Road Connectivity Research Articles

How Creative Economy Development Plays a Role as a Driving Force in Community-Based Tourism Village Development? An Empirical Study in Central Java Province

This study aims to develop a Creative Economy Development Strategy as a Driving Force for Community-Based Tourism Village Development. This study uses quantitative and qualitative approaches. This study uses primary and secondary data. Data collection methods in this study include observation, interviews, documentation, and questionnaires. The questionnaire in this study was addressed to key persons consisting of several elements including academics, government, society, NGOs, Communities, and also the private sector. The results of this study indicate that Infrastructure and Accessibility and Policy and Regulatory Support have the highest weights, which means that supporting factors such as infrastructure and government policies are very important in the development of creative economy-based tourism villages. Policy and Regulatory Support and Local Human Resource Empowerment are the next priority criteria. Provision of Grants and Subsidies for Creative MSMEs has the highest final score, which means that these elements are alternative strategies that are the main priority for driving the creative economy. Improving Road Connectivity to Villages, Training in Crafts, Fine Arts, and Local Culinary, and Adding Tourism Support Facilities are important strategies to increase the attractiveness and accessibility of tourism villages. The development of galleries or showrooms for MSME products and digital campaigns through social media are also considered strategic in supporting the promotion of local creative products.

Rural Road Extraction in Xiong'an New Area of China Based on the RC-MSFNet Network Model.

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model's ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong'an New Area, China, using high-resolution imagery from China's Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong'an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong'an New Area.

Clustering Urban Roads Using Local Binary Patterns to Enhance the Accuracy of Traffic Flow Prediction

Many studies employ a dynamic statistical approach to model the relationship between roads for predicting traffic flow. Statistical relationships among roads pertain to the associations between road segments in nearby areas. Roads with similar traffic patterns usually define the relationship. Previous studies have shown that adjacent roads demonstrated a similar traffic pattern on the same day and time interval. Studying similar patterns between roads in surrounding areas provides information about the traffic state among roads in a cluster. Furthermore, the results of this finding may be considered to increase the performance of prediction of traffic conditions. In general, road segment roads are correlated with other road segments. They are connected upstream, downstream, or both; they are connected upstream, downstream, or both. To address this, we propose a Local Binary Pattern (LBP) to explore road connections by clustering congestion features between the target roads and their neighboring roads. The outcomes show that the roads with high connectivity obtained from the clustering process were utilized to predict traffic conditions using the SVM approach. The David-Bouldin Index of LBP with k-means shows the lowest compared with PCA k-means and k-means only. By evaluating the clustering result using the Davies-Bouldin Index and assessing the prediction results with SVM, we discovered that incorporating LBP with K-Means improved outcomes in identifying highly connected roads using K-Means.

Impact Of Road Connectivity On Daily Livelihood Sustainability Of Commuters In Sandeshkhali-I CD Block, North 24 Parganas, West Bengal, India

Background: The study area has different infrastructural deficiencies with compare to other regions. The natural constraints for communication, Poverty, Infrastructural deficiency, Rurality, and Transportation barriers are the main problems in this region. This study has tried to determine the different road connectivity and their impacts on daily sustainable livelihoods among the commuters of Sandeshkhali-I CD block in North 24 Parganas district of West Bengal, India. Materials and Methods: The study has used the Primary and Secondary data to fulfill the objectives. To execute the objectives, road networks are differentiated to measure the road connectivity indices (Alpha, Beta and Gamma Index) from different Gram Panchayats (GPs) to find out the road network condition in this rural deltaic region of the Indian Sundarban. Using the Random sampling to collect the primary data through questionnaire from commuters at different busiest places in the study area. Based on different selected livelihood components, the daily livelihood sustainability index has been evaluated. To find out the relationship the road connectivity Indices (R.C.I.) and daily livelihood sustainability index (D.L.S.I.) are correlated with the help of Karl Person ‘r’ value. Results: The highest values of Alpha, Beta and Gamma Indices are 0.34, 1.89 and 0.65 with respect to Metalled and Unmetalled roads. The daily livelihood sustainability index value has been found 54.33 and 17.54 as the highest and lowest respectively. The positive and negative values are present in the correlation between R.C.I. and D.L.S.I. Conclusion: From the outcome, most of the GPs are under the low and very low livelihood sustainability category whereas, the rest of the GPs have medium to high livelihood sustainability status as per selected class. The study concluded that the development needs to be accounted for because of intra-regional imbalance among the road connectivity as well as daily livelihood sustainability that has already existed in this area

How do urban block built environments affect older adults' walking activities and health effects: a case study in Nanjing, China.

