Transportation Challenges Research Articles

Critical review on on-site sanitation technologies: Typologies, treatment and transition towards circular economy.

Around 4.2 billion people globally depend on on-site sanitation systems, with 43% relying on basic or unsafe facilities with key challenges of containment, emptying, transport, treatment and resource recovery from faecal sludge. This review paper critically examines faecal sludge characteristics and treatment technologies in terms of urine diversion capability, land requirements and capital as well as operational expenditure based on Indian and international practices. Apart from these, the review also provides reuse-recycle of processed manure, treated effluent and bio-energy from faecal sludge to create environmental and economic benefits. The review also highlights future research on the development of nature-based solutions for solid-liquid separation and treatment, low footprints and on-site treatment for fostering a circular economy. Ultimately this review emphasizes the importance of advancing faecal sludge management to address global sanitation challenges and to create sustainable solutions for environmental protection.

AI-Driven Transport and Distribution Optimization Model (TDOM) for the downstream petroleum sector: enhancing sme supply chains and sustainability

The downstream petroleum sector faces significant challenges in transport and distribution logistics, particularly for small and medium enterprises (SMEs) that rely on efficient supply chains to remain competitive. Inefficiencies such as fluctuating demand, fuel shortages, and route optimization hurdles contribute to increased operational costs and environmental impact. Addressing these persistent issues requires innovative solutions that balance efficiency, cost reduction, and sustainability. This study introduces the AI-Driven Transport and Distribution Optimization Model (TDOM), a transformative approach designed to revolutionize logistics in the downstream petroleum sector. TDOM leverages advanced machine learning algorithms and predictive analytics to optimize delivery routes, reduce fuel consumption, and ensure real-time adaptability to market demands. By integrating data from diverse sources, including traffic patterns, fuel station inventories, and weather conditions, TDOM provides SMEs with actionable insights for enhanced decision-making. The primary objectives of this research are threefold: to improve supply chain efficiency for SMEs in the downstream petroleum sector, to minimize environmental impacts through reduced carbon emissions, and to ensure cost-effective transport and distribution processes. The methodology includes developing a robust AI-driven model utilizing supervised and unsupervised learning techniques, followed by simulation testing on real-world logistics data. Key performance metrics such as delivery time, cost savings, and carbon footprint reduction will be analyzed to validate the model's efficacy. Anticipated outcomes of the TDOM implementation include a 20-30% reduction in transport costs, a significant decrease in fuel consumption, and improved supply chain resilience for SMEs. Furthermore, the model is expected to enhance sustainability by promoting eco-friendly logistics practices, thereby aligning with global environmental standards. By addressing inefficiencies in downstream petroleum logistics and supporting SMEs, the AI-driven TDOM represents a paradigm shift in how the industry approaches transport and distribution challenges. This innovative solution underscores the potential of artificial intelligence to transform traditional industries and foster a more sustainable future.

Effectiveness of the implementation of the birth planning and complication prevention (P4K) program for pregnant women in supporting the optimization of the labor process in the Working Area of Poasia Health Center, Kendari City

Overview: Improving maternal health is crucial for achieving the Sustainable Development Goals (SDGs), specifically reducing the Maternal Mortality Ratio (MMR) to 70 per 100,000 live births by 2030. Indonesia faces significant challenges in maternal health, particularly in Southeast Sulawesi. This study aims to analyze the impact of Birth Planning and Complication Prevention (P4K) on labor outcomes in the Poasia Health Center's working area in Kendari City. It focuses on the relationship between P4K preparation and the smoothness of labor among pregnant women in this region. Body of Knowledge: Maternal health is directly linked to lower maternal mortality and reduced complications during childbirth. Programs like P4K help pregnant women prepare for safe deliveries by educating them on recognizing danger signs and managing potential complications. Previous research has shown that birth planning and early detection of complications lower labor risks. These findings emphasize the importance of education, accessible health services, and psychological support to improve maternal health outcomes. Methods: The study used a cross-sectional design with a total sample of 69 pregnant women from the Poasia Health Center's working area. Data were collected through questionnaires and observations, with statistical analysis using the Chi-square test to assess the relationship between P4K preparation and smooth labor. Total sampling was employed to ensure all eligible participants were included. Results: Results revealed a significant link between P4K preparation and smoother labor. Of the 69 women surveyed, 85.7% who prepared for P4K had smooth deliveries, while 50% of those who did not prepare experienced complications. The statistical analysis indicated a p-value of 0.041, confirming the positive impact of P4K. Recommendation: The study recommends enhancing education and accessibility of P4K services, particularly for women facing transportation or economic challenges. Additionally, improving psychological support and educating families on pregnancy danger signs can further reduce complications and improve maternal health in the Poasia Health Center area.

