Transportation Network Research Articles

Optimization of reliable vehicle routing problem for medical waste collection with time windows in stochastic transportation networks

The collection and transportation of medical waste (MW) is critical because of environmental and public safety implications. However, the variability in travel times of waste transport vehicles, influenced by various factors, complicates this process. This article proposes a multi-objective optimization model to enhance travel time reliability (TTR) while minimizing vehicle transportation costs and penalties for time window violations. Given the NP-hard nature of the problem, a genetic algorithm enhanced by an improved harmony search genetic algorithm (IHSGA) is utilized. The model and algorithm are evaluated in N City, China. The results highlight the significance of TTR in optimizing MW collection operations. Sensitivity analysis of key parameters validates the model’s robustness. Furthermore, comparative analysis demonstrates the superiority of IHSGA over traditional methods. These findings underscore the importance of incorporating additional costs associated with travel time variability in decision making, and advocate for strategies to mitigate such uncertainties.

Analysis of Decarbonization Potential in Mobility Sector with High Spatial Resolution: Study Case of the Metropolitan Area of Valencia (Spain)

This study addresses the urgent need for sustainable urban mobility through a comprehensive analysis of decarbonization potential in the metropolitan area of Valencia, Spain. The research is motivated by the imperative to mitigate climate change amidst high traffic and congestion levels. Utilizing the Origin–Destination matrix from the Valencian community’s mobility plan, the study prioritizes displacements for substitution by sustainable alternatives. Through a detailed case study analysis, critical areas for decarbonization are identified, and practical strategies to reduce carbon emissions are proposed. The methodology encompasses a thorough literature review on decarbonization measures, examination of existing plans, and analysis of sustainable trips with high spatial resolution using geographic information systems (GISs). The study identifies trips with significant decarbonization potential and assesses the current public transportation network. Spatial analyses illustrate demand patterns for sustainable transport options, emphasizing the need for tailored strategies. Key findings highlight the role of private and commercial transport in CO2 emissions, advocating for targeted interventions to enhance active transport infrastructure, promote carpooling, adopt low-emission vehicles, and improve public transport accessibility and efficiency.

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

Optimal allocation of autonomous PV-powered fast charging stations for electrified transportation considering traffic dynamics

In response to the imperative to reduce greenhouse gas emissions, global efforts are focusing on electrifying transportation and adopting renewable energy sources. This necessitates establishing reliable fast-charging stations (FCSs) for the widespread integration of electric vehicles (EVs). Indeed, the arbitrary distribution of FCSs can detrimentally affect the efficiency of traffic movement. Thus, this paper presents a planning methodology aimed at optimizing the allocation of FCSs within large-scale transportation networks. The energy system proposed includes an autonomous photovoltaic (PV)-powered FCS based on a DC microgrid, supplemented by a stationary battery energy storage system for backup power. The behavior of EVs within the traffic flow is modeled using a realistic traffic simulation software called Dynamic Network Assignment Simulation for Advanced Road Telematics (DYNASMAT). The conflicting objectives of minimizing total annual costs and EV average waiting time at stations are addressed through multi-objective optimization. To solve this optimization problem, the GAMS software is employed. A transportation network with 92 nodes and 284 links is utilized to test the proposed approach. The proposed autonomous PV-powered FCS is compared with Natural Gas-fired Distributed Generators (NGDG)-powered FCS. The simulation results show that while the NGDG-powered FCS exhibits higher emissions, the autonomous PV-powered equivalent operates emission-free. However, the annual cost of the autonomous PV-powered FCS is higher than that of the NGDG-powered FCS.

A Generalised Additive Model and Deep Learning Method for Cross-Validating the North Atlantic Oscillation Index

This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning techniques. The analysis is conducted on an extensive corpora of free text information sourced from media outlets, public companies, government reports, and newspapers. Overall, the result shows that the NAO index has been reproduced well by the Deep-NAO model with a correlation coefficient of 0.74.

