Major Roads Research Articles

The accumulation process of pollutants in deposited particles of different sizes on different roads in Beijing, China.

Road-Deposited Sediments (RDS) samples were collected from four different roads in Beijing, and the distribution of pollutants in RDS with various particle sizes was compared. In this study, the cumulative mass of RDS exhibited a positive correlation with the number of dry days, and the RDS load below 75μm was also influenced by road traffic volume. As traffic volume escalated, there was a corresponding increase in the load of these smaller RDS. Most pollutants accumulated within RDS with sizes below 150μm, rendering them the primary contributors to the pollution. In terms of the antecedent dry-weather days, fifteen days emerged as a potentially pivotal point, with both the rate of pollutant accumulation and the contribution of pollution sources to RDS having stabilized after this duration. The origins of pollutants in roads of different functional areas exhibited certain disparities. The pollutants on major roads with high traffic volume were attributed to frequent vehicular activities. The pollutants on residential roads stemmed from soil particles and fallen leaves in the roadside green belts as well as from human activities. And the pollutants on urban-rural crossroad might be attributed to the industrial contamination with factories located on one side.

Assessment of Air Pollution Tolerance Index (APTI) and Expected Performance Index (EPI) of Plants along Nsukka, Enugu Road, Nigeria

Air pollution is a worldwide ecological problem that threatens human health through harmful emissions as vehicular exhaust emission. There is a need to examine ecological role of plants growing around major roads to identify their susceptibility and resistance towards air pollutants. Air Pollution Tolerance Index (APTI) and Expected Performance Index (EPI) of some plants growing along Nsukka - Enugu road in Enugu State, Nigeria were estimated to explore plants’ ability to absorb air pollutants. Twenty-eight plant species comprising herbs, shrubs and tree species were sampled and determination of APTI used combined biochemical parameters measurements. The APTI results showed that herbs are sensitive with APTI range of < 7 to 11.91, shrubs are mostly moderate tolerant with range of 9.38 to ≤ 17.43 while trees species are tolerant that ranged from ≥ 10.58 to ≥ 23.26 at t ≤ 1.51, p>0.05 in the experimental and control sites respectively. Based on APTI grades, Rauvolfia. vomitoria (11.91) had highest sensitive index and Erigeron canadensis (6.45) had the least, Persea americana (16.11) had highest moderate tolerant index and Ricinus. communis had the least while Terminalia. catappa (23.26) produced the highest tolerant index and Gmelina arborea (17.50) had the least in the experimental site. The EPI revealed that plants can be categorized as fit, very fit and unfit for plantation and those under ‘excellent to best’ performer category can be highly recommended as ‘very fit’ for plantation to control air pollution. Dacryodes edulis (94.74%) was in the ‘Best’ performer category and the best recommended species for plantation planting to monitor and control air pollutants.

Optimal investment strategies for decarbonizing the Indian private road transport sector

The Indian government's COP26 emission reduction target has led to explore strategies to decarbonize India's private road transport sector. Adoption of novel vehicle options such as E85, electric, and CNG vehicles and strong network of public transportation are expected to reduce the emissions significantly. A system dynamics model for India's private road transport sector has been developed previously. This study expands that model by incorporating the dynamics between public and private road transportation and uses it to identify strategy for optimal incentive allocation for novel vehicle adoption to minimize the GHG emissions. The idea of epsilon-constraint method for multi objective optimization has been used in this work. The results suggested a trade off between investment in incentives and reduction in GHG emissions. Incentivization strategy prioritized electric cars and two-wheelers, as well as charging/CNG stations, to discourage the adoption of petrol vehicles till CNG infrastructure was established. The minimum GHG emissions achieved was 535.5 Mt CO2e by 2050 with an investment of 137.74 trillion Indian Rupee when public transportation was not considered. Upon considering public transportation, the minimum GHG emissions further reduced to 464.5 Mt CO2e by 2050 with reduced investment of 128.25 trillion Indian Rupee, indicating greater emission reduction benefits per unit investment. However, beyond a certain threshold, increase in public transportation resulted in increased incentive investment due to a feedback effect. This necessitates incorporating dynamic analysis into policy strategy. Other strategies such as carbon tax and renewable share in electricity grid proves very effective in reducing GHG emissions as well as incentive investments. However, despite reducing emissions COP26 emission target for 2030 was missed by 34%. Banning the purchase of new petrol and diesel vehicles, along with restrictions on the use of existing petrol and diesel vehicles, could help bridge this gap.

