Impact of PMGSY Roads on Education: A Case Study on Dudu and Chaksu Blocks of Jaipur, Rajasthan

Rural roads contribute significantly to the socio-economic and cultural development of rural habitations/villages. Improved road infrastructure facilitates rural population to look for work beyond their respective communities. They fuel the development of non-farm markets and create possible livelihood diversification opportunities for rural people, thereby decreasing their liability to economic distresses. The developing countries like India have been introducing various schemes to improve the status of their citizens. In 2000, Government of India launched Pradhan Mantri Gram Sadak Yojana (PMGSY) with a broad objective of improving the mobility of the rural population to achieve sustainable development in a phased manner. This article proposes a comprehensive approach to investigate the impact of the development of rural road infrastructure on livelihood diversification of the target population at the habitation level by employing fuzzy framework. It also accounts econometric modeling to assess the influence of diversification and control variables on household income. The effectiveness of the proposed model is illustrated by taking a case study of the construction of PMGSY roads in Jhunjhunu district of Rajasthan state, India. The findings reveal that newly constructed roads have been used prominently for agricultural activities. However, there is minimal growth in other occupations leading to low diversification of livelihood due to unawareness and lack of resources. It is, therefore, necessary that the policy makers should focus to implement reliable schemes to maximize livelihood diversification in a sustainable way. Thus, rural population can be served in an effective manner by promoting both farm and non-farm activities.

The Pradhan Mantri Gram Sadak Yojana (PMGSY), launched in 2000, has been one of India’s flagship programs aimed at enhancing rural connectivity and inclusive growth. Despite its nationwide success, the implementation trajectory across states reveals significant disparities. This paper critically evaluates PMGSY implementation in Haryana during 2016–2023, focusing on policy design, fund utilization, execution quality, and institutional accountability. Haryana, despite its above-average road density and receipt of over ₹2,500 crores under PMGSY, continues to underperform relative to neighbouring states such as Punjab and Rajasthan. The study employs a mixed-method research design combining quantitative analysis of secondary data with qualitative case studies from Bhiwani and Hisar districts. Data were sourced from the Ministry of Rural Development (MoRD), the National Rural Infrastructure Development Agency (NRIDA), Comptroller and Auditor General (CAG) reports, and Haryana Statistical Abstracts. Analytical tools include inter-district comparison, fund-utilization efficiency ratios, content analysis of implementation documents, and field-level policy matrix evaluation. Comparative benchmarking with Punjab and Rajasthan is used to contextualize Haryana’s performance. The results demonstrate persistent implementation inefficiencies and governance gaps. Between 2016 and 2023, fund utilization in Haryana fluctuated between 76 and 90 percent, accompanied by disbursement delays of up to eight months. Only 47.2 percent of the targeted rural roads were completed, compared to 89 percent in Punjab and 93.5 percent in Rajasthan. Quality-control issues affected nearly 18 percent of projects, indicating weak contractor supervision and limited third-party auditing. Despite digital governance tools like OMMAS, real-time monitoring and e-accounting systems remain underused. District-level evidence from Bhiwani and Hisar further reveals that local fiscal bottlenecks, weak institutional coordination, and delayed state contributions have hindered progress. In Bhiwani, only 67 percent of allocated funds were utilized, with poor drainage and design deficiencies causing early road deterioration. In Hisar, 62.7 percent of targeted works were completed, but overlapping expenditures with other schemes and manual record-keeping reduced transparency. The social implications are severe: school dropout rates rose in remote areas due to poor accessibility, while transport costs for marginal farmers increased by 15–18 percent, limiting their access to agri-markets. The study argues that Haryana’s PMGSY experience reflects systemic governance failures rather than financial inadequacy. The findings emphasize the necessity of decentralizing planning to empower Panchayati Raj Institutions (PRIs) and ensuring participatory decision-making. Policy recommendations include (i) strengthening local governance and Gram Sabha involvement, (ii) institutionalizing third-party audits and performance-linked funding, (iii) integrating gender-sensitive and climate-resilient road designs, (iv) establishing a dedicated post-construction maintenance fund, and (v) aligning PMGSY objectives with Sustainable Development Goals (SDGs) 9, 11, and 13. By situating Haryana’s case within the broader discourse of fiscal federalism and rural governance, the paper underscores that infrastructural development must transcend expenditure efficiency to encompass institutional reform and community accountability. Aligning PMGSY with sustainability indicators and leveraging digital monitoring can bridge the state’s rural infrastructure gap and enhance socio-economic mobility. The study thus contributes to the evolving debate on rural road governance by providing evidence-based insights for policymakers and development economists seeking to make rural connectivity more inclusive, transparent, and future-ready.