Spatiotemporal trends and geographic disparities in spatial accessibility to maternal and child health services in Nanning, China: impact of two-child policies

BackgroundChina’s family planning policies have experienced stages of one-child policy, partial two-child policy, and universal two-child policy. However, the impact of these policy shifts on the spatial accessibility to maternal and child health (MCH) services for women and children remains uncertain. This study aimed to evaluate the spatiotemporal trends and geographic disparities in spatial accessibility to MCH services in the context of two-child polices.MethodsThis study was conducted in Nanning prefecture, China, from 2013 to 2019. Data on the transportation networks, MCH institutes, the annual number of newborns, and the annual number of pregnant women in Nanning prefecture were collected. Gaussian two-step floating catchment area (Ga2SFCA) method was employed to measure the spatial accessibility to MCH services at county, township, and village levels. Temporal trends in spatial accessibility were analyzed using Joinpoint regression analysis. Geographic disparities in spatial accessibility were identified using geographic information system (GIS) mapping techniques.ResultsOverall, the spatial accessibility to MCH services showed an upward trend from 2013 to 2019 at county, town, and village levels, with the average annual percent change (AAPC) being 5.04, 4.73, and 5.39, respectively. Specifically, the spatial accessibility experienced a slight downward trend during the period of partial two-child policy for both parents only children (i.e., 2013–2014), a slight upward trend during the period of partial two-child policy for either parent only child (i.e., 2014–2016) and the early stages of universal two-child policy (i.e., 2016–2018), and a large upward trend in the later stages of universal two-child policy (i.e., 2018–2019). Spatial accessibility to MCH services gradually decreased from central urban areas to surrounding rural areas. Regions with low spatial accessibility were predominantly located in remote rural areas.ConclusionWith the gradual opening of the two-child policies, the spatial accessibility to MCH services for women and children has generally improved. However, significant geographic disparities have persisted throughout the stages of the two-child policies. Comprehensive measures should be considered to improve equity in MCH services for women and children.

ST-TDCN: A two-channel tree-structure spatial–temporal convolutional network model for traffic velocity prediction

In the development of intelligent transport systems which aims to provide safe, convenient, and comfortable transport and effectively addresses issues with traffic congestion and pollution, accurate collection of spatial–temporal data is of great significance. Based on the study of the topology and spatial characteristics of the transportation network, data with spatial–temporal attributed can be predicted by relevant models. Thus, the purpose of providing additional information for decision making and labor cost saving is achieved. This study proposed a more accurate traffic velocity prediction model, named Spatial-Temporal Tree-structure Dual-channel Convolution Network. The model designs a spatial tree convolution module for capturing the spatial features of nodes in the traffic network represented by the tree structure and applies a dual-channel temporal convolutional network module to capture the temporal information of velocity data more accurately. Comparative tests have demonstrated that the model proposed in this study has the advantages of higher accuracy, better convergence and more flexibility in responding to dynamic changes of traffic velocity.

The nexus between economic growth, urbanization, technological advancement, and CO2 emissions: Where does the GCC stand?

Most countries, particularly developing and growing economies, have the primary goal of achieving greater economic growth in order to improve living standards. However, a boost in economic activity raises energy demand, which in turn raises carbon dioxide emissions and deteriorates the environment. This paper investigates the effect of economic growth, energy consumption, and research and development on CO2 emissions in Gulf Council Cooperation (GCC) countries. The study uses the Vector Error Correction Model to investigate the short- and long-run synergy between variables over the period 2000–2022. The results indicate that boosting economic activity requires more energy consumption and will increase CO2 emissions in the long run. Investing in research and development can enhance the quality of the environment, yet the effect of urbanization on CO2 emissions in GCC countries remains unclear. To reduce energy consumption, it is first necessary to expand renewable energy sources and encourage energy-saving techniques. Then, to lower CO2 emissions, the government must promote investments in green technologies, particularly in manufacturing. Third, the government should encourage sustainable urbanization by putting laws in place that encourage the growth of green cities and transportation networks.

Resilience quantification and recovery strategy simulation for urban underground logistics systems under node and link attacks: A case study of Nanjing city

Urban Underground Logistics Systems (UULS) have become an emerging solution to mitigate urban surface traffic congestion, environmental pollution, and surface transport safety risks. However, during the operation of UULS, the use of advanced technologies such as the Internet of Things (IoT) introduces cybersecurity risks to the system. Moreover, severe natural disasters can also cause damage to underground transportation network links. Existing research and planning primarily concentrate on the system design and benefits of UULS, neglecting the system's service level under attack scenarios. This study outlines three representative UULS network prototypes and proposes a resilience quantification method centered on logistics efficiency. It also focuses on comparing the effectiveness of three recovery strategies. These strategies give priority to maximum flow, betweenness centrality, and regional importance, as well as the priority of node and link repairs. The resilience quantification method and recovery strategies are applied in a case study set in Nanjing City. The case study results reveal that the Two-echelon network shows exceptional resilience. Regarding the effectiveness of recovery strategies, the strategy based on maximum flow proves to be the most effective, and focusing on node repair can lead to higher system resilience. Based on these findings, this study offers recommendations to transportation and logistics management decision-makers, focusing on UULS resilience and recovery strategy selection. These recommendations are intended to provide valuable guidance for the planning and design of future UULS, ensuring their resilience and reliability.

