Road Connectivity Research Articles

Estimating On-Road Transportation Carbon Emissions from Open Data of Road Network and Origin-Destination Flow Data

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Charging the non-networked: Water pricing governance of the heterogeneous infrastructures beyond the utility network in Dar es Salaam

Though urban scholars have drawn our attention to the multiple water infrastructures serving urbanites in the global South, studies rarely explored the practice through which prices are produced and governed within the heterogeneous infrastructures that supply water beyond the utility. Drawing perspectives from everyday pricing practices and heterogeneous water infrastructures, we contribute to the scientific discourse on heterogeneous infrastructures, everyday practices and infrastructure governance by showing how multiple infrastructural systems beyond the utility network, such as hydro-mobile and private network water providers produced prices to mediate water collection. Prices were established based on the cost of electricity, fuel, repairs and maintenance, location and/or distance, nature of road connectivity to clients’ residences, and providers’ expected profit margins. Water providers’ discretions and learning by doing enabled the continuity of pricing practices. The conventional practice of non-collective negotiation and bargaining produced specific prices between water providers and end-users. The novelty of the paper emanates from the ways in which prices are produced and governed. In contrast to conventional tariff systems, reflectivity, creativity, practical knowledge and experiences acquired by non-state actors over time works to produce prices. The involved non-state actors exercised regulatory power over prices of water produced and supplied beyond the utility. When prices were established, they remained subject to modification. We argue that the focus on pricing sheds light on an important aspect of heterogeneous infrastructure provision and governance: where varied prices are established outside formal regulation, they reflect, shape and exacerbate fine-grained socio-spatial differences between individuals within single neighbourhood.

Exploring the Built Environment Factors Influencing Town Image Using Social Media Data and Deep Learning Methods

The representational image of the city has attracted people’s long-term attention. Nevertheless, the mechanism of interaction between the image and the built environment (BE) and image studies at the town scale have not been fully explored. In this study, we collected multi-source data from 26 characteristic towns in Tianjin, China. We explored a deep learning approach to recognize social media data, which led to the development of quantifiable town uniqueness image (UI) variables. We studied the influence of the BE on the town UI and the moderating effects of positive emotions on the relationship between the two. The results showed that positive emotions had significantly positive moderating effects on the water system ratio’s effect on UI, but weakened sidewalk density and tourist attraction density. They also inhibited the negative effects of road connectivity but could strengthen the negative effects of the sky view factor and points of interest (POI) mix. The moderating effects on other variables are relatively mediocre. This study helps to reveal the inner mechanism of BE and town image. It is conducive to accurately coordinating the relationship between planning policies and design strategies, optimizing resource allocation, and promoting sustainable town development.

Visualizing road connectivity under traffic influence: a CiteSpace-based method

ABSTRACT The connectivity of roads under the influence of traffic is determined by the topological connections established by the road network and the transport connections formed by moving vehicles between two roads simultaneously. A thorough examination of this connectivity is crucial for comprehending traffic patterns and providing guidance to address transportation issues such as traffic congestion. However, existing methods ignore the intuitive representation and explanation of this connectivity, limiting the understanding of actual traffic patterns. This study aimed to intuitively visualize the connectivity of roads under the influence of traffic using CiteSpace, a bibliometric tool for analyzing the relationship between literature keywords. Here, the literature was replaced by trajectory, and keywords were replaced by roads. We then presented the connectivity of roads under the influence of traffic in a network, where nodes represented roads and edges represented the topological and transport connections between roads. A case study was conducted in Beijing, demonstrating the dynamic connectivity of roads under the influence of traffic in the visualized network. By comparing the visualized network with the topological road network, we identified three types of road connectivity under the influence of traffic: roads with topological connections but lacking transport connections, roads with transport connections but lacking topological connections, and roads with both topological and transport connections. This finding further revealed the traffic relationship between roads. Additionally, we simultaneously presented the multidimensional traffic characteristics in the visualized network, aiding in predicting traffic states.