Boosting the Reaction Rate of Blocky Aluminum-Water via Ultrasound-Based Surface Engineering in Room-Temperature Liquid Metal Solvent.

Hydrogen gas holds immense promise as a clean fuel source, yet its widespread adoption faces significant challenges in storage and transportation due to its gaseous and highly flammable nature. An increasingly attractive approach to overcoming these limitations involves reacting aluminum (Al) blocks with water to produce hydrogen, providing an alternative distribution mechanism in which Al blocks can be used as "hydrogen storage" for on-demand production at any location. However, current methods suffer poor hydrogen production rates and yields, primarily influenced by the limited contact area between Al and the catalyst, such as Ga-based room-temperature liquid metal. Herein, we introduce an energy-efficient pretreatment method to enhance hydrogen conversion efficiency by leveraging ultrasound-based surface engineering of Al blocks in a Galinstan solvent. Our strategy facilitates rapid penetration of Galinstan into the Al block microstructures via ultrasound-induced metal jets, allowing maximum contact between Al and Galinstan within a short period of time, by which the hydrogen production rates and yields of block Al are greatly improved. Overall, our results mark a significant advancement in hydrogen production methods, presenting a viable pathway toward sustainable and efficient hydrogen generation and storage.

Scale-up of the CRADLE Vital Signs Alert intervention to improve maternity triage in Sierra Leone: a mixed methods evaluation of device repair and maintenance

BackgroundScale-up of the CRADLE intervention, a simple and accurate blood pressure and pulse device and focused training package, across all government maternity care facilities in Sierra Leone was completed in...

A Model-Data Dual-Driven Approach for Predicting Shared Bike Flow near Metro Stations

Bike-sharing has emerged as an innovative green transportation mode, showing promising potential in addressing the ‘last-mile’ transportation challenge in an eco-friendly manner. However, shared bikes around metro stations often face supply–demand imbalance problems during peak hours, causing bike shortages or congestion that compromise user experience and bike utilization. Accurate prediction enables operators to develop rational dispatch strategies, improve bike turnover rate, and promote synergistic metro–bike integration. However, state-of-the-art research predominantly focuses on improving complex deep-learning models while overlooking their inherent drawbacks, such as overfitting and poor interpretability. This study proposes a model–data dual-driven approach that integrates the classical statistical regression model as a model-driven component and the advanced deep-learning model as a data-driven component. The model-driven component uses the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to extract periodic patterns and seasonal variations of historical data, while the data-driven component employs an Extended Long Short-Term Memory (xLSTM) neural network to process nonlinear relationships and unexpected variations. The fusion model achieved R-squared values of 0.9928 and 0.9770 for morning access and evening egress flows, respectively, and reached 0.9535 and 0.9560 for morning egress and evening access flows. The xLSTM model demonstrates an 8% improvement in R2 compared to the conventional LSTM model in the morning egress flow scenario. For the morning egress and evening access flows, which exhibit relatively high variability, classical statistical models show limited effectiveness (SARIMA’s R2 values are 0.8847 and 0.9333, respectively). Even in scenarios like morning access and evening egress, where classical statistical models perform well, our proposed fusion model still demonstrates enhanced performance. Therefore, the proposed data–model dual-driven architecture provides a reliable data foundation for shared bike rebalancing and shows potential for addressing the challenges of limited robustness in statistical regression models and the susceptibility of deep-learning models to overfitting, ultimately enhancing transportation ecosystem sustainability.