Challenges and Opportunities for 2D Materials in Energy Storage Technologies

Efficient energy storage systems represent a critical technology across many sectors including consumer electronics, electrified transportation, and a smart grid accommodating intermittent renewable energy sources. Arguably, the most important advance in energy storage over the past three decades is the lithium-ion battery, which was recognized with the Nobel Prize in Chemistry. However, despite its many successes, issues related to safety, energy density, charging time, and operating temperature range have hindered the realization of the full potential of lithium-ion battery technology, particularly in large-scale applications such as grid-level storage and full electrification of transportation networks. Nanostructured materials were once thought to present compelling opportunities for next-generation lithium-ion batteries, but inherent problems related to high surface area to volume ratios at the nanometer-scale (e.g., undesirable surface chemical interactions between electrodes and electrolytes) have impeded their adoption for commercial applications. This talk will explore how chemically inert two-dimensional (2D) materials are driving a resurgence in nanostructured lithium-ion battery materials [1,2]. For example, conformal graphene coatings on battery anode and cathode powders mitigate surface degradation and minimize the formation of the solid electrolyte interphase, thus improving cycling stability [3,4]. In addition, the high electrical conductivity of graphene reduces cell impedance, resulting in enhanced kinetics that enable high-rate capability and low-temperature performance down to –20 °C [5]. On the other hand, ionogel electrolytes based on ionic liquids and hexagonal boron nitride (hBN) nanoplatelets provide safe, high-rate operation at high temperatures up to 175 °C, which represents the highest operating temperature to date for solid-state lithium-ion batteries [6,7]. The strong interfacial interactions between hBN and ionic liquids further enable novel electrolyte architectures based on layered heterostructure ionogels that result in unprecedently high energy densities and rate performance for solid-state batteries [8]. Since hBN ionogels can be formulated into printable inks and slurries, these electrolytes also present promising opportunities for additive manufacturing of solid-state batteries, supercapacitors, and related energy storage technologies [9-11].[1] D. Lam, et al., ACS Nano, 16, 7144 (2022).[2] N. S. Luu, et al., Accounts of Materials Research, 3, 511 (2022).[3] J.-M. Lim, et al., Matter, 3, 522 (2020).[4] K.-Y. Park, et al., Advanced Energy Materials, 10, 2001216 (2020).[5] K.-Y. Park, et al., Advanced Materials, 34, 2106402 (2022).[6] W. J. Hyun, et al., ACS Nano, 13, 9664 (2019).[7] W. J. Hyun, et al., Advanced Energy Materials, 10, 2002135 (2020).[8] W. J. Hyun, et al., Advanced Materials, 33, 2007864 (2021).[9] C. M. Thomas, et al., ACS Energy Letters, 7, 1558 (2022).[10] W. J. Hyun, et al., Nano Letters, 22, 5372 (2022).[11] L. E. Chaney, et al., Advanced Materials, DOI: 10.1002/adma.202305161 (2023).

Revealing and reconstructing the 3D Li-ion transportation network for superionic poly(ethylene) oxide conductor.

Understanding the Li-ions conduction network and transport dynamics in polymer electrolyte is crucial for developing reliable all-solid-state batteries. In this work, advanced nano- X-ray computed tomography combined with Raman spectroscopy and solid state nuclear magnetic resonance are used to multi-scale qualitatively and quantitatively reveal ion conduction network of poly(ethylene) oxide (PEO)-based electrolyte (from atomic, nano to macroscopic level). With the clear mapping of the microstructural heterogeneities of the polymer segments, aluminium-oxo molecular clusters (AlOC) are used to reconstruct a high-efficient conducting network with high available Li-ions (76.7%) and continuous amorphous domains via the strong supramolecular interactions. Such superionic PEO conductor (PEO-LiTFSI-AlOC) exhibites a molten-like Li-ion conduction behaviour among the whole temperature range and delivers an ionic conductivity of 1.87 × 10-4 S cm-1 at 35 °Ϲ. This further endows Li electrochemical plating/stripping stability under 50 μA cm-2 and 50 μAh cm-2 over 2000 h. The as-built Li|PEO-LiTFSI-AlOC|LiFePO4 full batteries show a high rate performance and a capacity retention more than 90% over 200 cycling at 250 μA cm-2, even enabling a high-loading LiFePO4 cathode of 16.8 mg cm-2 with a specific capacity of 150 mAh g-1 at 50 °Ϲ.

Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours

The scientific and rational assessment of the evolution of node importance in rail transit line networks is important for the sustainability of transportation systems. Based on the complex network theory, this study develops a weighted network model using the Space L method. It first considers the network topology, the mutual influence of neighboring nodes of the transportation system, and the land use intensity in the station influence domain to construct a comprehensive index evaluation system of node importance. It then uses the covariance-weighted principal component analysis algorithm to more comprehensively evaluate the node importance evolution mechanism and analyzes the similarity and difference of the sorting set by adopting three different methods. The interaction mechanism between the distribution of important nodes and the evolution of land use intensity is explored in detail based on the fractal dimension theory. The Xi’an rail transit network is considered an example of qualitative and quantitative analysis. The obtained results show that the importance of nodes varies at different times of the day and the complexity of the morning peak is more prominent. Over time, articulated fragments with significance values greater than 0.5 are formed around the station, which are aligned with the direction of urban development, creating a sustainable mechanism of interaction. As the network’s crucial nodes in the center of gravity increase and the southern network expands, along with the increased intensity of the city’s land utilization, the degree of alignment in evolution becomes increasingly substantial. Different strategies for attaching the network, organized based on the size of Si can lead to the rapid damage of the network (reducing it to 0.2). The identification of crucial nodes highlighted in this paper serves as an effective representation of the functional characteristics of the nodes in transportation networks. The results obtained can provide a reference for the operation and management of metro systems and further promote the sustainable development of transportation networks.

Biological invasions and epidemics with nonlocal diffusion along a line.

The goal of this work is to understand and quantify how a line with nonlocal diffusion given by an integral enhances a reaction-diffusion process occurring in the surrounding plane. This is part of a long term programme where we aim at modelling, in a mathematically rigorous way, the effect of transportation networks on the speed of biological invasions or propagation of epidemics. We prove the existence of a global propagation speed and characterise in terms of the parameters of the system the situations where such a speed is boosted by the presence of the line. In the course of the study we also uncover unexpected regularity properties of the model. On the quantitative side, the two main parameters are the intensity of the diffusion kernel and the characteristic size of its support. One outcome of this work is that the propagation speed will significantly be enhanced even if only one of the two is large, thus broadening the picture that we have already drawn in our previous works on the subject, with local diffusion modelled by a standard Laplacian. We further investigate the role of the other parameters, enlightening some subtle effects due to the interplay between the diffusion in the half plane and that on the line. Lastly, in the context of propagation of epidemics, we also discuss the model where, instead of a diffusion, displacement on the line comes from a pure transport term.

Effect of Ground Motion on Fragility and Vulnerability of Reinforced Concrete Bridge in Different Soil Conditions

Bridges are essential components of transportation networks, serving as lifelines for the movement of people and goods. The bridges' resilience to natural disasters, particularly earthquakes, is an issue of paramount significance. Yet, a critical knowledge gap exists when it comes to understanding how bridges perform in the challenging terrain of medium-density sandy and clay soils during seismic events. This study seeks to bridge that gap by developing fragility curves that quantitatively evaluate the likelihood of bridge damage or failure across varying levels of earthquake magnitudes. The CSI Bridge v25.0.0 was used to simulate earthquake ground motions specifically within medium-density sandy and stiff clayey soils. Nonlinear time history analyses of the bridge were performed by using 5 different earthquake events with PGA ranging from 0.25 – 1.5g. Fragility analysis is performed to develop seismic fragility curves for the critical part of the bridge for various peak ground acceleration (PGA) in two types of soil conditions. The findings revealed that the deck component is more susceptible to damage than the pier. The impact of seismic activity on medium-dense soil is more significant than on stiff clayey soils in relation to the critical components of the bridge. This result enhances the ability to design bridges that can withstand and recover from seismic events more effectively.

Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker’s escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender’s waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By employing this approach and conducting in-depth analyses, the aim is to provide valuable insights into the efficiency and practicality of the developed methods on real-world transportation networks.