Towards better public transport services: Understanding service quality attributes of public transport in states of developing countries

This research investigates the service quality attributes of PT and the influence of the attributes on users’ satisfaction. Improving public transport (PT) facilities such as road networks and connectivity systems must align with service quality aspects. This research investigates the service quality attributes of PT and the influence of the attributes on users’ satisfaction. A survey was conducted to examine users’ perceptions of the service quality of PT in Sarawak, Malaysia. Service quality indicators have been chosen based on service quality attributes in previous research and analyzed using factor analysis (FA) to group the indicators into service quality attributes. Conformity Factor Analysis (CFA) was used to determine which attributes could significantly impact users’ satisfaction. Based on the survey results, the service quality of PT services in Sarawak is average, as most of the mean scores for each indicator of service quality and user satisfaction are less than 4.0. Four service quality attributes have been obtained from FA, i.e., vehicle condition, customer service, travel experience, and accessibility. The CFA analysis and structural model results showed that the attributes of vehicle condition, customer service, and accessibility significantly positively impact users’ satisfaction. The findings in this research can be used as references for PT providers, policymakers, and the government in improving the service quality of PT in Sarawak State.

Road Access, Fertility, and Child Health in Rural India

AbstractExpansion in access to public infrastructure can have varied, microlevel impacts. In this paper, we use a discrete and quasi‐random change in the access to paved roads through a large‐scale rural road construction program in India to study how road access impacts fertility decisions and investments in child health. We find that increased access to paved roads at the district level decreases fertility, improves investments in children, and lowers infant mortality. We also provide evidence that highlights the mechanisms that drive this effect. First, we show that local roads improve access to health care facilities and raise immunization rates, which reduces infant mortality. Then, we demonstrate that last‐mile road connectivity has contrasting effects on employment across gender. Overall, the evidence suggests that rural roads can help accelerate demographic transition through their effects on fertility and infant mortality.

Microsatellite-based analysis reveals Aedes aegypti populations in the Kingdom of Saudi Arabia result from colonization by both the ancestral African and the global domestic forms.

The Aedes aegypti (Linnaeus, 1762) mosquito is the main vector of dengue, chikungunya and Zika and is well established today all over the world. The species comprises two forms: the ancestral form found throughout Africa and a global domestic form that spread to the rest of the tropics and subtropics. In Saudi Arabia, A. aegypti has been known in the southwest since 1956, and previous genetic studies clustered A. aegypti from Saudi Arabia with the global domestic form. The purpose of this study was to assess the genetic structure of A. aegypti in Saudi Arabia and determine their geographic origin. Genetic data for 17 microsatellites were collected for A. aegypti ranging from the southwestern highlands of Saudi Arabia on the border of Yemen to the north-west in Madinah region as well as from Thailand and Uganda populations (as representatives of the ancestral African and global domestic forms, respectively). The low but significant level of genetic structuring in Saudi Arabia was consistent with long-distance dispersal capability possibly through road connectivity and human activities, that is, passive dispersal. There are two main genetic groupings in Saudi Arabia, one of which clusters with the Ugandan population and the other with the Thailand population with many Saudi Arabian individuals having mixed ancestry. The hypothesis of genetic admixture of the ancestral African and global domestic forms in Saudi Arabia was supported by approximate Bayesian computational analyses. The extent of admixture varied across Saudi Arabia. African ancestry was highest in the highland area of the Jazan region followed by the lowland Jazan and Sahil regions. Conversely, the western (Makkah, Jeddah and Madinah) and Najran populations corresponded to the global domesticated form. Given potential differences between the forms in transmission capability, ecology and behaviour, the findings here should be taken into account in vector control efforts in Saudi Arabia.

Analysis of the Influence of Public Transport Fleet on Regional Development through Community Accessibility and Road Connectivity in the City of Lhokseumawe

Currently, a new concept in the transportation system has developed, namely a sustainable transportation system. The purpose of this research is to analyze the effect of the public transportation network system on community connectivity in Lhokseumawe City. The analytical method used in this research is a descriptive quantitative method using primary and secondary data. Quantitative descriptive is a type of research that is used to analyze data by describing or describing the data that has been collected as it is. This research uses multiple linear regression analysis tools. The results showed that the number of fleets, passenger capacity, routes had a positive and significant effect on the accessibility of the people in Lhokseumawe City. The number of fleets, passenger capacity and public transport routes have a positive and significant effect on community connectivity. The results of research on community accessibility and road transportation connectivity have a positive and significant effect on the development of the Lhokseumawe City area.