App Assisted Depression Self-Management in Integrated Primary Care: A Pilot Study Exploring Patient and Provider Experiences

Abstract The integrated primary care (IPC) setting is currently the de facto institution for accessing mental healthcare among underserved populations. However, many barriers exist for these populations when seeking regular and adequate mental healthcare, including provider availability and transportation challenges. This study piloted the incorporation of mindLAMP, a mHealth app, into IPC treatment to offer a low-resource intervention designed to improve and expand access to mental healthcare for underserved patients between face-to-face visits. This study gathered data from a sample of both behavioral health providers and patients (N = 6) in IPC settings to pilot the use of the mindLAMP app among traditionally underserved patients with depression. The Patient Activation Measure and Patient Health Questionnaire were used to assess patient outcomes in addition to conducting a directed content analysis on qualitative data obtained with interviews of both patients and providers. Results from the mixed methods study design indicate that implementing this digital intervention in the IPC setting is feasible and positively experienced by both patients and providers. Perceived positive outcomes were reported by both patients and providers after incorporating the mHealth app into their mental healthcare. However, both patients and providers discussed barriers to mHealth app use that need to be addressed in order to accommodate more widespread use. Further, constraints and barriers at the provider, patient, and healthcare system levels were discussed and need to be examined further to increase the implementation of this multilevel digital intervention.

Navigating DR-TB Treatment care: a qualitative exploration of barriers and facilitators to retention in care among people with history of early disengagement from drug-resistant tuberculosis treatment in Johannesburg, South Africa

BackgroundDespite advances in drug-resistant tuberculosis (DR-TB) diagnosis, treatment, and service delivery, individuals with DR-TB often face significant socioeconomic and psychosocial challenges due to limited resources. These challenges can hinder retention in care, undermining the progress made in DR-TB management. As a consequence, advances in DR-TB diagnostics and treatment have not resulted in DR-TB programs meeting the 75% treatment success targets set by the World Health Organization (WHO).MethodsWe interviewed people with DR-TB who had disengaged from care and their family members to identify barriers and facilitators to retention in care as well as possible strategies to address these barriers. We recruited 16 people with DR-TB and 8 family members from five health facilities in Johannesburg, Gauteng Province, South Africa. All DR-TB patients disengaged from DR-TB care for ≥ 45 days. Semi-structured interviews and focus group discussions were used to collect data, which were analysed through thematic content analysis using a multidimensional adherence model.ResultsThe facilitators of retention in care were positive interactions with health care workers (HCWs), nutritional support, transport from local clinics to DR-TB sites, self-motivation, and emotional support from family members. Barriers to optimal retention in care included a limited understanding of DR-TB disease and treatment, transport challenges, side effects of the medication, pill burden, stigma and discrimination experienced at health care facilities other than DR-TB facilities, food insecurity, and financial difficulties, which included loss of income and a lack of transport money and mental health challenges such as fear, anxiety and feeling lonely and unsupported.ConclusionThe findings from this study highlight the need for TB treatment programs to collaborate with people being treated for DR-TB and their families to understand facilitators and barriers to retention in care and how these could be addressed to facilitate optimal retention in care.

Sustainable Transport and Development Partnership: Enhancing Urban Growth in Hobart, Australia Through TOD, PPP and Green Building Practices

This paper introduces the concept of sustainable transport and development partnership (STDP), a holistic approach integrating transit-oriented development (TOD), public–private partnership (PPP) and green building practices to address high carbon emissions from the building sector. Focusing on Hobart, Australia, the study explores the city’s urban context and potential for STDP implementation, proposing a comprehensive framework that incorporates green building practices across six key dimensions. Through in-depth interviews with PPP experts and on-site observations, the research identifies key transport challenges in Hobart, analyzes their interrelationship with potential STDP initiatives and proposes a set of STDP metrics to evaluate their efficacy. The findings contribute to the discourse on sustainable urban development, offering practical insights for policymakers, developers and researchers and providing a valuable reference for regional cities globally seeking to implement effective and sustainable STDP strategies, ultimately enhancing urban growth and reducing carbon emissions.

