Transportation Model Research Articles

Computational Modeling of Pharmaceuticals with an Emphasis on Crossing the Blood–Brain Barrier

The discovery and development of new pharmaceutical drugs is a costly, time-consuming, and highly manual process, with significant challenges in ensuring drug bioavailability at target sites. Computational techniques are highly employed in drug design, particularly to predict the pharmacokinetic properties of molecules. One major kinetic challenge in central nervous system drug development is the permeation through the blood–brain barrier (BBB). Several different computational techniques are used to evaluate both BBB permeability and target delivery. Methods such as quantitative structure–activity relationships, machine learning models, molecular dynamics simulations, end-point free energy calculations, or transporter models have pros and cons for drug development, all contributing to a better understanding of a specific characteristic. Additionally, the design (assisted or not by computers) of prodrug and nanoparticle-based drug delivery systems can enhance BBB permeability by leveraging enzymatic activation and transporter-mediated uptake. Neuroactive peptide computational development is also a relevant field in drug design, since biopharmaceuticals are on the edge of drug discovery. By integrating these computational and formulation-based strategies, researchers can enhance the rational design of BBB-permeable drugs while minimizing off-target effects. This review is valuable for understanding BBB selectivity principles and the latest in silico and nanotechnological approaches for improving CNS drug delivery.

A transportation model minimizing total cost and time in disaster struck zone for debris removal under a mixed neutrosophic environment

A transportation model minimizing total cost and time in disaster struck zone for debris removal under a mixed neutrosophic environment

Machine Learning Tool for New Selective Serotonin and Serotonin–Norepinephrine Reuptake Inhibitors

Depression, a serious mood disorder, affects about 5% of the population. Currently, there are two groups of antidepressants that are the first-line treatment for depressive disorder: selective serotonin reuptake inhibitors and serotonin–norepinephrine reuptake inhibitors. The aim of the study was to develop Quantitative Structure–Activity Relationship (QSAR) models for serotonin (SERT) and norepinephrine (NET) transporters to predict the affinity and inhibition potential of new molecules. Models were developed using the Automated Machine Learning tool Mljar based on 80% of the dataset according to 10-fold cross-validation and externally validated on the remaining 20% of data. The molecular representation featured two-dimensional Mordred descriptors. For each model, Shapley additive explanations analysis was performed to clarify the influence of the descriptors on the models’ predictions. Based on the final QSAR models, the following results were obtained: NET and pIC50 value RMSEtest = 0.678, R2test = 0.640; NET and pKi RMSEtest = 0.590, R2test = 0.709; SERT and pIC50 RMSEtest = 0.645, R2test = 0.678; SERT and pKi value RMSEtest = 0.540, R2test = 0.828. QSAR models for serotonin and norepinephrine transporters have been made available in a new module of the SerotoninAI application to enhance usability for scientists.

The complexity of integrating land-use and transportation policies to promote sustainable compact development

ABSTRACT Urban compaction is widely recognized as a key strategy for promoting sustainability, yet successful implementation cases remain rare in the United States. We use the Transportation, Economic, and Land-Use Model (TELUM) to examine to what extent a land-use or transportation policy must be regulated to make the compaction occur in a typical auto-dependent city, Fresno, California with five policy scenarios. The results from the land-use policy scenarios suggest that high-density development and outskirt restriction can promote compaction, with the latter accelerating the process. Transportation policy scenarios, locational impedance and mileage-based impedance, demonstrate that transportation interventions can either create barriers between regions or foster the formation of a polycentric compact city, respectively. The policy analysis approach we propose not only evaluates the outcomes of specific compaction policies but also assesses whether development trends align with the intended objectives of policy interventions designed to achieve urban compaction.

Empirical Insights into Centroid Optimization in Transportation Planning: A Novel Approach

In complex urban environments, sophisticated analytical techniques are essential to improve the accuracy of transportation modeling and the precision of travel time predictions. This research endeavors to critically examine various multi-zone centroid positioning strategies, with the goal of unveiling the method that most authentically represents the dynamics of urban travel. Anchored by a comprehensive case study of the city of Istanbul in Turkey, the investigation utilizes various analytical tools meticulously tailored to account for the intricate interplay of demographic and geographic variables. This methodical approach enables the identification of optimal centroid locations by evaluating various methods, including a novel approach called kernel density estimation (KDE), as well as five other established other methods. The results are then aligned with real-world travel time data through rigorous statistical analysis to ensure accurate predictions of travel times. The KDE method demonstrated its more acceptable correlation coefficient and lower mean absolute error, root mean square error, and mean absolute percentage error metrics, establishing its superiority in model performance. By highlighting the paramount importance of accurately placed centroids within urban transportation models, this research’s findings contribute significantly to the evolution of transportation planning methodologies and the advancement of urban mobility models through a comparative analysis of optimal methods.