Facilitators and barriers to the uptake of COVID-19 vaccine precaution dose among adult population: qualitative analysis across six different states of India.

India launched the COVID-19 vaccination drive on 16th January 2021 by vaccinating the adult population above 18 years of age. This was followed by the introduction of an additional precaution dose. As on 18th October 2022, 1,02,66,96,808 (1.02 Billion) first dose and 94, 95, 39,516 (949 Million) second doses of COVID-19 vaccine were administered. However, when compared to the uptake of the primary doses, the precaution dose uptake lagged behind with only 21,75, 12,721 (217 million) doses administered. Even though, the uptake of the primary doses remained optimal, irrespective of different interventions by the Government of India, the uptake of the precaution dose remained poor. In this context, the Ministry of Health & Family Welfare wanted to understand the facilitators and Barriers for precaution dose uptake among adults so that future immunization campaigns could address these issues. An exploratory qualitative study was conducted to assess the facilitators and barriers for COVID-19 precaution dose uptake at community level across 6 different states in India. From each of the states, two districts with the highest and lowest rates of COVID-19 vaccine precaution dose uptake were selected. In each of these districts, 2 block Primary Healthcare Centres (PHCs), one with high and one with low uptake were identified. Within these block PHCs, a PHC field area with high and low precaution dose uptakes was identified. From the identified sites a minimum of four IDIs, four FGDs were conducted among the community members. KIIs of the State Immunization Officers, District Immunisation Officers, PHC Medical Officers, healthcare workers like Accredited Social Health Activist/Auxiliary Nurse Midwife were also conducted. The data was audio recorded and it was transcribed, translated and analysed using framework approach. It was observed that rise in COVID-19 cases prompted the community to take the precaution dose, this along with the cost of hospitalization and the number of productive days being lost as a result of being infected resulted in vaccine uptake. The fear of non-availability of COVID-19 vaccines latter on also prompted people for vaccine uptake. While the barriers were, poor accessibility to vaccination centers, long hours of travel, poor road connectivity and lack of transportation facilities. However, the most prominent barriers observed across all study sites was that a sense of pandemic fatigue and complacency had developed both among the providers as well as the beneficiaries. Other barriers include differences in vaccination schedules and longer duration between the primary doses of some vaccines. Media was identified to be both a barrier and facilitator for Covid-19 Precaution dose uptake. Even though media played an important role in disseminating information in the beginning of the campaign, it was soon followed by the circulation of both misinformation and disinformation. The study identified that dissemination of accurate information and community involvement at each stage of planning and implementation are crucial for the success of any campaign. Efforts should be constantly made to address and re-invent strategies that will be most suitable for the needs of the community. Therefore, in order to ensure successful vaccination campaigns, it is crucial that along with political will it is also important to have a decentralized approach with inter-sectoral coordination with different stakeholders such as healthcare workers, community members and the different departments such as the local self-governments, education department, law & order department etc. These lessons learnt from COVID-19 vaccination campaigns must not be forgotten and must be applied in future vaccination campaigns and while framing public health policies.

Road networks and socio-demographic factors to explore COVID-19 infection during its different waves

The COVID-19 pandemic triggered an unprecedented level of restrictive measures globally. Most countries resorted to lockdowns at some point to buy the much-needed time for flattening the curve and scaling up vaccination and treatment capacity. Although lockdowns, social distancing and business closures generally slowed the case growth, there is a growing concern about these restrictions' social, economic and psychological impact, especially on the disadvantaged and poorer segments of society. While we are all in this together, these segments often take the heavier toll of the pandemic and face harsher restrictions or get blamed for community transmission. This study proposes a road-network-based networked approach to model mobility patterns between localities during lockdown stages. It utilises a panel regression method to analyse the effects of mobility in transmitting COVID-19 in an Australian context, together with a close look at a suburban population’s characteristics like their age, income and education. Firstly, we attempt to model how the local road networks between the neighbouring suburbs (i.e., neighbourhood measure) and current infection count affect the case growth and how they differ between delta and omicron variants. We use a geographic information system, population and infection data to measure road connections, mobility and transmission probability across the suburbs. We then looked at three socio-demographic variables: age, education and income and explored how they moderate independent and dependent variables (infection rates and neighbourhood measures). The result shows strong model performance to predict infection rate based on neighbourhood road connection. However, apart from age in the delta variant context, the other variables (income and education level) do not seem to moderate the relationship between infection rate and neighbourhood measure. The results indicate that suburbs with a more socio-economically disadvantaged population do not necessarily contribute to more community transmission. The study findings could be potentially helpful for stakeholders in tailoring any health decision for future pandemics.