EVALUATING MATERNAL UNDERSTANDING OF WHO-RECOGNIZED NEONATAL DANGER SIGNS: A CROSS-SECTIONAL STUDY

Background: Neonatal mortality constitutes a significant proportion of under-five mortality worldwide, with over 2.4 million neonatal deaths reported annually. In Pakistan, the neonatal mortality rate (NMR) stands alarmingly high at 42 per 1,000 live births. Neonatal danger signs, such as fever, jaundice, and breathing difficulties, require immediate medical attention but often go unrecognized due to insufficient maternal knowledge. Socioeconomic disparities, cultural practices, and inadequate access to healthcare exacerbate the issue, delaying timely interventions and contributing to preventable neonatal deaths. Objective: The study aims to assess maternal knowledge of neonatal danger signs, identify determinants of adequate awareness, and explore barriers to timely healthcare-seeking practices. Method: A cross-sectional, community-based survey was conducted from August to December 2024 in one urban and one rural district of Sindh, targeting mothers of neonates aged 0–28 days. A structured questionnaire adapted from the WHO Safe Motherhood Toolkit was used to collect data on maternal knowledge, antenatal care (ANC) attendance, and healthcare-seeking behaviors. A multistage random sampling method ensured representativeness, with a sample size of 300 participants. Descriptive statistics and logistic regression analyses were performed using SPSS version 25 to identify key predictors of knowledge, with significance set at p<0.05. Results: Among 300 mothers, 48.5% demonstrated adequate knowledge of neonatal danger signs. Fever (75%), poor feeding (62%), and jaundice (51%) were the most recognized signs, while hypothermia (18%) and convulsions (12%) were least identified. Maternal education, urban residence, and ANC attendance significantly predicted knowledge levels. Only 57% sought timely medical care, with rural participants reporting financial constraints, transportation challenges, and reliance on traditional healers as primary barriers. Conclusion: Maternal knowledge of neonatal danger signs remains suboptimal, especially in rural areas. Targeted health education programs, improved healthcare access, and financial support mechanisms are essential to enhance neonatal survival and reduce preventable deaths.

Porous Silicon on Paper: A Platform for Quantitative Rapid Diagnostic Tests.

Porous silicon (PSi) thin films on silicon substrates have been extensively investigated in the context of biosensing applications, particularly for achieving label-free optical detection of a wide range of analytes. However, mass transport challenges have made it difficult for these biosensors to achieve rapid response times and low detection limits. In this work, we introduce an approach for improving the efficiency of molecule transport in PSi by using open-ended PSi membranes atop paper substrates in a flow-through sensor scheme. The paper substrate provides structural support as well as an efficient means of draining solutions from the PSi membrane without the use of an external pump and microfluidic channels. Distinct changes in the reflectance properties of the PSi membrane are measured when molecules are captured in the membrane. A concentration-dependent response of the sensor for protein detection is demonstrated. Factors influencing the interaction time of molecules in the PSi membrane and the drying time of the membrane, which directly affect the detection sensitivity and overall testing time, are discussed. The demonstrated performance of the PSi-on-paper sensor establishes the feasibility of a platform for low-cost rapid diagnostic tests with a highly sensitive, quantitative readout.

Conducting Clinical Research in Low Research Resource Countries: Lessons Learned From the International Registry of Men With Advanced Prostate Cancer Study in Nigeria.

There is limited cancer clinical research in sub-Saharan African countries despite the significant burden of cancers. The primary objective of this strengths, weaknesses, opportunities, and threats (SWOT) analysis was to understand and document factors affecting the successful implementation of prostate cancer (CaP) clinical research in Nigeria. The research team used a qualitative design involving International Registry of Men with Advanced Prostate Cancer (IRONMAN) study team members as participants from four regional sites in Nigeria. One-hour listening sessions were conducted via Zoom at each site, focusing on SWOT related to the IRONMAN study. All sessions were recorded, transcribed, and analyzed. Themes were inductively coded and then synthesized across all four sites. The study teams identified several strengths and weaknesses in conducting the IRONMAN study at their respective sites. Key strengths included access to robust patient population for recruitment and the availability of essential resources, such as lab space, clinical knowledge, and adequate staffing. Weaknesses centered on the social determinants of health that hinder patient participation, such as transportation challenges, distance to clinics, limited access to care, and insufficient biorepository space for sample storage. A prominent opportunity identified was the potential for Nigerian institutions to engage in more clinical research, particularly multisite global trials. Threats included difficulties in retaining research staff and political instability. This study highlights the promising research opportunities in Nigeria. The lessons learned from the IRONMAN study provide valuable insights into the feasibility of conducting CaP clinical research and trials tailored to the needs of Black men in sub-Saharan Africa. These findings offer a roadmap for future research efforts, with the potential to expand clinical trials and improve health outcomes across the region.

Social, Racial, and Economic Disparities Affecting Outcomes of Hypertensive Adolescents.