TRAFFIC AND ROAD STUDY IN CONSTANTINE CITY: ANALYSIS OF TRAFFIC IN CONSTANTINE CITY

This study primarily aims to shed light on the traffic and road conditions in the city of Constantine. As a crucial measure in the transportation sector, traffic and road studies play a pivotal role in evaluating the efficiency of existing road networks, determining current and future traffic volumes, identifying bottlenecks, and proposing solutions through traffic redistribution and planning. To assess the current state of traffic and roads in Constantine and their adequacy in relation to the city's area and population, this study was conducted. Constantine was chosen due to its unique position as a hub for numerous major roads with high traffic volumes. Six out of the seven national roads traversing the entire province pass through Constantine. To comprehensively address the subject, the research is divided into three main sections: - Section One: Provides essential definitions related to the topic, such as road network, road capacity, traffic volume, etc. - Section Two: Delves into the details of traffic volume, its types, methods, and the significance of its study. - Section Three: Conducts a detailed analysis of traffic volume and roads in Constantine. Initially, the study relies on the administrative classification of roads to identify those within the study area. Subsequently, it analyzes the average traffic volume and traffic density on these roads.

Real-vehicle experimental validation of a predictive energy management strategy for fuel cell vehicles

Predictive information is highly valuable for energy management strategies (EMSs) of fuel cell vehicles. In particular, long-term predictions can significantly improve the fuel efficiency because they allow for an optimization of the energy management before departure. This potential has been demonstrated in numerous simulation studies. This work extends the literature with an extensive experimental validation of a predictive EMS that exploits route-based long-term predictions in the form of optimized reference trajectories for the battery state of charge. The experimental validation is performed with a real passenger fuel cell vehicle and strongly focuses on the real-world application where random influences such as traffic cause considerable disturbances with respect to the long-term prediction. The validation comprises two stages: First, real driving tests are repeatedly conducted on public roads, analyzing the robustness of the predictive EMS and assessing fuel efficiency gains over a nonpredictive EMS. Second, chassis dynamometer tests are performed where a selected real driving cycle is reproduced to compare the two EMSs directly. The chassis dynamometer tests confirm a significant reduction in the fuel consumption by 6.4% compared to the nonpredictive EMS. The experimental results are analyzed quantitatively and qualitatively in detail.

The Green Infrastructure Model Based On Waterlogging Mitigation In The Street Landscape Of Sungai Penuh City, Jambi Province

Background: In many cases, the planning of public and social space in Indonesia is not accompanied by the planning of green open space, which is their functions as a healthy environmental balancer. One of the public spaces is the street landscape. This results in to the impact of natural phenomena such as floods, waterlogging in the rainy season, and hot weather in summer. The purpose of this study is to provide an overview of the Green Infrastructure model of roads, can also function as a place to anticipate waterlogging, flooding, air quality improvement, and human-friendly designation. Materials and Methods: The methodology was carried out through field inventory, mapping based on Shp ArcGis data, and a modified literature study. The location of the research is on roads spread across 8 subdistricts of Sungai Penuh City, Jambi Province. Results: Based on government administration and axis content, the public roads of Sungai Penuh City are categorized into class III Provincial Roads. The number of road sections in Sungai Penuh City is 49 (forty-nine) road sections. The details are that the primary collector road landscape consists of 11 road sections, the secondary collector road landscape consists of 12 road sections, and the local road landscape is very dominant, which is as many as 26 roads. The total length of the entire Sungai Penuh City road section is 51.43 km with an average width of 6 meters. Based on the level of urgency, the results of the assessment of priority sub-districts determined 31 road sections spread across Sungai Penuh District, Sungai Bungkal District, and Pondok Tinggi District as priority sub-districts for the implementation of the Green Infrastructure system. Based on hydrological calculations of rainfall for the last 20 years (2003-2023). Conclusion: The Green Infrastructure Model is prioritized for 3 sub-districts. Based on the assessment of the Green Infrastructure Implementation Indicator, two green infrastructure concepts are applied, namely Green Stormwater Infrastructure 1 (GSI 1) and Green Stormwater Infrastructure 2 (GSI 2) which are equipped with the Complete Street concept. The GSI 1 system is applied on 24 roads in Sungai Penuh City using the Stormwater Tree Trench and planter. The GSI 2 system consisting of Stormwater Bump-Out, Stormwater Tree Trench, and Stormwater Planter and the Complete Street concept will be applied on 7 roads in Sungai Penuh City

Comparison of associations between proximity to major roads and all-cause mortality across a spectrum of cardiovascular diseases.

