Transportation Infrastructure Management Research Articles

Statistical and machine learning models for predicting spalling in CRCP

Continuously reinforced concrete pavement (CRCP), crucial for the resilience of transportation infrastructure owing to its continuous steel reinforcement, confronts a critical challenge in the form of spalling—a distress phenomenon posing a threat to pavement durability and overall structural integrity. The detachment or breakage of concrete from the surface compromises CRCP’s functionality and raises safety concerns and escalating maintenance costs. To address this pressing issue, our study investigates the multifaceted factors influencing spalling, employing a comprehensive approach that integrates statistical and machine learning techniques for predictive modeling. Descriptive statistics meticulously profile the dataset, emphasizing age, thickness, precipitation, temperature, and traffic parameters. Regression analysis unveils key relationships, emphasizing the significance of age, annual temperature, annual precipitation, maximum humidity, and the initial International Roughness Index (IRI) as influential factors. The correlation matrix heatmap guides feature selection, elucidating intricate interdependencies. Simultaneously, feature importance analysis identifies age, Average Annual Daily Traffic (AADT), and total pavement thickness as crucial contributors to spalling. In machine learning, adopting models, including Gaussian Process Regression and ensemble tree models, is grounded in their diverse capabilities and suitability for the complex task at hand. Their varying predictive accuracies underscore the importance of judicious model selection. This research advances pavement engineering practices by offering nuanced insights into factors influencing spalling in CRCP, refining our understanding of spalling influences. Consequently, the study not only opens avenues for developing improved predictive methodologies but also enhances the durability of CRCP infrastructure, addressing broader implications for informed decision-making in transportation infrastructure management. The selection of Gaussian Process Regression and ensemble tree models stems from their adaptability to capture intricate relationships within the dataset, and their comparative performance provides valuable insights into the diverse predictive capabilities of these models in the context of CRCP spalling.

Transport infrastructure: Adaptive choice modelling for deliberative appraisal judgments

Political representation is responsible for choices regarding the supply and the management of transport infrastructure, but its decisions are sometimes in conflict with the will and the general interest expressed by citizens. This situation has progressively prompted the use of specific corrective measures in order to obtain socially sustainable decisions, such as the deliberative procedures for the appraisal of public goods. The standard Stated Choice Modelling Technique (SCMT) can be used to estimate the community appreciation for public goods such as transport infrastructure; but the application of the SCMT in its standard form would be inadequate to provide an estimation that expresses the general interest of the affected community. Hence the need to adapt the standard SCMT on the basis of the operational conditions imposed by deliberative appraisal procedures. Therefore, the general aim of the paper is to outline the basic conditions on which a modified SCMT with deliberative procedure can be set up. Firstly, the elements of the standard SCMT on which to make the necessary adjustments are identified; subsequently, modifications and additions to make to the standard technique are indicated; finally, the contents of an extensive program of experimentation are outlined.

Ship Lock Extraction from High-Resolution Remote Sensing Images Based on Fuzzy Theory and Prior Knowledge

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area.

Monitoring the Corrosion Resistance of Cold-Spray Coatings with Membrane-Based Sensors

Ensuring the management of gas transportation infrastructure requires the monitoring of internal corrosion within pipelines. The utilization of membrane-based electrochemical sensors is a promising advancement in corrosion risk monitoring. These sensors demonstrate an ability to operate within humidified gas environments, previously inaccessible to conventional electrochemical monitoring methods. Simultaneously, ongoing research is delving into novel sacrificial coatings and application methods to prolong the lifespan of existing pipeline structures and minimize associated repair costs. In this context, we broaden the application of membrane-based electrochemical sensors to investigate the response of electrodes equipped with cold-spray coatings. This examination encompasses a range of fluids exhibiting varying levels of corrosivity pertinent to natural gas pipelines. Multiple sensors with 316 stainless steel working electrodes were coated with cold spray sacrificial coating then exposed to humidified gases with differing relative humidities (RH) (5 %, 50 %, and 95 %) and condensed phases with differing salt content (deionized water and 0.1 mol kg-1 NaCl solution). Three electrochemical techniques (potentiometry, impedance spectroscopy and potentiodynamic scans) were used to monitor the corrosive environment and probe the extent of coating coverage. For the conditions studied, impedance values were extremely sensitive to changes in water content through changes in the membrane resistance (from ~1 kΩ in deionized water to ~5 MΩ with 5 % RH). Polarization resistances (Rp) consistently decreased with increasing corrosivity (~3 kΩ in deionized water to 15 GΩ with 5 % RH). Scanning electron microscopy and energy dispersive X-ray spectroscopy were used to identify the extent to which corrosion products permeated the membrane after major corrosion events.

