Critical Transportation Infrastructure Research Articles

Predicting the hydraulic response of critical transport infrastructures during extreme flood events

Understanding the effects of extreme floods on critical infrastructures such as bridges is paramount for ensuring safety and resilient design in the face of climate change and extreme events. This study develops robust computational and predictive modeling tools for assessing the impacts of extreme floods on the hydraulic response and structural resilience of bridges. A computational fluid dynamic (CFD) model utilizing RANS equations and k-ω Shear Stress Transport (SST) for simulating supercritical flows is adopted to compute hydrodynamic pressures and the water levels on bridge piers of cylindrical and rectangular shapes during a flood event. The CFD model is validated based on the case study data obtained from the Haj Omran Bridge, built on the Khorramabad River in Iran. The numerical simulations consider hydrological conditions and exclude geotechnical parameters and abutment damages. The numerical results are evaluated based on well-established design guidelines. Machine learning techniques, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized with Grid Search Cross-Validation (GSCV), are adopted to enhance the accuracy of hydrodynamic pressure forecasting at bridge piers. The XGBoost model exhibits superior performance (R2 = 0.908, RMSE = 0.0279, and E = 3.41%) compared to the RF and SVR models. All the estimated pressure data by XGBoost falls within ±6 percent error lines, highlighting the model's robustness for out-of-range hydrodynamic pressure prediction. Additionally, an optimized Long Short-Term Memory (LSTM) model is adopted to effectively predict free surface flow profiles (i.e. flood depth) over the bridge (R2 = 0.937 and RMSE = 0.083), demonstrating its potential for practical applications of flood depth predictions over bridge infrastructures. The proposed methodological framework outlined in this study can facilitate modeling the impacts of extreme floods on bridges, enabling robust climate resilience assessment of critical infrastructures.

THE EFFECTS OF ENVIRONMENTAL PECULIARITIES ON TRANSPORTATION INFRASTRUCTURE PERFORMANCE IN LAGOS METROPOLIS, NIGERIA: RESIDENTS' EXPERIENCES

Efficient transportation infrastructure is pivotal for the seamless functioning of global cities, with coastal cities facing unique challenges due to environmental peculiarities. This study delves into how Lagos' environmental peculiarities impact transportation infrastructure performance, shedding light on both positive and negative implications. A comprehensive survey involving 1284 residents within Lagos Metropolis was conducted using multistage sampling techniques. A combination of descriptive (percentage and mean-weighted analysis) and inferential (Fisher’s exact test and Phi Cramer's V Test) statistics was adopted for data analysis. Major findings revealed that the environmental peculiarities in Lagos foster the use of multimodal infrastructure options and concurrently exert adverse effects on various facets of transportation infrastructure performance, including travel cost, commuting time, fare charges, wear and tear of infrastructure, and the overall cost of maintenance and repairs. The results of the Fisher’s exact test underscore the undeniable impact of Lagos' environmental peculiarities on transportation infrastructure performance (p = 0.000 < 0.05). This study concludes that high water tables, as a representative environmental peculiarity, substantially influence transportation infrastructure performance, shaping the mobility needs of residents. In light of these findings, the study advocates for the full implementation of sustainable infrastructure solutions. Recommendations include establishing robust drainage systems, incorporating corrosion-resistant materials, and adopting innovative technologies to mitigate the repercussions of rising sea levels on critical transportation infrastructure, enhancing its resilience in the face of environmental challenges.

Adaptive Pathways Using Emerging Technologies: Applications for Critical Transportation Infrastructure