Global urbanization is leading to increased exposure to traffic-related air pollution (TRAP), which is associated with adverse health events. While individuals with cardiovascular disease (CVD) are known to have elevated susceptibility to air pollution exposure, no studies have evaluated how mortality risks associated with TRAP exposure differ based on the presence of CVD. We used three electronic health record-based cohorts to examine associations between proximity to major roadways and all-cause mortality. The three cohorts were a random sample of the hospital population, individuals with a prior myocardial infarction, and individuals with diagnosed heart failure (HF). We used Cox proportional hazards models to evaluate associations while adjusting for age, race, sex, and census block group socioeconomic status. Residing <250 m from a major roadway was associated with a hazard ratio (HR) of 1.13 (95% confidence interval = 1.05, 1.23) for individuals with HF, an HR of 1.07 (95% confidence interval = 0.96, 1.20) for those with a prior myocardial infarction, and an HR of 1.03 (95% confidence interval = 0.89, 1.20) for a random sample of hospital patients. This pattern persisted across several sensitivity analyses including alternative definitions of proximity to major roadways and matching the cohorts on demographics. These results highlight the differences in air quality-related health risks based on underlying CVD. Individuals with HF consistently had the highest environmental health risks. These results may better inform risks related to TRAP exposure in populations with differing underlying CVD.

Identification of accident-prone segments using APIU: A case study on highway safety analysis in China

Objective This study proposes the accident point interval unit (APIU) method combined with the characteristics of road traffic accidents. The aim is to automatically identify accident aggregation areas, providing basis for highway design and traffic management. Methods Historical accident data from a secondary highway in Guizhou Province and an expressway in Guangdong Province over 3 to 4 years were analyzed using APIU to identify accident-prone segments. A backpropagation (BP) neural network model was utilized to calculate the weight of the impact of the alignment on the occurrence of the accidents, which were then integrated with evaluation levels to formulate a risk index model. Results The APIU exhibited stability and consistency in identifying accident-prone sections, effectively accounting for the influence of adjacent road sections. The BP neural network model quantified the impact of road alignment on accidents, and the risk index model provided a comprehensive evaluation of road section risk. A significant risk zone was identified within 200 to 300 m before the accident staking point, validating APIU. Conclusions Using the APIU, accident-prone segments can be accurately identified. The risk indexes start rising within a specific range before the accident stakes, suggesting that road accidents are influenced by the geometric alignment preceding the accident point. Based on this insight, highway authorities can implement targeted safety measures and enhance signage in critical areas.

Dynamics of Zinder’s Urban Landscape: Implications for Sustainable Land Use Management and Environmental Conservation

Unplanned urban expansion poses significant challenges to environmental sustainability and urban planning. This study analyzes the spatiotemporal dynamics of Zinder’s urban landscape using Landsat satellite imagery from 1988, 2000, 2011, and 2022. The study applied remote sensing (RS), geographic information system (GIS) techniques, and urban growth models. The random forest classifier, a machine learning algorithm, was used to classify three land use/land cover categories: “vegetation”, “built-up”, and “others”. Zinder’s arid environment is characterized by sparse vegetation, which constitutes a limited but vital component of its landscape. Despite the already sparse vegetation in the area, the findings reveal a 3.5% reduction in vegetation cover between 1988 and 2022, alongside an 11.5% increase in “built-up” areas and an 8% decrease in the “others” category. This loss of already minimal vegetation raises significant concerns about environmental degradation and the exacerbation of desertification risks. Interestingly, urban expansion showed no significant correlation with population growth (r = 0.29, p &gt; 0.5), suggesting that other factors, such as economic activities, infrastructure development, and land use policies, drive land conversion. Edge expansion emerged as the dominant growth type, with a significant directional preference (Chi-Square = 2334.41, p &lt; 0.001) toward major roads and areas with higher accessibility to public services. These findings emphasize the need for strategic urban planning and land management policies to address the drivers of unplanned expansion. Prioritizing sustainable infrastructure development, enforcing land use regulations, and conserving natural landscapes are critical to balancing urban growth with environmental preservation, ensuring resilience and sustainability in Zinder.