Impacts of connected autonomous vehicle platoon breakdown on highway

Connected Autonomous Vehicles (CAVs) are seen as an opportunity to improve traffic efficiency and safety. However, further studies are needed to prove such positive effects. Particularly, there is a lack of quantitative research on the impacts of CAV breakdown in mixed traffic flow conditions (i.e., CAVs and Human Driven Vehicles, HDVs). The aim of this research is to explore the impacts generated by the breakdown of the leading vehicle of a CAV platoon in mixed traffic conditions, which in turn generates CAV platoon breakdown (i.e., platoon dispersion), by considering several CAV market penetration rates (MPRs) and platoon size. In this perspective, a control algorithm (“avoidance algorithm”) for modelling CAVs and HDVs behaviour to avoid obstacles (i.e., breakdown CAV platoon) has been developed and tested on some simulation scenarios in order to derive key traffic flow parameters. The dynamic characteristics of traffic flow with CAV platoon breakdown have been explored for both low and high traffic flow volumes. Finally, potential conflicts, congestions, as well as energy consumption and CO2 emissions resulting from the breakdown of CAV platoons in mixed traffic streams have been assessed and discussed. Results suggest that (1) CAV platoon breakdown can reduce the traffic capacity by about 20 %; (2) higher CAV MPRs are more suitable for enhancing highway safety even in breakdown conditions, decreasing energy consumption, and reducing CO2 emissions; (3) platoon size should be limited to 4, since larger sizes affect traffic safety, increases vehicle average delay time as well as energy consumption. The obtained results provide useful insights for transportation planners and transport infrastructure management companies to design and apply policies aimed at improving driving conditions, traffic quality, and safety on highways.

Enclave urbanism and urban growth complications in sub-Saharan Africa: The Lagos scenario of enclave infrastructure provision and management

ABSTRACT In the last few decades, cities in sub-Sahara Africa (SSA) have experienced a rise in the construction of premium residential enclaves in response to infrastructure deficits, perceived insecurity, overcrowding and “chaos.” Residential enclaves represent an urban ideal that is very distant from existing towns and cities in the Global South, aiming to match the Global North’s urban conditions. Splintering infrastructure and service provision are some of the main features of this development. A perspective from within these urban enclaves is so far mainly missing. In particular, the intricacies of infrastructure provision and management in urban enclaves in the Global South have not been examined in-depth and in context-specific ways. This paper analyses electricity, transport and sewage infrastructure provision and management in three qualitative case studies of urban enclaves in Lagos, focusing on a stakeholder perspective. The findings show scenarios and outcomes of largely self-provisioning of infrastructure across the enclaves, depending on the local governance/management arrangements. Achieving autonomy from infrastructure deficits appears as an ongoing process linked to residents increasingly questioning their relationship with the rest of the city.

A pyramid auxiliary supervised U-Net model for road crack detection with dual-attention mechanism

The application of road crack detection technology plays a pivotal role in the domain of transportation infrastructure management. However, the diversity of crack morphologies within images and the complexity of background noise still pose significant challenges to automated detection technologies. This necessitates that deep learning models possess more precise feature extraction capabilities and resistance to noise interference. In this paper, we propose a pyramid auxiliary supervised U-Net model with Dual-Attention mechanism. Pyramid auxiliary supervision module is integrated into the U-Net model, alleviating information loss at the encoder end due to pooling operations, thereby enhancing its global perception capability. Besides, within dual-attention module, our model learns crucial segmentation features both at the pixel and channel levels. These enable our model to avoid noise interference and achieve a higher level of precision in crack pixel segmentation. To substantiate the superiority and generalizability of our model, we conducted a comprehensive performance evaluation using public datasets. The experimental results indicate that our model surpasses current great methods. Additionally, we performed ablation studies to confirm the efficacy of the proposed modules.

Road Transport Efficiency and Organizational Performance: Does Traffic Infrastructure Management Matter?

The objective of this study is to assess the relationships between road transport efficiency and organizational performance. In addition, the investigation explored the intermediary function of traffic infrastructure management in the relationship between road transport efficiency and organizational performance. A study model was built, drawing on a resource-based approach and stakeholder theories, to investigate the interactions between the constructs. The data was collected from a sample of 30 logistics enterprises located in Ghana. The research model underwent analysis utilizing the partial least squares structural equation modeling technique. The results of the study indicate that there is a strong and statistically significant relationship between road transport efficiency and organizational performance. Once more, the analysis has demonstrated a favorable impact of traffic infrastructure management on the performance of organizations. Furthermore, the management of transportation infrastructure has shown a noteworthy and substantial role in mediating the impact on organizational performance.