Generalized Intuitionistic Trapezoidal Fuzzy Numbers in Transportation Problems: An Optimizational Method

In this study, we propose an optimization method for transportation problems utilizing Generalized Intuitionistic Trapezoidal Fuzzy Numbers (GITFNs). This method effectively handles uncertainty and imprecision in transportation systems, enabling more realistic and reliable decision-making. A GITFN-based transportation model is developed, incorporating fuzzy costs, capacities, and demands. An optimization algorithm is designed to minimize transportation costs while satisfying supply and demand constraints. The method is applied to a numerical example, demonstrating its efficiency in solving complex transportation problems. The results show significant improvements in transportation effectiveness and cost reduction.

A physics teaching material: myopia model

Transferring physics to daily life supports that the subjects are learned effectively. The human eye offers a rich potential for connecting optics concepts to everyday life. This study introduced a cost-effective and easily transportable eye model made from wood as an optic instructional material. With this eye model, myopia and its correction can be explained in physics courses, as well as why a person with myopic cannot see the distance clearly but can see the near clearly. The advantages of this material which is used with flashlight, light limiter and wood grid from other commercial models which is used with laser were shown experimentally. It is thought that this teaching material will important contribute to associating optics topics with daily life in courses.

Physiologically Based Pharmacokinetic Model of Cefotaxime in Patients with Impaired Renal Function.

Cefotaxime is a widely prescribed cephalosporin antibiotic used to treat various infections. It is mainly eliminated unchanged by the kidney through tubular secretion and glomerular filtration. Therefore, a reduction of kidney function may increase exposure to the drug and induce toxic side effects. The objectives of this study were to develop a physiologically based pharmacokinetic (PBPK) model of cefotaxime in healthy European adults, to mechanistically describe the impact of chronic kidney disease (CKD) on cefotaxime pharmacokinetics, and to assess the applicability of the model to patients requiring intensive care. Using PK-Sim® software, we developed a PBPK model for cefotaxime, including basolateral and apical renal transporters and renal esterases, in healthy subjects and then extrapolated to patients with CKD by incorporating pathophysiological changes and reductions in activity of drug-metabolizing enzymes and transporters into the model. We then evaluated the predictive performance of the model in patients requiring intensive care using clinical routine data. Model predictions were considered adequate in healthy subjects and patients with CKD, with predicted-to-observed area under the curve ratios within the two-fold acceptance criterion. Mean prediction error and mean absolute prediction error did not exceed ± 30 and 30%, respectively, except in patients with stage 4 CKD, where they were 70.5 and 75.6%, respectively. The model showed good predictive performance when applied to patients requiring intensive care, but its clinical applicability in this population needs to be further evaluated. We successfully developed whole-body PBPK models to predict cefotaxime pharmacokinetics in different populations. These models represent an additional step toward improving personalized cefotaxime dosing regimens in vulnerable populations.

Transportation noise investigation and modeling in selected roads in Metro Manila, Philippines

Transportation noise investigation and modeling in selected roads in Metro Manila, Philippines

Strawberry (Fragaria L.) transportation model in Mexico for Closed Market

The objective of this research is to develop a transportation model for the optimization of strawberry distribution in Mexico in a closed market through the use of linear programming. The formulation of this model includes the objective function and supply and demand constraints derived from various calculations and research. The information gathered comes from official sources such as SIACON, INEGI and the Foreign Trade Information Cube of the Bank of Mexico. On the other hand, transportation costs are calculated using GlobalMap© and the resolution of the model through LINDO 6.1® software. This model allows for the identification of optimal routes and quantities for distribution between strawberry supplying and demanding states, and not only optimizes internal logistics, but also reduces transportation costs.

Method for Defining VD-Functions for Transport Network Elements in an Urban Mobility Model

Abstract. Problem. One of the critical factors influencing the assignment of private transport demand in urban mobility models is the Volume-Delay Function (VDF) applied to transport network elements such as links, nodes, and turns. Decades of research have provided substantial insights into transportation modeling, including the identification of parameters essential for constructing VD-functions. Nevertheless, the accurate definition and calibration of VD-functions for all significant transportation network objects in urban models continue to demand innovative approaches. Goal. The objective of this study is to formulate a method for determining VD-functions for objects of the transport network within the urban mobility model developed using PTV Visum software, leveraging the analysis of historical traffic data. Methodology. The study establishes the relationships between the current speed of private transport and links congestion levels, as well as between travel time and congestion at nodes and turns when applying different VD-functions. Using Kryvyi Rih's urban mobility model, developed in PTV Visum software, the VD-functions were tested in various combinations, with a focus on selecting appropriate calibration parameters. The average vehicle flow rate in the loaded network served as the criterion for assessing the alignment of the model's results with real-world conditions. For the city's street and road network, average daily traffic speeds were analyzed using data from the TomTom Traffic Stats portal, which utilizes FCD technology. The analysis concluded that Scenario 2 is the most suitable for Kryvyi Rih's mobility model, applying the Lohse function for links and TModel Nodes for nodes and turns. Originality. A scientifically interesting aspect is the approach of utilizing average daily vehicle speed data, obtained through FCD technology, to determine and calibrate VD-functions for various types of transport network objects within the urban mobility model. Practical value. This work holds practical significance for transportation modeling engineers, as the developed VD-functions can be applied during the calibration of private transport demand in mobility models for other cities. Additionally, it is valuable for decisionmakers in the transport sector, aiding in more informed planning and management.