Hazards are becoming more frequent and disturbing the built environment; this issue underpins the emergence of resilience-based engineering. Adaptive pathways (APs) were recently introduced to help flexible and dynamic decision making and adaptive management. Especially under the climate change challenge, APs can account for stressors occurring incrementally or cumulatively and for amplified-hazard scenarios. Continuous records from structural health monitoring (SHM) paired with emerging technologies such as machine learning and artificial intelligence can increase the reliability of measurements and predictions. Thus, emerging technologies can play a crucial role in developing APs through the lifetimes of critical infrastructure. This article contributes to the state of the art by the following four ameliorations. First, the APs are applied to the critical transportation infrastructure (CTI) for the first time. Second, an enhanced and smart AP framework for CTI is proposed; this benefits from the resilience and sustainability of emerging technologies to reduce uncertainties. Third, this innovative framework is assisted by continuous infrastructure performance assessment, which relies on continuous monitoring and mitigation measures that are implemented when needed. Next, it explores the impact of emerging technologies on structural health monitoring (SHM) and their role in enhancing resilience and adaptation by providing updated information. It also demonstrates the flexibility of monitoring systems in evolving conditions and the employment of AI techniques to manage pathways. Finally, the framework is applied to the Hollandse bridge, considering climate-change risks. The study delves into the performance, mitigation measures, and lessons learned during the life cycle of the asset.

Uncertainty in flood risk assessment of linear structures: Why correlation matters

Estimation of the risk posed by inland fluvial floods to critical linear transportation infrastructures requires quantifying the damage inflicted by flooding on roads and railways. The estimated risk is often quantified in terms of the Expected Annual Damage [EAD] of the given linear structure. The aim of this article is to shed light on the estimation of this number and its uncertainty. The damage function models, which describe the degree of damage given flood intensity, represent a source of uncertainty. In this article we argue that this uncertainty needs to be embedded with a correlation structure in order to be evaluated for aggregated values. To this end, a novel methodology based on the use of Gaussian Processes to model the uncertainty associated with the damage function is introduced. Assuming a spatial correlation structure, parameterized by the so called decorrelation length scale, this framework is applied to estimate the EAD to roads in Portugal. The study shows that the application of an appropriate decorrelation length scale is decisive to the estimated uncertainty of the EAD. The proposed methodology may also be applicable to other types of hazards, if represented by some hazard intensity parameter associated with a damage function.

Point cloud generation for critical transportation infrastructure through Bézier curve

While Light Detection and Ranging (LiDAR)-based sensors exhibit considerable potential for transportation infrastructure management, not all infrastructure elements can be comprehensively captured by point clouds, leading to the formation of undesirable “holes” due to both temporary and permanent occlusions. It is imperative to devise mechanisms for identifying and predicting the missing data within these “holes” to ensure the continuous acquisition of critical inventory information. This paper describes a method for generating point clouds based on Bézier curves, which effectively fills the voids within the infrastructure. This method comprises three integral processes, including angle-based boundary detection, identification of principal elevation change, and Bézier curve-based hole filling. The method demonstrates promising results on different roadside surfaces and at different ranges of scales of “holes”. Case studies on the sidewalk, and sound barrier inventories show that the proposed method can significantly improve the quality of the point cloud data for subsequent measurements.

"Ankara Batıkent Mesa Bölgesinde Zemin Deformasyon Kontrolü: Metro Hattı Kontrolü

Subway systems in large cities play a crucial role in enhancing the sustainability and livability levels of urban areas by offering a solution to heavy traffic and transportation problems. Subway tunnels are critical transportation infrastructures that are constructed to serve residential areas, and thus, they have a direct relationship with residential areas in terms of construction and operation. This study is designed to generate precise data by analyzing ground deformation of metro systems using the Persistent Scatterer (PSI) Interferometry method and utilizing free dataThe Batıkent-Törekent metro line in Ankara is identified with the M3 code. At the location where M3 metro passes, ground deformation has been detected. In the analysis of the relevant location, both ascending and descending Sentinel-1 satellite datasets were used. The StaMPS (Stanford Method for Persistent Scatterers)/PSI (approach was utilized in the study. It was found that the results obtained from the ascending datasets were more sensitive. Observations were made in two different periods using ascending datasets: 2015-2016 and 2017-2022. Deformations were observed in the Line of Sight (LOS) direction in the locations where rail systems and tunnels were present. Eight months after the observation, due to the ground deformation in the area, passenger safety was at risk, and a ground strengthening work was carried out, resulting in a 16-day shutdown of the metro system in this location. The results of this study provide concrete contributions to the literature on the possibility of accurate ground measurement using free data

Analyzing the Connectivity of Combined Statistical Areas in Different Census Regions Using ArcGIS

The study aims to analyze the spatial relationship between critical infrastructure and socioeconomic regions in Texas cities, identify areas at risk, and develop effective emergency response strategies. It also focuses on analyzing the connectivity of Combined Statistical Areas (CSAs) in different census regions through various modes of transportation, identify areas that require infrastructure upgrades, and inform policy decisions aimed at improving transportation infrastructure and increasing connectivity. The goal is to provide a comprehensive understanding of the complex relationship between critical infrastructure, emergency response, and transportation infrastructure in Texas cities, and inform policy decisions aimed at addressing disparities and improving connectivity.