A Supervised Machine Learning Monitoring System for Vehicle-Railway Bridge Collision

Vehicle collisions on bridges is an important issue for the transportation infrastructure management. This study explores the significance of bridge monitoring and the benefits of employing machine learning (ML) techniques to detect and classify vehicle-deck collisions on railway bridges. The ultimate goal is to transition from traditional bridge monitoring methods to a real-time monitoring system based on a machine learning approach, aiming to improve efficiency and accuracy in detecting bridge issues. Multiple supervised ML algorithms are evaluated to identify the most accurate model for collision detection and signal categorization. The selected ML model employs a distributed approach, enhancing its adaptability and integration into a comprehensive monitoring system for diverse bridge structures. The dataset comprises frequency, velocity, and displacement measurements collected over a one-year monitoring period from three distinct railway bridges. Additionally, a controlled experiment was conducted to identify signal patterns associated with collisions of different energy levels. The collected data underwent rigorous processing, including data cleaning, synchronization, pattern identification, and statistical analysis, to extract relevant features. The proposed model achieved an accuracy of 100% in detecting vehicle-deck collisions on railway bridges and demonstrated high accuracy in classifying other types of signals. The model provides bridge managers with a valuable digital decision support tool that aids in evaluating bridge conditions, minimizing maintenance costs, and ensuring train user safety. Furthermore, the developed approach aids in reducing disk storage and saving energy in embedded systems, enhancing its practicality and sustainability in real-world applications.

A Comprehensive Review of Life Cycle Cost Assessment of Recycled Materials in Asphalt Pavements Rehabilitation

This paper provides a comprehensive review of the use of life cycle cost assessment (LCCA) and life cycle assessment (LCA) methods for evaluating the sustainability and costs of using recycled materials in asphalt pavement rehabilitation projects. The review begins with an introduction to pavement rehabilitation strategies and the importance of choosing techniques based on thorough engineering and economic analyses. It then explores the different types of recycled materials that can be utilized, including reclaimed asphalt pavement, recycled concrete aggregate, and recycled asphalt shingles, discussing the key characteristics and properties of these materials based on previous laboratory studies. The review also examines the various rehabilitation methods that employ recycled content, such as cold in-place recycling, hot in-place recycling, and full-depth reclamation, providing a detailed breakdown of the construction, maintenance, and rehabilitation costs considered in LCCA and analyzing the environmental benefits of recycled material usage through a review of LCA techniques and criteria like carbon footprint reduction, impacts on air and water quality, and considerations of technological factors. Software tools for conducting LCA are compared and challenges to advancing the adoption of recycled materials are reviewed along with directions for future research efforts. The unique contribution of this work is its holistic assessment of LCCA and LCA methodologies to inform the sustainable and cost-effective deployment of recycled materials in asphalt pavement rehabilitation, a topic of growing importance for transportation infrastructure management. In summary, this current work provides a valuable review of how LCCA and LCA methodologies can assess the sustainability and costs of employing recycled content in asphalt pavement rehabilitation projects.

Summary of Research on Highway Bridge Vehicle Force Identification

Vehicle force identification is one of the core technical problems to be solved urgently in the management of transportation infrastructure, and it has also been a research hotspot in recent years. To promote the application of vehicle force identification technology on bridges and explore its development direction, the development status of indirect vehicle force identification methods based on bridge response is reviewed during this study. The basic theories of two major methods, including bridge weigh-in-motion (BWIM) and moving force identification (MFI), are described in detail in this study, and then, the key technical principles of bridge force identification are revealed. Secondly, the development status of BWIM in recent years is reviewed from three aspects, including test accuracy, applicability and test efficiency. Combined with a variety of theories, the current status of MFI is analyzed from the establishment of the solution to the equation. Finally, the development direction of an artificial neural network and machine vision technology are prospected in this study. The BP neural network has good self-learning ability and self-adaptive ability, but the algorithm needs to be improved. The identification method based on machine vision represents the current development direction in vehicle force identification, with great potential.

