Abstract Current accident statistics show that the highest rate of fatal traffic accidents in Germany occurs on rural roads, particularly as a result of vehicles leaving the road. Advanced driver assistance systems (ADAS) and highly automated driving functions therefore have high potential to improve safety in this domain. A key challenge is lateral vehicle control, especially the selection of an appropriate trajectory when cornering in automated driving mode (SAE Level 3+). The aim of this work is to derive characteristic driving variants from real-world measurement data, which serve as a basis for the design of automated lateral vehicle control and contribute to achieving high customer acceptance at the same time. For this purpose, extensive data from real world field tests was collected, standardized, and segmented at defined nodes (curve entry, apex, curve exit). A subsequent cluster analysis identified typical driving styles. Based on this, various trajectory variants were systematically generated using graph theory methods. These variants differ in terms of vehicle class, curve radius, and preference for corner-cutting. In addition, environmental influences such as the presence of oncoming traffic were considered. The outcome is a catalog of reality-based trajectories that serves as the basis for future driving functions. This enables further investigations in which the influence of the variants on driving comfort and safety will be evaluated, both in the Dynamic Vehicle Road Simulator (DVRS) and in real-world driving tests with test vehicles.

In Japan, unlike in many other countries, urbanization has progressed while original rural road structures have been retained, leading to distinctive urban sprawl areas with intermingling residential lots and farmland. Currently, much of Japan’s urban areas consist of urban sprawl areas, posing considerable challenges for infrastructure development. However, for such urban sprawl areas in Japan, it is difficult to say that methods have been established to identify their spatial distribution based on quantitative evaluation. Therefore, for this study, we used machine learning to investigate a system that extracts sprawling urban areas from aerial photographs divided into meshes. In the system’s design, we prioritized precision to ensure the reliable detection of urban sprawl areas. Consequently, the accuracy of identifying sprawl areas achieved precision of 0.81, recall of 0.63, and an F-score of 0.71. Examination of the classification results of sprawl areas revealed that most misclassifications occurred near class boundaries. By contrast, areas with particularly high levels of urban sprawl showed few misclassifications.

Enhancing pedestrian safety on rural primary school roads in shanghai using machine learning and spatial-temporal simulation modeling

Abstract- This study focuses on designing and testing a collision avoidance management system tailored for rural road conditions in India. Rural roads often face challenges such as narrow single-lane pathways, mixed traffic, and limited infrastructure, making accidents more frequent. To address these issues, a fuzzy logic controller was implemented, using key parameters such as velocity, distance, and angle to predict and prevent potential collisions. The system was tested under both single-lane and double-lane scenarios, simulating real-world rural traffic conditions. Results demonstrated that the fuzzy controller effectively identified collision risks and provided adaptive responses, such as speed regulation and steering adjustments, thereby enhancing safety. The study highlights the potential of intelligent control systems in reducing accidents and improving mobility in rural areas where conventional safety technologies are often absent. Keywords: Smart Controllers, Collision Avoidance, Autonomous Systems, Safety Management etc.

A hybrid statistical-machine learning methodology for addressing endogeneity and temporal instability in speeding-crash frequency relationships.

Identifying rural roads in remote sensing imagery: From benchmark dataset to coarse-to-fine extraction network—A case study in China

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.

Rice, Rules, and Rural Roads: Evolutionary Dynamics of China’s Milled Rice Industry

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.

Background: Rural road infrastructure is a critical determinant of socio-economic development, influencing access to essential services, mobility, and quality of life. In South Africa, inadequate road infrastructure in rural areas perpetuates poverty and social exclusion, raising concerns about human dignity and sustainability. Within an ecohumanist framework, these infrastructural challenges are not merely technical but relate to broader issues of social justice and environmental stewardship. Aim: The aim of this article is to examine the challenges associated with road infrastructure in the Galili community and to explore the measures adopted by community members to address these challenges. Setting: The study was conducted in the Galili community under the Nyandeni Local Municipality, Eastern Cape Province, South Africa, a predominantly rural area with severe road infrastructure limitations. Methods: A qualitative research design was employed, using semi-structured interviews with residents aged 18 to 65 years. Convenience sampling was applied, and data collection continued until thematic saturation was achieved. Data were analysed thematically, following the six-phase approach. Results: Findings revealed significant infrastructural challenges, including road impassability during rainy seasons, dust-related health risks during dry periods, restricted emergency service access, and limited public transport options. Community members adopted coping measures such as pooling resources for ad hoc repairs, reporting issues to local authorities, and engaging in informal advocacy. However, these measures were temporary and unsustainable due to a lack of technical expertise and institutional support. Conclusion: The study demonstrates that poor rural road infrastructure compromises mobility, access to essential services, and quality of life, contravening the ecohumanist principles of equity and human dignity. While community initiatives show resilience, long-term solutions require systemic interventions integrating participatory governance and climate-resilient designs. Contribution: This article contributes to the ecohumanist discourse by highlighting the lived experiences of rural communities in infrastructural governance, offering insights for policy frameworks that combine sustainability, inclusivity, and community empowerment.

Development of a Framework for Assessing the Sustainable Index for Maintaining Low-Volume Rural Roads in India

Assessing the safety effectiveness of advanced driver assistance systems.