Essential hypertension is one of the most common conditions managed in pediatric cardiology and can result in lasting deleterious effects on the cardiovascular system. Pediatric hypertension is so prevalent in the United States that it is often referred to as a public health challenge. Social determinants of health (SDH) are the cultural, economic, educational, healthcare accessibility, and political influences in the environment in which an individual is born or lives, all of which can affect that individual's overall health. This study investigated the impact of social determinants such as rurality, food insecurity, transportation challenges, minority status, income, and race on cardiovascular outcomes in adolescent patients with essential hypertension. This study utilizes multiple validated tools including those from the United States Census and the United States Department of Agriculture (USDA). Using these tools, the patients were scored on their social vulnerability based on home address. These scores were then compared with their echocardiographic data, focusing on measures of end-organ damage known to occur in the setting of hypertension, including but not limited to indexed left ventricular (LV) mass. LV mass is an independent risk factor for future adverse cardiovascular events. In this study, more social vulnerability and low income were associated with a greater indexed LV mass (r = 0.18, p = 0.008). African American race was associated with a higher left atrial (LA) volume (p = 0.03). These findings substantiate that adolescents with essential hypertension are not only impacted by biological factors but also a combination of intersecting social constructs. The results of this study provide both adeeper understanding of the challenges these patients face and the opportunity to develop real-life interventions that can optimize clinical outcomes.

Implications of Climate Change on Brazilian Emergency Medicine

Introduction: Climate change poses a significant global health threat, impacting human health through extreme weather events, disruptions to food systems, and the spread of diseases. Brazilian emergency medicine faces unique challenges, particularly in response to climate-related crises such as floods, heatwaves, and wildfires. This study aims to discuss the implications of climate change on Brazilian emergency medicine and the roles emergency physicians must adopt to address these challenges. Methods: A narrative literature review was conducted, focusing on recent events and actions taken by Brazilian emergency physicians and institutions. Key topics analyzed include extreme weather events, disease outbreaks, and public health interventions. Institutional reports and clinical guidelines from the Associação Brasileira de Medicina de Emergência (ABRAMEDE) were also reviewed. Results: Brazil has been increasingly affected by climate-related disasters, particularly in states like Rio Grande do Sul. Emergency departments were overwhelmed during recent floods and heatwaves. Arboviral diseases such as dengue have surged, exacerbated by rising temperatures. Wildfires, particularly in the Amazon, have further strained emergency services, with respiratory diseases and logistical challenges in patient transport becoming critical issues. ABRAMEDE has issued recommendations on hospital disaster preparedness and clinical guidelines to mitigate these health crises. Conclusions:Brazilian emergency physicians must focus on enhancing climate-related education, increasing resource allocation for emergency departments, and improving disaster preparedness. These steps are crucial for addressing the evolving health challenges posed by climate change, particularly in vulnerable populations. Emergency medicine must adapt to safeguard public health as environmental conditions continue to shift.

Design prognostics for 4400 TEU container vessel by multi‐variate Gaidai reliability approach

AbstractThis case study introduces an innovative multivariate methodology for assessing the lifetime of marine engineering systems, specifically in cargo vessel transportation. The analysis focused on stress data collected onboard a 4400 TEU container vessel during multiple trans‐Atlantic voyages. One of the major challenges in marine cargo transport lies in mitigating the risk of container loss due to excessive whipping loads. Accurate prediction of extreme stress levels on vessel deck panels remains difficult, primarily because of the nonlinear and non‐stationary nature of wave and ship motion interactions. Higher‐order dynamic effects, such as second‐ and third‐order responses, often become significant when ships operate under adverse environmental conditions, amplifying nonlinear influences. Laboratory simulations, constrained by wave characteristics and scale similarity issues, may not always provide reliable results. Consequently, data collected from vessels navigating extreme weather conditions serves as a critical resource for comprehensive container ship risk assessment. The primary goal of this study was to validate and demonstrate the effectiveness of a novel multivariate risk evaluation approach, leveraging onboard measurements of dynamic areal pressure on cargo ship deck panels as the core dataset. The Gaidai methodology for multivariate risk evaluation proved to be a robust tool for assessing failure, hazard, and damage risks in complex, nonlinear vessel deck panel and ship hull stress systems.