THE STRATEGIC PLANNING OF TRANSPORT INFRASTRUCTURE AND MANAGEMENT OF LOGISTICS SOLUTIONS IN CONDITIONS OF WAR

This article examines the challenges faced by Ukraine, which is embroiled in a military conflict within its borders, particularly the imperative to ensure the efficiency of its transport infrastructure and logistics processes. It has been identified that military actions result in damage to transport networks, restricted access to critical resources, and alterations in logistics operations. It has been established that in wartime conditions, strategic planning of transport infrastructure and effective management of logistics solutions become particularly pertinent as they dictate the country's capacity to meet its own needs and accomplish strategic objectives. Furthermore, it has been determined that Ukraine's future integration into Europe necessitates reforming its transport and logistics system to align with EU standards. Strategic approaches to restoring the transport network and addressing logistics challenges during conflict are discussed, primarily focusing on alternative routes, the development of effective supply chain management systems, and enhancing system redundancy. Strategies for adaptation and response to changes aimed at ensuring the reliability and efficiency of transport and logistics processes in Ukraine have been explored.

Assessing climate change vulnerability of coastal roads

Climate change is a global phenomenon, which affects in several ways different regions all around the world, beyond the rise in global temperature. Among the different climate change issues, the management of transport infrastructures is crucial. Particularly, their vulnerability against changes in climatic conditions should be assessed. Vulnerability indicators are based on the IPCC concept of vulnerability and can be defined as a function of Climate Exposure, Sensitivity, and Adaptive Capacity. These dimensions need to be addressed during the assessment making and can be modelled as a Multiple Criteria Decision Analysis (MCDA) problem. This study proposes an integrated approach of several MCDA methods as a possible tool for ranking the climate change vulnerability of coastal roads in Malta. The application covers six coastal roads in the islands of Malta, classified by three different MCDA methods. The results indicate that the proposed approach can produce a consistent ranking of the climate change vulnerability of coastal roads. The study provides policy and decision-makers with a definition of a coastal road, an inventory of such roads, a list of climate change impacts, and a mathematical model incorporating climate change vulnerability indicators. The model can be used to prioritize investment and plan climate change adaptation strategies for infrastructural works on coastal roads.

Ultra-low detection limit self-sensing nanocomposites with self-assembled conductive microsphere arrays for asphalt pavement health monitoring

Accurately monitoring micro-level strain within pavements poses a significant challenge in contemporary road engineering, limiting the effectiveness of early-stage pavement health diagnostics. Addressing this critical gap, this study introduces an innovative ultra-low detection limit sensor, specifically designed for intricate pavement health monitoring. It pivots on the unique structural design of a well-ordered 3D conductive network, where the self-assembly effect of microsphere arrays ingeniously mitigates the prevalent issue of nanofiller agglomeration within polymer matrices. The study highlights the significant role of carbon nanotubes (CNTs) length in shaping the structure of conductive networks, revealing that while longer CNTs lead to less uniform microsphere assemblies, they significantly enhance the bridging effect crucial for conductive pathways. To balance these traits, surface chemical modifications were strategically applied, effectively enhancing microsphere organization and dispersion, thereby elevating the conductive and sensing performance. The optimized sensor stands out for the ultra-low detection limit of 0.0005% (5 uε), significantly surpassing those of current strain sensors. Moreover, it exhibits remarkable sensitivity, evidenced by a gauge factor of 13.2, along with swift response and recovery times of 19 ms and 26 ms respectively, and impressive durability, enduring over 100,000 loading-unloading cycles. Field tests under diverse vehicular loads and speeds further validate the sensor's accuracy in capturing dynamic responses within pavement structures, a key factor in proactive pavement maintenance and management. Hence, this advancement shows promise in contributing to the development of safer and more resilient road networks, playing a beneficial role in the significant progress of intelligent transportation and sustainable infrastructure management.

Road transport infrastructure and supply chain asset management: evidence from manufacturing firms in the Sub-Saharan African market

Road transport infrastructure and supply chain asset management: evidence from manufacturing firms in the Sub-Saharan African market

Transport Infrastructure Management Based on LiDAR Synthetic Data: A Deep Learning Approach with a ROADSENSE Simulator

Transport Infrastructure Management Based on LiDAR Synthetic Data: A Deep Learning Approach with a ROADSENSE Simulator