Transcriptomic and epigenomic profiling reveals altered responses to diesel emissions in Alzheimer's disease both in vitro and in population-based data.

Studies have correlated living close to major roads with Alzheimer's disease (AD) risk. However, the mechanisms responsible for this link remain unclear. We exposed olfactory mucosa (OM) cells of healthy individuals and AD patients to diesel emissions (DE). Cytotoxicity of exposure was assessed, mRNA, miRNA expression, and DNA methylation analyses were performed. The discovered altered pathways were validated using data from the human population-based Rotterdam Study. DE exposure resulted in an almost four-fold higher response in AD OM cells, indicating increased susceptibility to DE effects. Methylation analysis detected different DNA methylation patterns, revealing new exposure targets. Findings were validated by analyzing data from the Rotterdam Study cohort and demonstrated a key role of nuclear factor erythroid 2-related factor 2 signaling in responses to air pollutants. This study identifies air pollution exposure biomarkers and pinpoints key pathways activated by exposure. The data suggest that AD individuals may face heightened risks due to impaired cellular defenses. Healthy and AD olfactory cells respond differently to DE exposure. AD cells are highly susceptible to DE exposure. The NRF2 oxidative stress response is highly activated upon air pollution exposure. DE-exposed AD cells activate the unfolded protein response pathway. Key findings are also confirmed in a population-based study.

Cohort Study of Indecent Exposure in the Netherlands from 2012 to 2020: Incidents, Perpetrators, Victims, and Trends over Time.

Indecent exposure is often regarded as a nuisance offense and detailed studies into this topic are relatively rare. However, there is consensus that relatively high recidivism rates and risk of escalation to more severe offenses can be of serious concern among these perpetrators. This cohort study aims to increase our general knowledge on the basic characteristics of these offenses and includes all registered police cases of indecent exposure in the Netherlands between 2012 and 2020, including 6741 incidents, involving 4663 suspects and 3808 registered victims. This first study of a large cohort over a long period of time describes the basic characteristics of these incidents, the perpetrators and their victims, and visualizes the results to explore trends over time. Results show that a modal indecent exposure incident is perpetrated by a 25-year-old male, on foot, on a public road, on a Wednesday afternoon in July, masturbating and directing his genitals intentionally toward a 13-year-old girl. The age distribution of victims shows remarkable similarity to victims of sexual assault. Compared to the first year of the period studied, the number of annually reported incidents gradually declined to half in the last year of the study. Findings are discussed in light of the most prominent theories on exhibitionism. Issues and suggestions relevant to apprehension and treatment of perpetrators are identified and discussed.

Capacity modeling for mixed traffic with connected automated vehicles on minor roads at priority intersections

ABSTRACT As connectivity technology advances, connected automated vehicles (CAVs), connected vehicles (CVs), and regular vehicles (RVs) form mixed traffic traffic at priority intersections. This paper presents an analytical capacity model to evaluate the impacts of such mixed flow on minor roads. We define two platoon types based on the leading vehicle: CAV-led and CV/RV-led platoons. Assuming headway distribution on the major roads follows the M3 distribution, we derive probabilities of these platoons passing through the intersection during a single time gap on a major road. According to the expected probabilities of different vehicle types, the probabilities of these two types of platoon configurations are obtained. Based on this, we establish a mixed traffic capacity model. The capacity model’s effectiveness is validated through numerical experiments. The results demonstrate that the proposed model can analyze the impacts of key factors on mixed traffic capacity, including CAV penetration rate, CV proportion, traffic volume on major roads, and vehicle headway. HIGHLIGHTS Propose an analytical capacity model for mixed traffic on minor roads at priority intersections. Conduct numerical experiments to validate the effectiveness of the proposed mixed traffic capacity model. The proposed model can be applied to evaluate the capacity impacts of connected automated vehicle penetration rate, connected vehicle proportion, traffic volume on major roads, and vehicle headway at priority intersections.

