students can learn optimally and effectively thanks to school facilities and infrastructure. The aims of this study were (1) to describe how the Planning of Educational Facilities and Infrastructure at M.I.S. Guppi 13 Tasikmalaya, (2) to describe how the Procurement of Educational Facilities and Infrastructure at M.I.S. Guppi 13 Tasikmalaya, (3) to describe how the Maintenance of Facilities and Infrastructure Education at M.I.S. Guppi 13 Tasikmalaya, (4) to describe how efforts to improve the management of educational facilities and infrastructure should be carried out in the future. The method used is field research (field research), which uses a qualitative descriptive approach. This study has three data analysis steps: reduction, presentation of data (display data), and conclusion (Verification). The research results show several conclusions, one of which is that M.I.S. Guppi 13 Tasikmalaya focuses on the administration, provision, usage, and upkeep of educational infrastructure. However, the implementation may be improved even though infrastructure and educational facilities are crucial for supporting teaching and learning activities.

Analyzing the posterior predictive capability and usability of landslide susceptibility maps: a case of Kerala, India

AbstractLandslide susceptibility maps serve as the basis for hazard and risk assessment, as well as risk-informed land use planning at various spatial scales. Researchers create these maps aiming to fulfil a variety of purposes, including infrastructure planning and restrictive land use zoning. These applications require accurate and specific information to fulfil these purposes, as decisions based on these maps have the potential to cost lives and cause infrastructure damage. The usability of the maps depends on whether they provide the required information and their accuracy to be utilized for the intended purpose. Therefore, assessing the usability and predictive accuracy of landslide susceptibility maps is of paramount importance. Typically, the accuracy of the maps is evaluated using the same landslide inventory that was used to create the map, which does not actually test the predictive ability of the maps in future situations. In this study, we briefly reviewed the purposes of the map creation using literature and stakeholder interviews and assessed the accuracy of three landslide susceptibility maps in a posterior manner. We generated a multi-temporal landslide event inventory after the creation dates of these landslide susceptibility maps. We devised a method to evaluate classified maps by making use of Unique Conditions Units (UCUs) to compare the posteriorly predicted susceptibility classes and the new landslide occurrences. Interviews with stakeholders revealed a disconnection between the aims set forth by map producers and the specific needs of the end users. Our posterior assessment shows that overall predictions of the maps provide plausible results; however, their interpretations for different use cases make them less likely to be used. When comparing the maps using UCUs, landslide densities overlap between the different susceptibility classes, indicating low predictive performance of the maps. Direct comparison of all maps shows a low agreement between susceptibility classes, which pinpoints the uncertainties in data and methods used to create different maps. This study highlights the need for purpose-oriented landslide susceptibility mapping and posterior assessment of the predictive capabilities of these maps aiming to fulfil respective purposes.