Managing digital transport infrastructure of a region

The socio-economic situation in the world and the Russian Federation in particular is characterized by increasing mobility in all spheres of public life and the expansion of economic ties. To maintain its competitive advantages, a territory should ensure effective communication lines and a highly developed transport system. The paper investigates the issues of transport infrastructure management in Russia at regional level. The theoretical framework includes the concepts of public management and digital transformation, as well as spatial development theory. The methods of synthesis, expert assessment, and min-max normalization were used. Statistical data were retrieved from the Russian Federal State Statistics Service (Rosstat) for 2016–2021. It was found that the current methodologies for assessing transport infrastructure are not in line with the global trends in the social development, including digital transformation. In the paper, we come up with a new methodology for assessing digital transport infrastructure (DTI) which suggests calculating a composite index of the region’s DTI based on four subindices: transport, education, information and communication, and innovation. The study provides composite indices for Russia’s all federal districts for the period from 2016 to 2021 indicating that the regions are seriously lagging behind the center, and the development level of innovation, ICT and education is insufficient.

Simulation modeling of infrastructure objects as a management tool of intelligent transport system

Nowadays, the formation of intelligent transportation systems is taking place, allowing to increase the efficiency of transportation through the use of innovative technologies. Methods and means of simulation modeling have gained considerable popularity among them, allowing to increase the accuracy of forecasting and efficiency of management decisionmaking. The purpose of this article is to develop the directions of application of simulation modeling in the field of passenger transport infrastructure management. The article presents the results of applied research using simulation modeling tools of passenger transport infrastructure objects. Based on these results, the effects of using simulation modeling methods in the management of passenger transport system are systematized. In order to achieve the research goal, a review of scientific works was conducted, the areas of application of simulation modeling in the management of passenger transport infrastructure were specified, the effects of using simulation modeling methods and tools were analyzed and generalized. The results of the research can be useful to transport organizations, state regulation bodies on transport and organizers of transportation. They can be used in the construction of scenarios for the development of transport infrastructure objects to improve the quality of service to the population and to ensure the effectiveness of management decisions on transport.

РОЛЬ ГЧП В ФОРМИРОВАНИИ ИНВЕСТИЦИОННОЙ ПРИВЛЕКАТЕЛЬНОСТИ КРЫМА

The current investment policy of the Republic of Crimea is analysed in the article. The authors evaluate the regulatory and legal framework that contributes to enhancing investment attrac-tiveness of the region. The results of the National investment climate rating of the Russian Federation constituent entities indicate that Crimea is quite attractive for investors, despite the international envi-ronment (over the past 84 years the Republic of Crimea has risen from 5th to 76th rating position). Ac-cording to the Investment Portal of the Republic of Crimea, the most popular areas with private investors are tourism, construction and agriculture. However, there is still a need for financing of such socially important areas as infrastructure and ecology. After assessing the implemented investment projects, it was revealed that the projects prove to be relatively financially efficient but it is imperative to ensure that they are more in line with the needs of the local population. The authors consider the mechanism of public-private partnership (PPP) as a tool to reconcile these issues. Only PPP can provide such advantages for investors as reduced risks and state guarantees, including guarantees of return on investment, for achieving not only economic but also social effects. The government, in its turn, obtain an expensive infrastructure facility without huge budget spending. The priority areas for PPP project execution in Cri-mea are water supply and wastewater disposal, road transport infrastructure and solid waste manage-ment, concession agreement is the main form of their implementation. The results of the ranking of the Russian Federation constituent entities by level of the public-private partnership development and the Investment climate rating show that PPP development in Crimea is at a rather low level (XNUMXth place), nevertheless the region has significant potential. In terms of the state of the regulatory environment the Republic is ahead of St. Petersburg, one of the traditional leaders. The position of Crimea in the ranking can be explained by the lack of implemented projects. The authors make an attempt to identify factors, hindering the development of PPP in the Republic of Crimea, and suggest possible solutions.

Transformation of the multilevel system of transport infrastructure project management

The article is devoted to the development of theoretical and methodological provisions aimed at systemic transformation of the organization of a multi-level system of project management of transport infrastructure. The authors propose to use the method of multi-parametric assessment for groups of projects aimed at the development of transport infrastructure based on economic and environmental indicators, including determining the level of efficiency of the socio-innovative potential of transport, taking into account the degree of possible risks. The significance of the practical component of the study is to determine a set of conditions for the possible practical use of the methodology for managing groups of projects in the field of transport infrastructure considering the effectiveness of the implementation of the tasks and goals laid down in them. The method of organization of project management makes it possible to optimize the number of unrealized groups of projects in a multiple way, to carry out the procedure of ordering and multifactorial selection in the context of each hierarchical management level. The reliability of the results obtained was confirmed by the implementation of projects in the field of transport in Moscow, as well as the use of various mathematical methods.