Research on the relationship between microscale built environments and physical activity among older adults in densely populated old urban areas with high aging rates is scarce. Particularly, the relationship between urban block land-use pattern and older adults' walking activities have not yet been completely understood. This study examined the daily walking habits and socioeconomic attributes of older adults in 17 blocks with different built environment characteristics in Nanjing City, China. A multi-layer linear model was used to quantitatively analyse the mechanism underlying the effects of various factors on the health of older adults. The result shows significant positive correlation between neighborhood walkability and the enhancement of walking frequency and activity. For every 10% increase in pedestrian road connectivity and land use, the walking activity volume increased by 22.4 and 12%, and the BMI increased by 9.1 and 7.3% toward the standard range, respectively. For every 10% increase in distance between residence and plaza and park, the walking activity volume decreased by 5.4 and 3.2%, and BMI decreased by 9.9 and 6.3%, respectively. For every 10.4% increase in land mixed-use rate and per capita green area, BMI increased by 19.4%. Furthermore, higher household income and number of family members have significant correlation with walking activities and health effects. Moreover, the block-scale built environment and walking activities jointly affected health, with a cross-functional relationship between multiple factors.

Estimating casualties from urban fires: A focus on building and urban environment information

This study developed two prediction models for urban fire occurrence and related casualties via a fire accident dataset from Seoul, South Korea, from 2017 to 2021. Our models exhibit improved predictive performance by incorporating built environment features, such as building characteristics and the urban context, alongside weather and demographic data. This approach showed improved predictive performance suitable for public health implementation. Compared with the weather- and demographic-only models, our models had an 18.1 % greater fire occurrence prediction accuracy and a 10.4 % greater casualty prediction accuracy. Major variables affecting fire occurrence include building characteristics, e.g., the floor area ratio (FAR), building age, and commercial building number. Important features affecting casualty occurrence include demographic aspects, e.g., income level and weather, and network-based features, e.g., road connectivity and fire station proximity. These findings suggest that fire prevention strategies and fire casualty prevention strategies may need to differ. Furthermore, we identify high-risk zones by conducting spatial analysis and fire risk and casualty prediction on all buildings by applying our models to Seoul's Gangnam District. These contributions can promote safe and healthy urban environments by improving fire risk prediction accuracy and providing important insights into urban planning for appropriate urban fire accident response and prevention.

Financing cross-border road connectivity: Potential impact on Sabah’s output—An input-output table analysis

Fiscal spending for road construction to link Kalabakan, Sabah, Malaysia with North Kalimantan, Indonesia is an idea that have been proposed for over 20 years. The announcement for the relocation of Indonesia’s capital city from Jakarta to East Kalimantan give a strong justification for the construction of the Serudong-Simanggaris road. The fact that population size is big in Kalimantan and strong purchasing power is estimated in North and East Kaliamantan provide a strong argument for the need to have a road link. Having said that, the effect of road construction on output growth is not clear. The purpose of this study is to estimate the impact of road construction and the business activities across two sectors being assumed on output Sabah’s output growth. Based on the input-output analysis conducted using the output multiplier, the one-off road construction would lead to 1.8% growth in Sabah’s overall output.

Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM

Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future.