Multi-level Accessibility Analysis of Hyderabad City Using GIS-based Network Evaluation

Transportation accessibility plays a pivotal role in transport geography, serving as a critical indicator of mobility for both people and goods. High levels of accessibility reflect a well-developed urban environment with fewer transportation challenges. In this study, Geographic Information Systems (GISs) are employed to conduct a comprehensive network analysis of accessibility across various zones in Hyderabad city, Telangana, India. Using a city-wide methodology for evaluating urban service availability and distance-based accessibility, the study incorporates a weighted scoring model. This model assigns varying weights to different urban services based on their relative importance, enabling a nuanced accessibility analysis. The study assesses accessibility at three distinct spatial scales: Macro-level Cluster Zones, Meso-level Strategic Zones, and Micro-level Traffic Analysis Zones (TAZs). By analyzing accessibility at these scales, the study offers a detailed and layered understanding of how different locations in Hyderabad perform in terms of access to essential services and opportunities. The results provide valuable insights for urban planners and decision-makers, helping them identify areas where accessibility can be improved, thus supporting more equitable and efficient urban development. Keywords: Weighted scoring model, Macro-level cluster zones, Meso-level strategic zones, Micro-level traffic analysis zones (TAZs), Accessibility analysis.

ASSESSING THE URBANIZATION-DRIVEN CHALLENGES OF TRANSPORTATION SCARCITY AND COMMUTER ACCESSIBILITY IN DAVAO CITY: A SYSTEMATIC REVIEW

This systematic review explores the transportation challenges and commuter accessibility issues arising from urbanization in Davao City. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, the study analyzes relevant literature to offer a thorough overview of the problem. Key themes identified include inadequate public transportation, unequal accessibility in outlying areas, environmental concerns, and lack of cohesive urban and transportation planning. The review concludes with practical recommendations, focusing on sustainable urban transport policies, infrastructure development, and community-oriented planning to effectively tackle these challenges.

Decoupling Interfacial Stability and Ion Transport in Solid Polymer Electrolyte by Tailored Ligand Chemistry for Lithium Metal Battery

AbstractAchieving fast ion transport kinetics and high interfacial stability simultaneously is challenging for polymer electrolytes in solid‐state lithium batteries, as the coordination environment optimal for Li+ conduction struggles to generate desirable interphase chemistry. Herein, the adjustable property of organic ligands is exploited in metal–organic frameworks (MOFs) to develop a hierarchical composite electrolyte, incorporating heterogeneous and spatially confined MOF nanofillers into a poly‐1,3‐dioxolane matrix. The defect‐engineered University of Oslo‐66 MOFs (UiO‐66d) with tailored Lewis acidity can separate ion pairs and optimize Li+ migration through weakened solvation effects, thereby enhancing ion conductivity by over sixfold (0.85 mS cm−1@25 °C). At the lithium anode side, a densified University of Oslo‐67 MOFs (UiO‐67) layer with conjugated π electrons facilitates anion participation in the solvation sheath, promoting anion reduction and thereby forming LiF/Li3N‐dominated solid electrolyte interphase for isotropic Li deposition. The as‐assembled Li||LiFePO4 full cell delivers superior cycling stability with 92.7% of capacity retained over 2000 cycles at 2 C. Notably, the developed electrolyte demonstrates excellent compatibility with high‐voltage cathodes, achieving 80% capacity retention with LiNi0.5Co0.2Mn0.3O2 over 630 cycles. This work provides valuable insights into decoupling transport and interfacial challenges in solid‐state lithium batteries, paving the way for advanced battery technologies.

The Application of Machine Learning and Deep Learning with a Multi-Criteria Decision Analysis for Pedestrian Modeling: A Systematic Literature Review (1999–2023)

Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

Impact of Logistics Factors on Procurement of Construction Materials in Lokoja, Kogi State

This study investigates the influence of logistics factors on the procurement of construction materials in Lokoja, Kogi State, aiming to identify and rank the critical elements affecting material procurement in building projects. A total of 250 questionnaires were distributed across nineteen neighborhoods in Lokoja using a random sampling technique. Data were analyzed using descriptive statistics, while the T-test assessed the significance of identified logistics factors. The findings revealed that seventeen logistics factors influence material procurement, with seven factors emerging as the most significant: on-time delivery, transportation challenges, material misdelivery, delivery inaccuracies, order processing, storage capacity limitations, and ineffective information flow. These factors were ranked by their impact on procurement processes, with hypothesis testing confirming a statistically significant relationship between logistics factors and material procurement efficiency. The study concludes that effective management of key logistics factors is essential to avoid cost overruns, delays, and material wastage. It recommends that project managers prioritize better coordination among stakeholders to address logistics-related challenges, ensuring materials are delivered efficiently and on time.