Disproportional fraction of inactive components leads to the variation in metabolic scaling

In biological systems, solitary organisms or eusocial groups, the metabolic rate often scales allometrically with systems’ size, when they are inactive, and the scaling becomes nearly isometric when the systems are active. Here I propose a hypothesis attempting to offer a departing point for a general joint understanding of the difference in the scaling powers between inactive and active states. When the system is inactive, there exist inactive components, which consume less energy than the active ones, and the larger the system is, the larger the fraction of the inactive components, which leads to sublinear scaling. When the system is active, most inactive components are activated, which leads to nearly isometric scaling. I hypothesize that the disproportional fraction of the inactive components is caused by the diffusants screening in the complex transportation network. I.e., when metabolites or information diffuses in the system, due to the physical limitation of the network structure and the diffusant’s physical feature, not all the components can equally receive the diffusants so that these components are inactive. Using the mammalian pulmonary system, ant colonies, and other few systems as examples, I discuss how the screening leads to the allometric and isometric metabolic scaling powers in inactive and active states respectively. It is noteworthy that there are a few exceptions, in which the metabolic rate of the system has an isometric scaling relationship with size at rest. I show that these exceptions not only do not disapprove the hypothesis, but actually support it.

Spatial Structure and Vulnerability of Container Shipping Networks: A Case Study in the Beibu Gulf Sea Area

Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea area is a key channel to supporting China’s participation in international economic cooperation in the western region. It is highly susceptible to the influence of the political and economic instability. This study introduces a dual-component framework to analyze the inherent structure and potential vulnerabilities of the container transportation network in the Beibu Gulf Sea areas. The findings show that the core layer of the network exhibited circular solidification characteristics. The entire network heavily relies on some core ports, such as Haiphong Port, Ho Chi Minh Port, and Qinzhou Port, and it highlights the potential increases in vulnerability. The finding shows that deliberate attacks have a greater impact than random attacks on the normal operations of maritime networks. If ports with high intermediary centrality are attacked, the connectivity and transportation efficiency of the Beibu Gulf maritime network will be significantly affected. However, under such circumstances, redistributing cargo transportation through route adjustments can deal with the transmission of cascading failures and maintain the network’s resilience. Based on the existing knowledge and the data collected in a case study, this research stands out as the first to provide a critical examination of the spatial structure and vulnerability of container shipping networks in the Beibu Gulf sea.

Regional earthquake-induced landslide assessments for use in seismic risk analyses of distributed gas infrastructure systems

Earthquake-induced landslides are associated with significant risks to human lives and infrastructure. Spatially distributed infrastructure systems, such as pipelines, power lines, and transportation networks, are at particular risk to seismic landslides due to their large spatial extent. To conduct a comprehensive seismic landslide risk assessment for these systems, there is the need to evaluate the seismic landslide characteristics (i.e., location, size, displacement, direction) on a broad, regional scale. This paper presents a framework for seismic landslide analysis that provides this information for subsequent risk assessments. The approach computes seismic landslide displacements using a sliding block approach while accounting for the uncertainties in the input variables and displacement models using a logic tree. The computed displacements are then aggregated based on geomorphic landforms to define landslide zones. For each landslide zone, the statistical distributions of landslide features, such as landslide size, displacement level, and direction of movement, are defined. These attributes are presented in a format that can be integrated with fragility models for distributed infrastructure systems to quantify risk on a regional scale. The application of the approach is demonstrated through assessments for gas pipeline networks across the state of California in the United States.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

AI based UPQC control technique for power quality optimization of railway transportation systems

Metro trains have non-linear load characteristics, which means that the power sent to them gets distorted. Problems are caused by changes in power, swells, harmonics, and other disturbances. In this research, an artificial intelligence-driven control method was used on a unified power quality conditioner (UPQC) to help reduce power quality problems and improve power quality. Three advanced control methods are built and compared using MATLAB Simulink. Some of these methods are the ANN Controller, the NARMA-L2 Controller, and the PI Controller, improved using the Adaptive Lizard Algorithm. The controls' usefulness is judged by how well they lower the total harmonic distortion (THD) in the source current. The results show that all three AI-based controls work much better than the system that was not paid for. The ANN Controller works the best, followed by the NARMA-L2 Controller, and the PI Controller improved with the Adaptive Lizard Algorithm. These AI-driven control methods can enhance power quality and ensure that metro rail systems run smoothly and efficiently, as shown by how well they work. Modern transportation networks need more advanced ways to handle power quality, and this research helps make those solutions come together.