"REVITALIZING HERITAGE TOURISM IN GWALIOR POST-COVID-19: CHALLENGES, STRATEGIES, AND SUSTAINABLE DEVELOPMENT"

The Gwalior region, which is abundant in historical and cultural assets, offers a substantial number of chances for the growth of tourist business. Gwalior, which is renowned for its magnificent forts, palaces, and temples, has the potential to become one of the most popular tourist destinations in India. With regard to the growth of tourism in the Gwalior region, this article investigates both the difficulties and the opportunities that are associated with it. The region is encountering a number of challenges in order to realize its tourist potential, despite the fact that it has a rich tradition. There are a number of key obstacles, including inadequate infrastructure, which discourages potential tourists. Some examples of this include poor road connectivity and limited hotel options. In addition, the region's attractions are not being utilized to their full potential since there are not enough efficient marketing tactics and promotional activities carried out. Inadequate investments in tourist development and a shortage of workers with the necessary skills both contribute to the worsening of these problems. Nevertheless, there is reason to be optimistic about the future of tourism in Gwalior. The region has the potential to capitalize on its distinctive offerings if it engages in strategic planning and investment. Through the development of sustainable tourism practices, the enhancement of infrastructure, the improvement of accessibility, and the enhancement of accessibility, the region's appeal to domestic and foreign visitors may be considerably increased. Increasing exposure and interaction with prospective visitors may be accomplished via the implementation of efficient marketing campaigns and the utilization of digital technology platforms. The development of tourism that is both inclusive and sustainable may also be promoted through partnerships with local communities and stakeholders, which will ensure that the benefits are transferred to a large number of people. At the end of this paper, recommendations are provided for policymakers and stakeholders to overcome the present difficulties and capitalize on the prospects for tourism expansion in Gwalior. This would ultimately contribute to the socio-economic development of the region.

Spatial heterogeneity and nonlinearity study of bike-sharing to subway connections from the perspective of built environment

Bike-sharing has become an effective solution to address the "last mile" challenge in public transit. Existing studies insufficiently consider architectural environmental factors from a design perspective in impacting bike-sharing-to-subway connection(BSC) behavior, and often relying on subjective assessments to explain the spatial heterogeneity of the built environment on BSC travel. To investigate the impact mechanisms of the built environment on BSC behaviors, this study used weekly data on bike-sharing travel trajectories from April 7 to April 14, 2021, in Shenzhen, China, as the research sample. By integrating large-scale urban point of interest(POI), street-view images and road-network data from Shenzhen, this study examines the mechanisms by which the built environment influences BSC travel. This study initially applied linear regression and multiscale geographically weighted regression (MGWR) to analyze the spatial heterogeneity of built environment impacts on BSC travel. Then, XGBoost's nonlinear relationships were used to explain and validate the MGWR results. This study reveals that functional diversity is the most significant factor influencing BSC travel. At subway stations where it is difficult to enhance functional diversity, improvements in road connectivity, vegetation, and enclosure can enhance BSC commuting. These findings are crucial for decreasing the reliance on motor vehicles and fostering sustainable urban growth.

Can good neighbourhood perception magnify the positive effect of favourable built environment on recreational walking in China?

BackgroundIt remains unknown whether good neighbourhood perception can enhance the benefits of favourable built environment to physical activity. Moreover, the moderation pattern is less understood in developing countries.ObjectivesThis work aims to examine the moderation effects of perceived neighbourhood safety and aesthetics on the relationship between built environment and time for recreational walking.MethodsWe performed the examination using a sample of 760 residents in Fuzhou City, China. The Negative Binomial Regression Model was developed to examine the moderation roles of neighbourhood safety and aesthetics on the impact of built environment, adjusting for the effects of location, socioeconomic, personal preferences and social environment factors. Moreover, two sensitivity analyses were performed to test whether the moderators found are robust to the control of residential self-selection, and differential measures of conceptually-comparable aspects of built environment.ResultsWe found stronger associations of time for recreational walking with road density and proportion of parks and squares POIs for residents with high perception of neighbourhood safety, compared to those with low perception of neighbourhood safety. There was a greater effect of the proportion of parks and squares POIs, when perceived aesthetics was high than when perceived aesthetics was low. The findings of neighbourhood safety and aesthetics as moderator, were robust in the two sensitivity analyses. No significant moderation effect was found for land use diversity.ConclusionsHigh perceived neighbourhood safety can magnify the positive effects of road connectivity and accessibility to parks and squares. Neighbourhood aesthetics positively moderates the association of time for recreational walking with accessibility to parks and squares. The findings emphasize the need to consider safety- and aesthetics-specific differences in estimates of built environment effects. Improvements in neighbourhood safety and aesthetics are key to effective interventions in built environment to better promote physical activity.