Sustainability of Bridges: Risk Mitigation for Natural Hazards

Bridges serve as essential parts of transportation infrastructure, facilitating the movement of people and goods across rivers, valleys, and other obstacles. However, they are also susceptible to a wide range of natural hazards, including floods, earthquakes, and landslides, which can damage or even collapse these structures, leading to severe economic and human losses. A risk index has been developed to address this issue, which quantifies the likelihood and severity of natural hazards occurring in a specific location. The application of risk indices for natural hazards in bridge management involves a data collection process and mathematical modelling. The data collection process gathers information on bridges’ location, condition, and vulnerability, while mathematical modelling uses the data to assess the risk of natural hazards. Overall, risk indices provide a quantitative measure of the vulnerability of bridges to natural hazards and help to prioritize maintenance and repair activities. Mitigation measures are then evaluated and implemented based on the risk assessment results. By using this tool, the UBMS research group has developed an algorithm for risk assessment which will be essential in the decision-making process, specifically focused on enhancing Fund Optimization, Deterioration Modelling, and Risk Analysis. These developments effectively fulfill the primary objectives associated with addressing and mitigating hazards. This development also helps bridge managers understand the potential threats posed by natural hazards and allocate resources more efficiently to ensure the safety and longevity of critical transportation infrastructure.

A standards-based approach to enhancing interoperability of low-cost industrial IoT flood sensors for transportation system resilience

Transportation infrastructure assets are particularly vulnerable to natural hazards, which are becoming more frequent and severe due to climate change and extreme weather conditions. Floods and flash floods are among the deadliest natural hazards, accounting for 50% of vehicle-related fatalities. This underscores the need for timely transportation flood detection systems adoption. However, current flood detection technologies are inadequate in terms of coverage, speed, geographical specificity, and interoperability, making it difficult for emergency managers to respond effectively to flood events. To address this issue, we propose a high-resolution network of low-cost Industrial Internet of Things (IIoT) sensing devices deployed at flood-prone transportation assets. These sensors collect location-specific data, which is then published in standardized formats, interfaces, and protocols, enabling other systems to generate flood forecasts, nowcasts, and warnings. A framework for Incident Management Systems (IMS) was also discussed to highlight the need for system interoperability during disaster management operations. Our solution employs standards-based interoperability, using the OIIE™ OpenO&M ecosystem architecture, to enable seamless interaction between interdependent systems and manages the risk to critical transportation infrastructure. The technology was tested at a microscale level to evaluate its performance. The model architecture supports scalable systems of systems interoperability for standardized use cases and common asset classes used in transportation, energy, facilities, and other critical infrastructure.

A comparative analysis of University Sustainable Travel Plans – Experience from Australia

Travel Demand Management (TDM) initiatives are widely applied by transport planners to establish and enable appropriate use of critical transport infrastructure. Less attention has been given to the specific case of TDM in an education precinct (university) context. Travel Plans have been promoted as a means for an organisation to encourage sustainable travel choices by their employees, visitors and customers. This paper offers an empirical contribution to the literature through a comparative qualitative evaluation of selected University Sustainable Travel Plans (USTPs) in Australia to identify the most important questions that a USTP should address explicitly. The evaluation comprised identification of a set of evaluation questions, completion of a template for each USTP considered and application of a simple scoring exercise. We also identify TDM measures that have been introduced as part of a USTP in response to the typical travel patterns exhibited in university settings. A contribution of this paper is to create a means of comparison of USTPs and to establish the components of a comprehensive travel plan.