Dual convolutional network based on hypergraph and multilevel feature fusion for road extraction from high-resolution remote sensing images

ABSTRACT Road extraction from high-resolution remote sensing images (HRSI) is confronted with the challenge that roads are occluded by other objects, including opaque obstructions and similarly colored areas. This paper proposes a dual convolutional network based on hypergraph and multilevel feature fusion (DHM) for road extraction to address these challenges. The DHM consists of two branch networks (HGNN branch and CNN branch) and a bimodal feature fusion module (BFFM). In the HGNN branch, an algorithm is developed to exploit the shape features of roads and construct hypergraphs on the HRSI. Then, hypergraph neural networks are employed for the first time to capture the long-range context of roads to enhance road connectivity. In the CNN branch, a bottleneck fusion module integrated with an encoder-decoder network structure is built to aggregate multiscale local features. In BFFM, the long-range context from the HGNN branch and the local features from the CNN branch are fused through the designed position converter and enhanced graph reasoning module to achieve the complementary advantages of the dual-branch network. Extensive experiments on three datasets show that DHM outperforms other state-of-the-art methods, especially on the GS-Mountain road dataset. Furthermore, DHM significantly improves road extraction in occluded and similar road regions.

Using a Space Syntax Approach to Enhance Pedestrians’ Accessibility and Safety in the Historic City of George Town, Penang

Contemporary urban development places a critical emphasis on pedestrian environments, especially in historic cities like George Town, which is a UNESCO World Heritage Site in Malaysia. Although survey questionnaires effectively captured public perceptions of issues such as poor road connectivity, weak accessibility, crime and safety concerns in George Town, they fell short in providing a comprehensive understanding of the root causes. This study leverages space syntax theory to model and analyze George Town’s unique pedestrian landscape, aiming to identify strategies for improving pedestrian networks in historical urban landscapes. Space syntax theory, known for revealing structural issues within urban contexts, is applied after a thorough examination of George Town’s urban layout, climate, architectural features, and development policies. George Town employs an informal grid layout widely utilized in British colonial port cities to enhance overall efficiency. The predominant architectural form is the shophouse, which is characterized by a ground level designed for pedestrian movement known as the “five-foot way” and adapted to Malaysia’s climate. Various axis drawing methods for the unique five-foot way under different circumstances are considered. The George Town special area plan (SAP) emphasizing heritage preservation guides development policies, thus requiring an inclusive approach to pedestrian environments. This enhances the practical significance of the current study, with the eastern and northern coastal areas serving as crucial focal points for investigation. This approach results in a comprehensive spatial model that captures the essence of George Town’s pedestrian landscape. Evaluation using space syntax indicators such as connectivity, integration, intelligibility, and choice reveals issues like poor overall network connectivity, inadequate access to key attractions, suboptimal integration, concentrated pedestrian flows, and significant safety concerns, which are exacerbated by limited infrastructure on certain two-way roads and a lack of zebra crossings. The practical implications of this study include recommendations for enhancing pedestrian spaces along identified roads and strategically installing zebra-crossings. This research is significant for its focus on a historical city in a Southeast Asian developing country, deeply integrating local environmental characteristics and providing insights into urban planning and optimization, thereby serving as a reference for similar cities.