The sustainable transportation model in Lake Towuti: Assessment of safety and management with MICMAC analysis

This research investigates the safety status of water transport in Lake Towuti, South Sulawesi, employing the MICMAC and MACTOR methodologies to discern the factors that affect navigation safety and the interactions among the relevant stakeholders. The MICMAC analysis reveals that the effectiveness of sustainable transportation in Lake Towuti is significantly dependent on technical elements such as vessel certification, maintenance practices, and safety monitoring, alongside robust relationships among key entities like The South Sulawesi Class II Land Transportation Management Center (BPTD), The East Luwu District Transportation Office (Dishub), and the Timampu Port Service Unit (Satpel). When implementing the MICMAC-MACTOR model, it is essential to consider the technical implications of the proposed recommendations from the perspectives of social justice, environmental sustainability, and economic feasibility. The outcomes derived from the MICMAC and MACTOR assessments in Lake Towuti provide critical insights that can be utilized in other lakes across Indonesia, especially those that exhibit deficiencies in safety measures and adherence to inland water transport safety regulations.

Exploring the Impact of Pharmaceutical Excipient PEG400 on the Pharmacokinetics of Mycophenolic Acid Through In Vitro and In Vivo Experiments.

Mycophenolic acid (MPA) is a commonly used immunosuppressant. In the human body, MPA is metabolized into mycophenolic acid 7-O-glucuronide (MPAG) and mycophenolic acid acyl-glucuronide (AcMPAG) mainly through liver glucuronidation, which involves UDP-glucuronosyltransferase (UGTs) and transfer proteins. Research has indicated that the pharmaceutical excipient PEG400 can impact drug processes in the body, potentially affecting the pharmacokinetics of MPA. Due to the narrow therapeutic window of MPA, combination therapy is often used, and PEG400 is widely used in pharmaceutical preparations. Therefore, investigating the pharmacokinetic influence of PEG400 on MPA could offer valuable insights for optimizing MPA's clinical use. In this study, we examined the impact of a single oral dose of PEG400 on the blood levels of MPA in rats through pharmacokinetic analysis. We also investigated the distribution of MPA in various tissues using mass spectrometry imaging. We explored the potential mechanism by which PEG400 affects the metabolism of MPA using hepatic and intestinal microsomes and the Caco-2 cellular transporter model. Our findings reveal that the overall plasma concentrations of MPA were elevated in rats following the co-administration of PEG400, with the AUC0-t of MPA and its metabolite MPAG increasing by 45.53% and 29.44%, respectively. Mass spectrometry imaging showed increased MPA content in tissues after PEG400 administration, with significant differences in the metabolites observed across different tissues. Microsomal and transport experiments showed that PEG400 accelerated the metabolism of MPA, promoted the uptake of MPA, and inhibited efflux. In conclusion, PEG400 alters the in vivo metabolism of MPA, potentially through the modulation of metabolic enzymes and transport.

Availability of Critical Benchmark Experiments for the Pebble Tanker Transportation Model for Nuclear Criticality Safety Validation of TRISO Pebbles

This study addresses the need for comprehensive investigations into TRi-structural ISOtropic (TRISO) fuel pebble transportation validation. In this work, an exploratory model, the pebble tanker(PT), was developed with the aim of facilitating the validation of nuclear criticality safety calculations in the context of industrial-scale transportation of TRISO fuel. The PT model was designed to investigate the availability and applicability of critical benchmark experiments crucial for assessing the transportation of these pebbles. This work incorporated sensitivity/uncertainty (S/U) similarity studies to quantify the applicability of critical benchmark experiments and to address nuclear data uncertainties in the context of TRISO transportation. Two container models were investigated: one for the Hermes-type pebble and one for the Pebble Bed Modular Reactor (PBMR)–type pebble. The models were simplified, considering fuel, containment, and either water or air, to enable a focus on the underlying physics of applications involving TRISO fuel pebbles using the PT model. A crucial aspect under consideration was the capacity of the transport package to hold pebbles while ensuring subcriticality in the flooded state. An approach in the criticality validation process involves assessing the similarity between systems through an integral index parameter evaluation. This involves calculating a correlation coefficient (referred to as ck) based on shared nuclear data–induced uncertainty between a benchmark experiment and the application of the PT model. To facilitate this analysis, the SCALE tools, particularly the CSAS6-Shift, TSUNAMI-3D-Shift, and TSUNAMI-IP sequences, were employed for comprehensive studies in neutronics and S/U analysis. Our findings showed that there are sufficient critical experimental benchmarks to perform this validation of the PT model in the most reactive state, i.e. when the tanker is flooded. This paper provides valuable insights into validating a transport package for Generation IV TRISO fuel pebbles.