Transportation Networks in the Face of Climate Change Adaptation: A Review of Centrality Measures

This paper presents a comprehensive review of centrality measures and their usefulness in transportation networks in the face of climate change adaptation. The focus is on understanding the importance of transportation nodes in the event of extreme weather events and climate-related disasters that may render them inoperable. The paper argues that if critical nodes can be identified, they can be better protected, while resources can be allocated to ensure their functioning in the event of such events. The paper assesses 17 centrality measures, including degree, closeness, betweenness, eigenvector, and Katz, and evaluates their usefulness and usability in transportation networks. The review highlights the need to reformulate these measures to take into account traffic- and transport-related parameters and variables. Without this reformulation, centrality measures only reveal node importance in a topological or structural way and fail to capture the true significance of the nodes in a transportation network. The reformulation enables the centrality measures to be properly applied in a transportation network and to expose the significance of their elements. This work has important implications for transportation planners and policy-makers in ensuring the resilience of critical transportation infrastructure in the face of climate-related disasters.

Design and Implementation of a Quantitative Risk Assessment Model for UK Road Tunnels

One of the critical transportation infrastructures is road tunnels and fire safety is one of the important aspects of their operation. Interaction between the fire, tunnel users, traffic, and fire safety measures influences fires in road tunnels. Therefore, a complex model is required to analyse the risk and quantify the consequences. In this paper, a novel quantitative risk analysis model developed for UK road tunnels is presented consisting of a quantitative consequence analysis model and a quantitative frequency frequency analysis model. The proposed quantitative consequence analysis model is provided through three sub-models; queue model, distribution model, and egress model. The frequency analysis is via an event tree that takes into account the tunnel fire rate in UK road tunnels. After a brief description of this model, the proposed method is illustrated through a case study of an urban road tunnel. The effect of different emergency ventilation systems on societal risk and sensitivity of the model to pre-movement time, accident frequencies involving Heavy Good Vehicles (HGVs), tenability threshold temperature, and different burning vehicles were studied in this case study.

Automated vision-based post-earthquake safety assessment for bridges using STF-PointRend and EfficientNetB0

Bridges are critical transportation infrastructure in Taiwan that are at high risk of structural damage during major earthquake incidents. The structural health monitoring method currently used to assess bridge safety nationwide is labor-intensive and expensive to implement. In this study, a novel automated bridge safety assessment system was developed to facilitate post-earthquake bridge inspections using symbiotic organism search-transfer learning-PointRend (STF-PointRend) for component and damage type detection, EfficientNetB0 for damage level detection, and earthquake resistance index for safety assessment. In the case studies, the STF-PointRend model obtained good testing results, with global mean Intersection over Union values of 76.31 and 75.47% for bridge-component and damage-type detections. Furthermore, the EfficientNetB0 model obtained an average F1 score of 0.86 for damage-level detection in the testing results. The developed model was used to conduct a safety evaluation of two bridges (Lempenge I and Luk I) that had suffered damage in a 2018 earthquake in Palu, Indonesia. The earthquake-resistance scores of 56.15 and 53.21 respectively earned by the two bridges indicate that both require immediate maintenance.

Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks

Identification of Resilience Dimensions in Critical Transportation Infrastructure Networks

A Vulnerability Assessment Approach for Transportation Networks Subjected to Cyber–Physical Attacks

Transportation networks are fundamental to the efficient and safe functioning of modern societies. In the past, physical and cyber space were treated as isolated environments, resulting in transportation network being considered vulnerable only to threats from the physical space (e.g., natural hazards). The integration of Internet of Things-based wireless sensor networks into the sensing layer of critical transportation infrastructure has resulted in transportation networks becoming susceptible to cyber–physical attacks due to the inherent vulnerabilities of IoT devices. However, current vulnerability assessment methods lack details related to the integration of the cyber and physical space in transportation networks. In this paper, we propose a new vulnerability assessment approach for transportation networks subjected to cyber–physical attacks at the sensing layer. The novelty of the approach used relies on the combination of the physical and cyber space, using a Bayesian network attack graph that enables the probabilistic modelling of vulnerability states in both spaces. A new probability indicator is proposed to enable the assignment of probability scores to vulnerability states, considering different attacker profile characteristics and control barriers. A probability-based ranking table is developed that details the most vulnerable nodes of the graph. The vulnerability of the transportation network is measured as a drop in network efficiency after the removal of the highest probability-based ranked nodes. We demonstrate the application of the approach by studying the vulnerability of a transportation network case study to a cyber–physical attack at the sensing layer. Monte Carlo simulations and sensitivity analysis are performed as methods to evaluate the results. The results indicate that the vulnerability of the transportation network depends to a large extent on the successful exploitation of vulnerabilities, both in the cyber and physical space. Additionally, we demonstrate the usefulness of the proposed approach by comparing the results with other currently available methods. The approach is of interest to stakeholders who are attempting to incorporate the cyber domain into the vulnerability assessment procedures of their system.