Planning of rural road network using sustainable practices to maximize the accessibility to health and education facilities using ant colony optimization

The management of low-volume rural roads in developing countries presents a range of challenges to road designers and managers. Rural roads comprise over 85 percent of the road network in India. The present study aims at development of deterioration models for the optimum maintenance management of the rural roads under a rural road programme namely Pradhan Mantri Gram Sadak Yojana (PMGSY) in India. Visual condition survey along the selected low-volume rural roads considers parameters like condition of shoulders, drainage features, cross-drainage structures, and camber, and pavement distresses, namely, potholes, crack area, and edge break, are collected for a period of three years. The deterioration models have a significant role in the pavement maintenance management system. However, the performance of a pavement depends on several factors. Cluster analysis can be used to group the pavement sections so that the performance of pavements in different clusters can be studied. Nonhierarchical clustering technique of k-means clustering was considered. Separate deterioration models have been developed for each of the clusters. A comparison of the models developed with and without clustered sections reveals that the clustering of pavement sections are preferred for the efficient rural road maintenance management.

One of the key elements for rural development is the connectivity using proper roads, which enhances the passage for economic and social utilities with overall socio-economic development. Socio-economic impact assessment (SEIA) forms one of the significant measures to evaluate the outcome received through infrastructure development in rural areas. SEIA modeling under computational intelligence coupled with fuzzy framework provides significant ground to deal with both qualitative and quantitative data. This study proposes a novel methodology by using Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fuzzy Delphi method (FDM) to evaluate socio-economic impacts. The effectiveness of the methodology is presented through a case study for 27 habitations connected with all-weather rural roads constructed under Pradhan Mantri Gram Sadak Yojana (PMGSY) scheme in Jhunjhunu district of Rajasthan State, India. 33 key-parameters under five different criteria are considered for SEIA. For a comprehensive view of the impacts, the results are depicted using ArcGIS tool.

In this study, we estimate the effect of rural roads on agricultural exports in India. We use a novel data set of state‐level agricultural exports at a highly disaggregated product level (HS6). We combine it with publicly available data on road construction under Pradhan Mantri Gram Sadak Yojana (PMGSY) from 2004 to 2016 to estimate the effect of rural road access on agricultural exports. We use the instrumental variable (IV) approach, whereby we use discontinuity in treatment under the program at the population‐based eligibility cut‐offs for road construction as a plausible exogenous variation in road placement. This rectifies the endogeneity bias due to the placement of road construction. We find strong evidence of the positive effect of increased access to rural roads on agricultural exports. In search of mechanisms, we also find evidence of an increase in the mechanization of farms due to the construction of rural roads. We also find that the results were primarily driven by states having more initial agricultural productivity.

The maintenance management of low volume rural roads in developing countries presents a variety of challenges to road designers and managers. Rural roads comprise over 85% of the road network in India, and their serviceable condition is crucial to the rural economy. The present study aims to develop an index for rating the condition of pavements of low volume rural roads under the national rural road program, namely Pradhan Mantri Gram Sadak Yojana (PMGSY) in a typical district in India, so that the index can be used as a decision support tool for the maintenance management of low volume rural roads. A visual condition survey of the selected roads included evaluation of the conditions for shoulders, drains, cross drainage structures, camber and pavement distresses viz., potholes, crack areas, and edge breaks at every 200 m section. The data were collected for a period of three years. A multivariate technique, factor analysis, was used to calculate the index number for every 200 m pavement section in the study area. Data on 12 indicators of pavement conditions were factor analyzed (principal components and varimax rotation) and four factors were extracted, namely distress factor, side drainage factor, shoulder vegetation factor, and cross drainage factor. A total variance of 77.8% was explained by these factors. The index numbers were standardized as percentages for easy comparison across all sections and named as Visual Condition Index (VCI).