Incorporating Road Network Connectivity in Neighboring Structures for Crash Prediction Models at the Area Level

Spatial correlation models have been traditionally used in road safety to account for spatial effects resulting from unmeasured or unknown risk factors that induce spatial correlation between neighboring areas. In transportation, the interaction between neighboring areas is highly influenced by the number of roads that connect those areas and the importance of those roads. This paper proposes an approach in which the weights of the spatial interaction (and therefore the spatial correlation) between areas depends on the number of road connections between those areas and the importance of those connections. The results using districts in Costa Rica show that the inclusion of road network connectivity in the models of spatial correlation significantly improves model fit, even after accounting for model complexity using the deviance information criterion (DIC) and widely applicable information criterion (WAIC). The inclusion of higher weights for national roads compared to municipal or local roads further improved the model fit. The best three models with respect to the posterior deviance, DIC, and WAIC are those that give at least three times more weight to national roads compared to local roads. With respect to site ranking, those three models present similar results, which also highlights the consistency among those models.

Drone a technological leap in health care delivery in distant and remote inaccessible areas: A narrative review.

In developing countries, last-mile delivery of medical products is a challenge, especially in hilly and rural areas where there is no road connectivity. As helicopters or other air services are not affordable all the time, drones can be used for the supply of medical products. They are cost-effective as compared to other air or road transport. However, the carrying capacity of drone is less, it is not able to carry heavier payloads. Also, operating drones requires trained operators, and it is a new venture in a developing country so possibilities of confusion and lack of clarity on operating procedures are there. Drones are becoming increasingly reliable for the health care delivery. This narrative review explores the use of drones in healthcare delivery globally.

Performance Evaluation of Reinforcement Learning-Based Adaptive Signal Control Deployment in Urban Network

The adaptive traffic control system (ATCS) has shown great potential to address traffic congestion, fuel consumption, and air pollution issues because of its strong adaption to traffic fluctuation. With the popularity of artificial intelligence, Reinforcement Learning (RL) has been applied to ATCS and achieved excellent performance. In RL-based ATCS, the controller collects traffic state information and learns a better signal control policy by minimizing travel delay. For ATCS of an area-wide network, most existing studies assumed that all intersections are installed with ATCS. However, fully ATCS networks are difficult to achieve in the real world due to the high upgrade cost. Therefore, a practical ATCS deployment strategy in a network is needed for agencies with limited budgets. This study proposes a concept of penetration rate, or the percentage of intersections with ATCS in an area, for intelligent intersections. The weighted PageRank algorithm determines a reasonable deployment plan based on road connectivity and link volume. The relationship between penetration rate and traffic performance is investigated in the SUMO simulation. It reveals that the average stopped and control delays gradually decreased with the penetration rate increase. When the penetration rate reaches 75%, the ATSC performance is about 90% of the optimum. However, inequity and inefficiency exist in non-intelligent intersections regarding average queueing time and length at the penetration rate below 40%. Consequently, the suitable range of penetration rate should be between 40% and 75% to balance the costs and benefits of ATCS deployment with budget constraints.

RoadCorrector: A Structure-Aware Road Extraction Method for Road Connectivity and Topology Correction

RoadCorrector: A Structure-Aware Road Extraction Method for Road Connectivity and Topology Correction

Drivers of changing cropping patterns in Nepal: A review

This paper reviews various aspects of drivers of changing cropping patterns in Nepal based on secondary data sources. Data were acquired from Google Scholar and Research Gate database 32 relevant professional journal articles in between 2000-2022 were intensively reviewed. The drivers of changing cropping patterns are complex and interrelated in the farming system. Spatial analysis of cropping patterns of different geographical scales shows that cropping pattern is an integral part of a behavioral focus to reflect the space relation of the community people. The drivers of changing cropping patterns are numerous and intricately linked. It is challenging to gauge the relative significance of the effects ascribed to each scale of the driver or the interactions between them at any particular geographic region. The study revealed that there are several types of drivers for changing cropping patterns. Among these various types of drivers, the theme of technological advancement/inputs, climate change, infrastructural development, road connectivity, and market demand play prominent roles in changing cropping patterns however other drivers such as off-farm employment, agriculture service extension/training, government policies, population growth/change/migration (rural to urban; international.), are also remarkable in this studies. In the Nepalese context, this study indicates that the more prominent role playing drivers of changing cropping patterns are technological advancement /inputs, infrastructural development and road connectivity, and market demand and climate change however land degradation and government policies also play a role to shifting cropping patterns. These all drivers interact and link with each other, leading to changing cropping patterns in Nepal.