Development of a Novel Quantitative Risk Assessment Tool for UK Road Tunnels

Some of the most critical transportation infrastructures are road tunnels. Underground passageways for motorists are provided through this cost-effective engineering solution, which allows for high traffic volumes. A crucial aspect of the operation of road tunnels is fire safety. Risk assessments have been established to ensure the level of safety in tunnels. As the existing quantitative risk analysis (QRA) models are inapplicable to assess the fire risk in UK road tunnels, this paper presents a novel QRA model, named LBAQRAMo, for UK road tunnels. This model consists of two main sections: quantitative frequency analysis, to estimate the frequency of fire incidents via an event tree; and quantitative consequences analysis, to model the consequences of fire incidents. LBAQRAMo covers the risk to tunnel users. The result of the risk analysis is the expected value of the societal risk of the investigated tunnel, presented via F/N curve. Another major result of this model is the estimation of the number of fatalities for each scenario based on the comparison between required safe egress time (RSET) and available safe egress time (ASET). Risk evaluation was carried out by comparison of the tunnel under study with the UK ALARP limit. The operation of the model is demonstrated by its application to the Gibraltar Airport Tunnel as a case study. Simulation of 34 different possible scenarios show that the tunnel is safe for use. The sensitivity of the model to HGV fire incident frequency and basic pre-movement times was studied as well.

Cross-border critical transportation infrastructure: a multi-level index for resilience assessment

Today, more than ever before, our society depends on interdependent infrastructure systems, such as transportation, energy, water, and telecommunications networks. These systems are often considered critical because they are necessary for the organization, functionality, and stability of a modern industrialized country. However, these infrastructures are vulnerable to accidents, malicious failures, and disruptions that could generate consequences impacting on the economy, health, safety, and welfare of the citizens of a country or of several neighboring countries. The disruption of critical cross-border transportation infrastructure, road or rail, as a result of a major event can affect the area where the event occurs and a wider area. Depending on the type and duration of an event, which can be natural or anthropogenic in origin, it is possible to estimate the impacts on the mobility of people and goods in terms of delays (alternative routes), increased traffic (congestion), and a potential increase in accidents. For instance, in 2019 there was an accident in Rastatt (Germany) that affected rail traffic on the Karlsruhe-Basel line of the Rhine-Alpine corridor in Europe. The rail line was disrupted for more than 50 days, causing disservices and about 2 billion Euro in economic losses in Germany, Switzerland, and Italy. The extended disruption of road and rail sections can have consequences (impacts) not only on the transport system but also on the socio-economic system in a macro-regional context. The research is part of the SICt project - Resilience of Critical Cross-Border Infrastructure developed in the Interreg VA Italy-Switzerland Programme 2014-2020. The work aims to define a RI - Resilience Index for the road and rail transport network falling within the study area. The RI index describes the capability of each network element (i-th link) to cope with a relevant event. The formulation of the index involves the calculation of three independent indicators: i) RIRM - Rescue Management related to the resources that can be activated and used to cope with an event; ii) RIPP - Plans & Management related to the speed with which the necessary resources can be activated and in fact, considers management aspects such as the presence of plans and procedures; iii) RIRN - Network & Traffic related to the robustness of the elements of the transport network. This work aims to present the proposed model and its application to the project area that includes the Lombardy Region (Italy) and the Canton Ticino (Switzerland) within the SICt Project.