Abstract. Waterlogging on roads severely impacts transportation safety majorly due to inadequate drainage systems, blocked drainage channels, poor road design and construction, especially in areas with limited or no regular monitoring and maintenance, leading to increased accidents and traffic disruptions. Identifying waterlogging from the field photographic image is challenging due to poor illumination, reflective distortions, transparent surfaces, and low resolution. This study aims to identify waterlogging on rural roads using a deep learning-based semantic segmentation approach. The YOLOv11 model was trained and tested on CDAC PARAM Siddhi-AI High Performance Computing (HPC) platform. A dataset of 1000 photographic images from the PMGSY (Pradhan Mantri Gram Sadak Yojana) were sourced and annotated for this purpose. The model effectiveness was evaluated using key evaluation metrics including precision, recall, F1-score, accuracy, and Intersection-over-Union (IoU). The Deep Neural Network (DNN) model achieved a precision of 91.27%, recall of 85.95%, F1-score of 87.58%, accuracy of 96.20%, and IoU of 80.06%. The results show that training DNN model on a GPU-accelerated HPC platform significantly improves both accuracy and processing speed, which is suitable approach for waterlogging detection effectively. The output model can be utilized for deployment in national programmes such as the PMGSY National GIS, offering a rapid, cost-efficient, and scalable solution for waterlogging detection on road.

The Government of India launched its National Rural Roads Program known as Pradhan Mantri Gram Sadak Yojana (PMGSY) to connect the 167 thousand unconnected villages in the country by all-weather roads to improve connectivity there. It is important to study the impact of such intervention on various socio-economic indicators of rural development there. This study assesses the impact of those roads on the different aspects of rural community. The assessment has been completed based on spatial visualisation of the impact created by various facility parameters in rural development using various questionnaires formed and applied on a few selected blocks. Spatial data was collected and integrated using open-source software (QGIS) and statistical analysis has been performed to understand the percentage change in socio-economic indicators related to education, healthcare, agriculture, marketing and employment opportunities which are essential elements of the integrated rural development in India. The analysis appears helpful in estimating the sensitivity of government policies in the context, and thus understanding the requirement of policy changes and implementation in rural India.

In developing countries like India, rural areas lack adequate transportation facilities. The Ministry of Rural Development (MoRD), Government of India (GoI) decided to develop rural roads under Pradhan Mantri Gram Sadak Yojana (PMGSY). The objective of this program is to achieve connectivity to all habitations under different climatic conditions. There are certain disadvantages associated with construction of the rural roads using gravel like dust generation, gravel loss over a period of time due to passage of vehicles and inaccessibility during rains. The best solution to reduce these problems is to seal the gravel roads instead of maintaining it, since many studies showed that the maintenance of unsealed road is more costly than the sealed road over a period of time. Hence, a project has been taken up to give full and all weather connectivity using locally available materials. Up to the surface course, the selected road was constructed using the locally available gravel. Many sealing techniques are available, out of which, open graded premix surfacing (OGPS) was opted in this study. This paper aims at studying the performance evaluation of gravel surface sealed with OGPS. The variables considered in this study are: gravel surface type, primer rate, and binder type. Past researchers found that the roughness, skid resistance and texture depth significantly affect the performance of sealed gravel roads and these three parameters are considered as performance indicators in this study. Eight sets of data were collected at regular intervals starting from November 2013 to March 2015. The surface distresses were also measured. From the analysis it was observed that the roughness on emulsion based OGPS was more when compared to bitumen based OGPS. Emulsion based OGPS resulted in less texture depth when compared to bitumen based OGPS. Skid resistance varied differently with respect to the binder type, type of surface course and priming rate. Even though there is little effect of gravel surface course type on roughness and texture depth, the effect of primer rate on roughness, texture depth, and skid resistance is negligible.

Village and Other districts’ roads play an important role to establish a link between the private and the commercial sector and it is beneficial for the financial growth of a nation. Govt. intended to upgrade and new construct the Village Road (VR) & Other District Road (ODR) road through Pradhan Mantri Gram Sadak Yojana (PMGSY) & Mukhya Mantri Gram Sadak Yojana (MMGSY) since 2000 therefore Structural Analysis is essential for upgrading the existing pavement performance. Conventional practice is based on the lab. outcomes but due to seasonal various required field evolution for getting correct periodic data for up gradation as well as construction of non plan road. Various techniques are available for structural evaluation of pavement. DCP (Dynamic Cone Penetration) test is a well-known practice to evaluate the strength of subsoil on the field. The DCP test is the simplest apparatus designed by Transportation Research laboratory (TRL), UK for on-field strength assessment. Presence research carries out a laboratory and field investigation on the subgrade of MMGSY road of central Gujarat, India, and also focuses on the enhancement of strength of subgrade utilization of nano materials. The relationship of DCP penetration in mm/blow with Field The California Bearing Ratio (CBR) & CBR with resilient modulus of sub grade soil (Mrs) was also established at MMGSY road for structure performance evaluation of the pavement.