AMFC: A New Approach Efficient Junctions Detect via Maximum Flow Approach

In this study, max flow analysis processes are carried out with a graph theory-based approach that can be used in optimizing the traffic load in transportation networks. The data used in the study consists of 2 years of vehicle number data consisting of 438 million vehicle passes of a real city. Bottleneck points affecting traffic flow, maximum flow values, and effectiveness values of traffic generating and attracting locations were determined in the uniquely created transportation network. The Ford-Fulkerson algorithm was used to determine the maximum flow and bottleneck road connections in the designed transportation network. According to the maximum traffic flow to the junction point, the most effective junction points were determined by the PageRank algorithm. In addition, a unique algorithm is presented in the study that determines the effective intersection points that transfer vehicle traffic at maximum capacity to all junction points according to the maximum demand capacity data.

Incorporating Socio-Economic Factors in Maximizing Two-Dimensional Demand Coverage and Minimizing Distance to Uncovered Demand: A Dual-Objective MCLP Approach for Fire Station Location Selection

In this study, we employ a dual-objective optimization model, utilizing the Implicit Modified Coverage Location Problem (MCLP-Implicit) approach, to determine an appropriate fire station allocation. Our objectives encompass maximizing coverage in areas with a heightened projected demand, based on socioeconomic predictors of building fires, and concurrently minimizing the distance of uncovered demand zones to the closest fire station. The challenges of this model reflect the criticality of strategic placement, aiming for not just swift response times but also the complete coverage of a region with priority given to subregions with a pronounced potential for incidents. The applicability of the proposed approach is demonstrated through a case study in south-east Queensland. Our findings indicate that there is a tangible justification for adopting this model. The broader coverage and wider spread of locations it produces can greatly aid in the dynamic deployment of personnel during surges caused by seasonal fluctuations or unforeseen calamities. By weaving in socioeconomic aspects into demand predictions, our model has also maintained appropriate coverage to socioeconomically disadvantaged communities in the region. Nevertheless, it is paramount to underline the recommendation that further research should account for road connectivity—a pivotal factor when pinpointing these locations in the real world. This research paves the way for enriched insights in long-term urban planning and fire response protocol crafting, especially in regions mirroring similar socioeconomic profiles or risks of natural disasters, not to mention its commercial implications to the relevant industry servicing the fire and rescue authorities.

Prediction of transportation index for urban patterns in small and medium-sized Indian cities using hybrid RidgeGAN model

The rapid urbanization trend in most developing countries including India is creating a plethora of civic concerns such as loss of green space, degradation of environmental health, scarcity of clean water, rise in air pollution, and exacerbated traffic congestion resulting in significant delays in vehicular transportation. To address the intricate nature of transportation issues, many researchers and planners have analyzed the complexities of urban and regional road systems using transportation models by employing transportation indices such as road length, network density, accessibility, and connectivity metrics. This study addresses the complexities of predicting road network density for small and medium-sized Indian cities that come under the Integrated Development of Small and Medium Towns (IDSMT) project at a national level. A hybrid framework based on Kernel Ridge Regression (KRR) and the CityGAN model is introduced to predict network density using spatial indicators of human settlements. The major goal of this study is to generate hyper-realistic urban patterns of small and medium-sized Indian cities using an unsupervised CityGAN model and to study the causal relationship between human settlement indices (HSIs) and transportation index (network density) using supervised KRR for the real cities. The synthetic urban universes mimic Indian urban patterns and evaluating their landscape structures through the settlement indices can aid in comprehending urban landscape, thereby enhancing sustainable urban planning. We analyzed 503 real cities to find the actual relationship between the urban settlements and their road density. The nonlinear KRR model may help urban planners in deriving the network density for GAN-generated futuristic urban patterns through the settlement indicators. The proposed hybrid process, termed as RidgeGAN model, can gauge the sustainability of urban sprawl tied to infrastructure and transportation systems in sprawling cities. Analysis results clearly demonstrate the utility of RidgeGAN in predicting network density for different kinds of human settlements, particularly for small and medium Indian cities. By predicting future urban patterns, this study can help in the creation of more livable and sustainable areas, particularly by improving transportation infrastructure in developing cities.