Integral recovery of an urban watershed through the implementation of nature-based solutions

Unplanned urban expansion in Latin America has profoundly disturbed the natural hydrological processes of watersheds, particularly in tropical regions with extreme weather conditions. This has resulted in substantial runoff volumes, necessitating extensive engineering interventions to avoid damage. Urbanization has led not only to flooding problems, but also to a lack of recreational spaces for residents and an ecological degradation, negatively affecting the watersheds health. This study shows a methodology for creating scenarios making use of available sites for Nature-based-Solutions (NbS). Taking account of existing land cover, the methodology harnesses NbS multifunctionality to achieve hydrological, ecological and social benefits counteracting the threats to the health of a 23 km2 urban tropical watershed located in Costa Rica. In these scenarios, suitable public roads in residential areas were used as sites for implementing permeable pavement, bioretention cells, infiltration trenches and street planters, while appropriate flat roofs and car parks were converted into green roofs and permeable pavements in industrial areas. In addition, several green public spaces were transformed into multifunctional storage areas. Hydrological and hydraulic modeling was used to evaluate the performance of each scenario, while the increase in green area of each scenario was used as an indicator of the ecological and social benefits. The concurrent implementation of the residential, industrial and green space scenarios provides an area available for NbS of 165.3 ha (7.2% of the watershed area), potentially leading to a decrease of up to 63.7% in peak flow and up to 46.2% in runoff volume for a specific rainfall event. As haphazard urban growth is a common feature of most urban watersheds in Latin America, this methodology, based on the use of different land cover types to create scenarios for NbS implementation, is transferable to and adaptable for other tropical urban watersheds.

From alleys to arterial streets: Unpacking the influence of diverse street types and associated business on urban theft patterns

Street segments have witnessed growing scholarly attention for their pivotal role in determining urban crime patterns. However, existing studies have not thoroughly considered the effects of refined street types on crime, let alone their interactive effects with business facilities on crime. This research aims to fill these gaps, by examining the relationship between theft, street type and Facilities in Cincinnati. Results show that alleys and pedestrians are less susceptible to theft than major and minor arterial streets. Interactive terms between street type and Facilities enhance the model's performance and reveal that the concentration of thefts is positively related to the association between Facilities and local streets or major arterial streets. Our findings offer not only add to the existing literature, but also offer insight on urban safety and planning practice.

A Trial of Psychological Evaluation of a Driving-Support Agent That Praises the Driver Through an Actual-Car Driving Experiment on Public Roads

A Trial of Psychological Evaluation of a Driving-Support Agent That Praises the Driver Through an Actual-Car Driving Experiment on Public Roads

Correction: Holzer, K.; Poor, C. Reduction of Runoff Pollutants from Major Arterial Roads Using Porous Pavement. Sustainability 2024, 16, 7506

The authors would like to make the following corrections to the published paper [1]:(1)In the abstract, the word “overly” should be replaced by word “overlay” in the following sentence: [...]

Paying Up

This article describes the mechanisms by which the Puerto Rico Highways and Transportation Authority (PRHTA), a state agency created “para dar al pueblo las mejores carreteras” (to give the people the best highways), has exacerbated uneven resource distribution in Puerto Rico. The PRHTA has functioned as a conduit for regressive restructuring—the work of building financial and physical infrastructures that transfer public wealth to elites. The function of the PRHTA is emblematic of how this politics of regressive social transfer is enabled by colonial capture—the articulation of colonial structures of governance to insulate political-economic power and public institutions from grass-roots and electoral mandates. This paper’s first section offers a brief historical background of the refashioning of Puerto Rico’s colonial politics since the 2016 debt crisis and the longer durée infrastructures to which these political changes pertain. The second section argues that, prior to 2016, political officials refashioned Puerto Rico’s public highways as revenue corridors for global capital in ways that anticipated and fostered the crisis. Contracts between the PRHTA, financial institutions, and private toll road operators reshaped key transportation infrastructures as avenues for the continuing capture of public resources through service fees and payments to bondholders. The final section discusses the material resources that these “public” services effectively transfer to private interests. Compared to median income from 2012 to 2021, the data on tolling rates begin to suggest, although hardly describe in full, the inequitable economic effects of these politics.