Road Safety Analysis (RSA) ensure prevention of loss of human life and damage to property which is a procedure to assess the safety standards of a road and helps in the overall decision making process of road management. Road are to be prioritized based on Safety levels to identify the most vulnerable roads to provide mitigation measures. In this paper a study was carried out to determine the priority of safety requirements of a certain category of rural roads, viz., Pradhan Mantri Gram Sadak Yojana (PMGSY) roads in the Jhunjhunu district of Rajasthan, India. Multi-criteria techniques were used to quantify the safety levels. Further analysis has also been done on the road having the worst safety features to rank various stretches. The parameters vital for safety have been selected and quantified using three multi-criteria decision making analysis tools: Simple Additive Weightage (SAW), Analytical Hierarchy Process (AHP) and Fuzzy AHP methods and results are compared. The methodology presented herein gives an insight to prioritize roads for safety mitigation measures which is expected to be useful to various Decision Makers.

The term Rural Development is the overall development of rural areas to improve the quality of life of rural people. In India, out of total population, 83.3 crores of population living in rural areas (Census of India, 2011) and this population is characterized by mass poverty, low levels of literacy and income, high level of unemployment, and poor nutrition and health status. The rural developmental programmers intends to reduce the poverty and unemployment, to improve the health and educational status and to fulfill the basic needs such as food, shelter and clothing of the rural population. To improve the conditions of rural people, Government of India has launched various schemes such as Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), Rastriya Sama Vikas Yojana (RSVY), Indira Awas Yojana (IAY), Sampoorna Grameen Rozgar Yojana (SGRY), Integrated Tribal Development Project (ITDP), Pradhan Mantri Gram Sadak Yojana (PMGSY), Integrated Child Development Services (ICDS), Development of Women and Children in Rural Areas (DWCRA), etc. All these schemes are aimed to reduce the gap between rural and urban people, which would help reduce economic imbalances and speed up the development process. This article is highlights Impact, Issues and Challenges of MGNREGA on Rural Development

The development of rural infrastructure is an important component in improving the standard of living in developing countries. In India, the government has taken up numerous measures to contribute toward the development of rural infrastructure, thereby initiating the Bharat Nirman Yojana (BNY). In this research study, the Malmquist model of input‐oriented DEA has been conducted to determine the efficiency of six schemes of the BNY for the time period of 2013–2015. The input variables selected were; designated investment and total amount released by the central government against the output variable of number of villages covered or achieved. The analysis of the data observed that only the telecommunication scheme has constant efficiency of 1.000 for all the years. On the other hand, the least efficiency from the Constant Return to Scale model was found for Accelerated Irrigation Benefit Programme (AIBP) (0.014 for 2013; 0.026 for 2014 and 0.007 for 2015). Again, the Variable Return to Scale model indicated technical efficiency for the electrification scheme and telecommunication for all the years. Further, in terms of technical efficiency, all the schemes had decreasing change where Pradhan Mantri Gram Sadak Yojana (PMGSY) showed the highest change with 99.98% and the telecommunication scheme reflected the least change. Furthermore, the productivity index was found to be highest for Telecommunications (1.443) followed by AIBP (1.312), and the lowest productivity index was found for Rajiv Gandhi Gramin Vidyutikaran Yojana (RGGVY) (0.353). Thus, it was interpreted that among all the schemes, telecommunications scheme was the most efficient.

This study explores the awareness and impact of key government welfare schemes in India, with a focus on the Pradhan Mantri Awaas Yojana (PMAY), Pradhan Mantri Gram Sadak Yojana (PMGSY), and Deen Dayal Upadhyaya Grameen Kaushal Yojana (DDU-GKY). The study uses secondary data and Exploratory Data Analysis (EDA) to assess demographic trends, awareness levels, and budget allocation. The findings show that, while most respondents are aware of government plans, their detailed knowledge is limited. Social media is important for distributing information, whilst traditional media sources have less impact. Statistical tests, such as an Independent Sample t-test and a One-Way ANOVA, show substantial differences in fund utilization and beneficiary reach across the schemes. Notably, government support for PMAY and PMGSY varies greatly by state, although DDU-GKY funding is stable