A multi-objective solid transportation model for e-waste recovery in the post-disaster phase under an uncertain environment

A multi-objective solid transportation model for e-waste recovery in the post-disaster phase under an uncertain environment

From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage.

Scenarios for energy transition in public transport

The growing global demand for new energy sources and innovative technologies is driven by environmental concerns and the need to address the worldwide energy crisis. The Paris Climate Agreement, also known as the United Nations Climate Change Conference Paris 2015 (COP21), has compelled nations to explore cleaner, more sustainable, and carbon-neutral technologies and products. This commitment was reaffirmed at the 2021 Climate Change Conference (COP26) in Ireland. This study seeks to determine whether the global shift in transportation models from combustion to electric traction holds promise for achieving the Sustainable Development Goals (SDGs) in the Brazilian context, thereby contributing to climate change mitigation. To investigate this, a quantitative field research approach was employed, accompanied by relevant literature references. Key indicators, including urban bus routes in Cascavel, Paraná, Brazil, their average mileage, emissions, and energy efficiency, were examined in comparison to electric buses. The research applied the Life Cycle principle, known as "Well to Wheels," to assess emissions in relation to the bus's propulsion system and energy sources. The findings revealed a remarkable 97.19% reduction in emissions (in gCO2eq/km) when using electricity from the predominantly renewable Brazilian electricity grid. Additionally, a 98.37% reduction in emissions (in gCO2eq/km) was observed when a photovoltaic system served as the energy source. These results underscore the sustainable potential of such systems in addressing the urgent global climate crisis.

Using the Transportation Model in Distributing Fish between the Governorates of the Arab Republic of Egypt

Using the Transportation Model in Distributing Fish between the Governorates of the Arab Republic of Egypt

Affordability and accessibility of condominium housing in urban Ethiopia using a combined transportation and housing cost (CHT) index model: implication for sustainable urban infrastructure, policy and development

ABSTRACT Condominium housing is a critical housing option, but its affordability measures continue to be debatable. This study examined the mutual effect of housing & transportation monthly expenses on housing location affordability and accessibility, considering three comparable locations- inner-city, intermediate, and outer-city condominium locations in Addis Ababa, Ethiopia. 1152 condominium residents were surveyed to assess urban housing location affordability using the combined H+T affordability index, GIS, one-way ANOVA, and Logit model. Housing affordability is diversified in the city, distance to CBD & transport are significant factor. Outer-city and intermediate neighbourhoods are unaffordable & inaccessible, with 55% and 48% index, respectively. Outer-city residents and lower-incomers face higher financial burdens. Nevertheless, inner-city is affordable & accessible, and residents enjoy better proximity to services. It contributes insights to enhance literature and debates on the model. H+T index provides up-to-date understanding and informs policymaking for innovative, smart, and sustainable, location-sensitive, integrated, and pro-poor policies.

Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation

Latent class choice modeling has gained great popularity in the transportation and choice modeling communities across the years. However, discussion of principles associated with the specification of the class membership model has barely appeared in the literature. Related to this issue, this study questions whether one of the basic assumptions of latent class choice modeling, that of independence between latent segmentation and the behavior generation process, is tenable. We formulate latent class choice models where the unobserved influences on latent segmentation and behavior generation are correlated, by introducing an error structure reflecting that supposition. The proposed method is applied to two empirical settings. In the first application, the dependent variable is an ordinal variable measuring willingness to share autonomous vehicle rides with strangers. In the second application, the dependent variable is a binary indicator of whether a person has used ridehailing services for social purposes. In both applications, error correlations were statistically significant, indicating that the segmentation and behavior generation processes are jointly determined. Although goodness of fits and parameter estimates per se are similar to those of the standard latent class choice models for these particular applications, allowing an error structure leads to a subtle change in model implications. In particular, our scenario analyses, which present marginal effects, illustrate the value of the proposed model for considering jointness arising from correlated errors, in contrast to standard latent class models. Lastly, we propose several avenues for future research.