Do land consolidations contribute to better landscape accessibility through rural roads? - Case study South Bohemia Region (Czech Republic)

Lighting the way: A global analysis of road lighting outside of urban areas.

Crash risk patterns among older bicyclists: Insights from hybrid XGBoost-Cluster Correspondence Analysis.

Rural road extraction from remote sensing images based on multi-view contextual information and multi-stage features

Expansive soils such as black cotton soil pose significant challenges in civil engineering works due to their high swelling and shrinkage potential, low bearing capacity, and poor stability under varying moisture conditions. To overcome these limitations and promote sustainable development, the present study focuses on improving the engineering characteristics of black cotton soil through the incorporation of recycled polymeric waste (plastic strips). The experimental program involved conducting a series of tests including Sieve Analysis, Atterberg Limits, Standard Proctor Compaction Test, Unconfined Compressive Strength (UCS) Test, and California Bearing Ratio (CBR) Test on both untreated soil and soil reinforced with different percentages (1%, 2%, and 3%) of plastic waste by dry weight of soil. The results revealed that the inclusion of plastic waste significantly enhances the geotechnical properties of the soil. The maximum dry density (MDD) increased while the optimum moisture content (OMC) slightly decreased, indicating better compaction behavior. The UCS and CBR values showed a remarkable improvement at an optimum dosage of 2% plastic content, beyond which the enhancement became marginal. This improvement is primarily attributed to the interlocking and tensile resistance of the randomly distributed polymeric fibers within the soil matrix, which provide confinement and reduce deformation under load. The study demonstrates that using recycled polymeric waste as a soil stabilizer not only enhances the strength and stability of problematic soils but also offers an ecofriendly and cost-effective solution for solid waste management. Therefore, this method can be effectively adopted in rural and low-volume road construction, contributing to sustainable geotechnical engineering practices.

Efficient project delivery is a critical requirement for rural road rehabilitation programs, yet empirical evaluations of implementation efficiency remain limited. This study analyzes the time and estimated cost efficiency of the Papring Hotmix Road Rehabilitation Project in Kalipuro District, Banyuwangi, using a descriptive quantitative case study approach based on weekly and monthly project documentation. Project performance was measured by comparing baseline planning with field realization, supported by calculations of time efficiency, deviation analysis, and cost implication estimation derived from work-weight distribution. The project was completed in 55 days, significantly faster than the 120-day planned duration, resulting in a time efficiency of 54.17% and a maximum positive deviation of +76.32% at completion. Major work components: AC-WC (65.616%), AC-BC (18.249%), and aggregate base layer (12.436%), were executed intensively between Weeks 4 and 7, enabling a shortened execution period that suggests potential savings in indirect project costs such as overhead, labor, and field operations. Acceleration was driven by effective technical scheduling, early resource mobilization, optimized labor deployment, and strong contractor–consultant coordination. The findings highlight the importance of structured progress evaluation for improving scheduling and cost control in rural road rehabilitation projects. Future work should incorporate financial realization data and risk-productivity modelling to strengthen efficiency assessment in accelerated infrastructure delivery.

This study developed a predictive model for evaluating the impact of road maintenance delays on the life-cycle cost of gravel roads in Kinondoni District, under the jurisdiction of the Tanzania Rural and Urban Roads Agency (TARURA). Gravel roads play a vital role in supporting socio-economic activities in both rural and urban areas of Tanzania. However, delayed maintenance has continued to pose significant challenges, leading to road deterioration, higher long-term costs, and reduced accessibility. A mixed-methods approach was employed, combining qualitative data obtained through interviews with TARURA officials and contractors, and quantitative data collected from 44 road segments representing diverse geographic and traffic conditions. The study analysed maintenance records spanning five years (2019-2024) and incorporated economic data from national road fund allocations. The Relative Importance Index (RII) was used to rank delay factors, while a Multiple Regression model was manually developed in Excel to predict life-cycle costs based on delay severity. Statistical validation included correlation analysis, residual diagnostics, and cross-validation using independent datasets. Key findings revealed that out of twelve assessed delay factors, seven were significant predictors of cost increases. These include inadequate funding release (RII = 0.941), multiple damage points (RII = 0.891), poor traffic management (RII = 0.850), equipment availability (RII = 0.809), material transport issues (RII = 0.782), emergency repair prioritisation (RII = 0.768), and rainfall intensity (RII = 0.759). The life-cycle cost model yielded an R² value of 0.8735, indicating strong predictive capability with 87.35% of cost variance explained by delay factors. Model validation using independent road segments showed prediction accuracy within 15% of actual costs in 82% of cases. The study concludes that maintenance delays significantly affect the long-term sustainability and cost-efficiency of gravel roads, with each year of delay increasing life-cycle costs by approximately 0.62 million TZS. It recommends strengthening planning mechanisms, improving budget disbursement efficiency, enhancing coordination of maintenance activities, and adopting predictive modelling in decision-making. The models developed provide practical tools for TARURA and other road agencies to prioritise interventions, forecast costs, and optimise road asset management strategies.

Research on maintenance strategies for rural road bridges based on cloud models: a case study