The Basis for Strengthening Organisational Resilience of Critical Transport Infrastructure Entities

As a result of the increase in threats and the growing severity of their impact, there is a need to place increasing emphasis on the protection of critical infrastructure. Transport critical infrastructure is one of the indispensable sectors for the proper functioning of the internal market and the realisation of long-term strategic objectives, especially in the area of competitiveness. Thus, in an effort to minimize the emergence or impact of threats to this type of critical infrastructure, the issue of resilience is coming to the fore. The concept of strengthening resilience represents one of the possible approaches that can be used not only to protect elements of critical transport infrastructure, but also to protect the entities that own or operate these elements. For this reason, the aim of the article is to present the starting points for strengthening organizational resilience that can be applied in the transport critical infrastructure sector.

ON THE DEVELOPMENT OF MANAGEMENT MODELS FOR REGIONAL PROGRAMS OF ENVIRONMENTALLY SAFE OPERATION AT CRITICAL TRANSPORT INFRASTRUCTURE FACILITIES

The objects of the critical transport infrastructure are located in all regions of Ukraine, and the question of the safety of these objects is extremely relevant. A functional approach should be used to form an effective safety management unit for critical transport objects. Therefore, in order to achieve an acceptable level of safety of the critical transport infrastructure, it is necessary to have an effective mechanism for achieving this result, which can be achieved through the formation and efficient management of regional programs for the safe operation of critical transport infrastructure objects. Management models for regional safety programs at critical transport infrastructure facilities based on the existing approaches to construction of models of program and project management are proposed in the article. Critical transport infrastructure includes highways, state-owned transport enterprises, subway facilities, gas stations, bridges, sea and river ports, airports, and pipelines. These facilities are strategic for the state, and as a consequence, vulnerable, so they require special protection. To form an effective apparatus for environmental safety management in critical transport infrastructure facilities, the application of a program approach is proposed in the article. To assess the life cycle of regional environmental safety programs of critical transport infrastructure facilities based on the Deming cycle, a spiral model was developed, which is the environment for the operation of schematic, system, and service models of the environmental safety management program. Development of approaches to the management of regional programs for the environmentally safe operation of critical transport infrastructure facilities, based on the formation of strategic objectives and their decomposition, will be aimed not only at solving existing problems of critical transport infrastructure in the region but factors related to the occurrence of dangerous events for them and the elimination of the causes leading to these problems. A system model for managing regional safety programs for objects of critical transport infrastructure is proposed.

High-speed rail effects on station area-level business commercial agglomeration: Evidence from 110 stations in China

As a critical transportation infrastructure, high-speed rail (HSR) greatly enhances accessibility and shortens the spatial-temporal distance among cities. It is well documented that HSR significantly impacts regions and cities’ economic development and spatial structure. The proportion and frequency of business passenger trips are increasing yearly, and the demand for “station as the final destination” is becoming more and more prominent. Given the pivotal role of the design and construction of HSR station areas in achieving “station as the final destination,” the study of their development characteristics and patterns has important implications for urban planning. Previous studies have focused extensively on the macro impact of the HSR operation on regional economies, urban industries, and tourism development, whereas only a few were conducted at the station level. Furthermore, the business-commercial agglomeration effects of the HSR operation on the development and construction of station areas have neither been studied nor accurately measured. To fill this gap, we first constructed a panel data set consisting of the point of interest (POI) data, China City Statistical Yearbook data, and the HSR station operation data from 2012 to 2017. Next, we developed difference-in-differences (DID) models to decipher the impact of the HSR operation on the station-level business and commercial agglomeration. The results show that the HSR operation has increased the business-commercial agglomeration index (BCAI), the commercial agglomeration index (BAI), and the business agglomeration index (CAI) by 28.3%, 29%, and 21.3%, respectively. In other words, the HSR operation has significant business-commercial agglomeration effects in the station area, and the agglomeration effect size of business is more extensive than that of commerce. Interestingly, the BCAI grew slowly in the first 3 years after the HSR operation but started to rise much faster from the fourth year, which HSR’s catalytic effects can explain. The results also reveal that the business-commercial agglomeration effects have a clear spatial threshold as the BCAI tends to decrease from 1500 m to 3000 m away from HSR stations. The plausibility of the results has been confirmed by the parallel trend, placebo, and robustness tests.