Relationship Between Road Traffic Composition and Noise Emission: an Investigation Based on Hungarian National Public Road Traffic Data

Different vehicles contribute to the noise emission of the total road traffic in different ways and degrees. Therefore, emission calculation methods classify road vehicles based on their typical noise emission parameters. This article investigated the role of the four vehicle categories of CNOSSOS-EU method (light motor vehicles, medium heavy vehicles, heavy vehicles, and powered two-wheelers) in generating road traffic noise emissions. Based on traffic data available for national public roads in Hungary, the proportion of each vehicle category in the total road traffic was determined, providing the average distribution and other statistical parameters for each road category. The impact of changes in the traffic composition on noise emission was analysed based on the statistical parameters. The results show the dominance of light vehicles for all road categories (76–89% on average). The average proportion of heavy vehicles is also significant on controlled-access highways (motorways: 21%, expressways: 15%) and main roads (9%). The deviation from the average traffic composition can cause a noticeable difference in road traffic noise emission (up to nearly 3 dB). It was found that the impact of changing the proportion of heavy and light vehicles on noise emission is higher on roads inside built-up areas.

Abstract 4132875: Living Address Environmental Biotope Affects the Risk of Coronary Obstruction and Myocardial Ischemia Detected by Coronary Computed Tomography

Background: Several studies reported positive associations between air and noise pollution exposure and coronary artery calcification, but environmental effects on coronary obstruction and flow remain largely unknown. Hypothesis: Distance to road and airport, air pollution, and residential green space are linked to coronary artery obstruction and myocardial ischemia. Low CV-risk patients develop coronary obstruction due to environmental factors, while high-risk patients are more prone to develop myocardial ischemia. Methods: Consecutive patients screened by coronary CT angiography for stable chest pain between 01/01/2019 and 31/12/2020 were included in a registry. Distance to airport and major road, and exposure to PM2.5, NO 2 and normalized difference vegetation index (NDVI) were modelled at patients’ living address. Risk of obstructive coronary artery disease (OCAD, stenosis ≥50%) and abnormal fractional flow reserve (FFR CT &lt;80%) was assessed using propensity score (PS)-adjusted logistic regression. Subgroup analysis was performed according to gender and Framingham CV-risk categories. Results: We enrolled 2620 patients, including 420 (16%) FFR CT analyses. OCAD was reported in 518 (20%) patients and abnormal FFR CT in 276 (11%). People with OCAD lived closer to the airport (10.841 [7.869-19.022] km vs. 12.297 [8.691-22.843] km, p&lt;0.001). Distance to airport decreased OCAD risk (OR 0.983, 95%CI 0.974-0.992, per 1 km) in univariate analysis, with the association present in male, female, and low-risk patients. After PS-adjustment, a trend of association was present between airport distance and abnormal FFR CT risk (OR 0.986, 95%CI 0.969-1.003, per 1 km). Road distance tended to decrease OCAD risk in univariate and PS-adjusted analysis, in the general (OR 0.928, 95%CI 0.808-1.309; and OR 0.928, 95%CI 0.840-1.021, per 1 km) and high-risk population. NO 2 exposure tended to increase OCAD risk in univariate analysis (OR 1.012, 95%CI 0.997-1.027, per 1 µg/m 3 ), with the association only present in female patients, and disappearance after PS-adjustment. PM2.5 exposure showed no associations, while NDVI tended to increase OCAD risk in low-risk patients after PS-adjustment. Conclusion: Residential distance to airport is the major risk component compared to other urban exposome factors studied in this epidemiological observation. Green space tends to exert a cardioprotective effect in low-risk patients, whereas living close to major road worsens the risk in high-risk patients

Assessing Chile's national expansion of urban and artificial coverages between 2000 and 2022

Abstract. This study investigates the national expansion of urban and artificial coverages in Chile between 2000 and 2022 using remote sensing techniques. The research employed Google Earth Engine and Landsat imagery to classify land use and land cover (LULC) changes. By analyzing annual cloud-free composites and employing random forest algorithms, the study produced detailed LULC maps, focusing on the 'infrastructure' class, which includes urban areas and major roads. Results indicate a significant 33% increase in infrastructure coverage, growing from approximately 380,000 hectares in 2000 to 510,000 hectares in 2022. This expansion was predominantly observed in seven regions, which accounted for 68% of the total infrastructure area and 83% of the national increase. A notable correlation (0.6) between urban growth and population growth was identified, suggesting that urban expansion is largely driven by demographic changes. However, regions with higher population growth exhibited more vertical urban development. The study highlights the complex interplay between urbanization, population dynamics, and regional planning, emphasizing the need for sustainable development strategies to manage urban growth effectively. Validation efforts showed high classification accuracies, with infrastructure class accuracies averaging 82% for the User and 87% for the Producer across the evaluated years.