The Determinants and Synergies of Livelihood Choices Across Climate-vulnerable Regions: An Empirical Analysis from Rural Bangladesh

This study explores the role of diverse climatic vulnerabilities and socio- economic variables in explaining Bangladeshi rural households’ livelihood choices and the synergies that exist across the choices. We develop a multivariate probit model for analysing a dataset of 5,604 households that is representative of rural Bangladesh. The findings reveal a landscape where agriculture-based strategies predominate, with households strategically combining various approaches. Households complement agriculture-based strategies, while substituting beyond agriculture-based strategies. Climate stressors such as flood, salinity, river erosion, drought, storms and cyclones induce notable shifts in livelihood choices. Among these, the most prominent is the substantial influence of storm and cyclone vulnerability. Migration is more prevalent in areas susceptible to salinity, storms and cyclones, while casual labour prevails in drought-prone and flood-vulnerable regions. Households residing in the economically marginalized and environmentally vulnerable feed-the-future (FtF) zone make distinct decisions regarding income strategies compared to their counterparts in other rural areas across the country. Households in this zone exhibit a higher likelihood of opting for specific agriculture-based income sources and casual labour, while self-employment, migration and salaried jobs are less favoured. Furthermore, we elucidate the pivotal role of infrastructure, information access and institutional factors in shaping income sources. Households in different divisions make distinct livelihood choices, especially in agriculture. Ultimately, the findings advocate for precise interventions tailored to climate vulnerabilities. This includes enhancements in road connectivity, the utilization of mobile banking and the provision of extension services, with a specific focus on geographically marginalized and climate-vulnerable regions.

Expressways and policy choices of local governments

We examine how intercity road connection affects the policy choices of local governments, using the construction of China’s national expressway as a natural experiment. We show that peripheral counties reduced the level of spending and changed the spending composition since the expressway connection. They became less involved in spending competition, increased the effective tax rate on private firms, and slowed down the privatization of state-owned firms. These results are consistent with the view that mobile capital tends to relocate from peripheral areas to core regions with a lower trade cost. We further show that such changes in government policies contribute significantly to the output decline in these regions after the expressway connection.

Examining the nonlinear relationship between neighborhood environment and residents' health

Although numerous studies have revealed linear associations between the urban environment and health, the nonlinear effects of subjective and objective neighborhood environment on health have been under-researched. Using Beijing as a case study, this study employs the random forest method to investigate the nonlinear relationship between subjective/objective neighborhood environment and physical/mental health, respectively. The findings indicate that the subjective environment is the most important for both physical and mental health, and subjective air and noise pollution are the key factors. Neighborhood environmental variables have nonlinear correlations with health. Improved perception of facility accessibility is positively associated with physical health, whereas extremely high levels of accessibility may inversely decrease the probability of reporting good health. Road connectivity and bus stop density exhibit inverted U-shaped patterns with physical health and mental health, separately. Furthermore, the study highlights different dimensions of green space affect residents' health in diverse ways.

Automatic extraction and 3D modeling of real road scenes using UAV imagery and deep learning semantic segmentation

ABSTRACT The extraction of roads from UAV images is challenged by lighting, noise, occlusions, and similar non-road objects, making high-quality road extraction difficult. To addressing these issues, this study proposes an enhanced U-Net network to automate the extraction and 3D modeling of real road scenes using UAV imagery. Initially, a cascaded atrous spatial pyramid module was integrated into the encoder to capitalize on global context information, thereby refining the fuzzy segmentation outcomes. Subsequently, a module for augmenting road feature extraction was added within the channel, and a spatial attention mechanism was introduced in the decoder to enhance edge clarity. Experimental results demonstrated that this model captures more road information compared to mainstream networks and effectively incorporates topological structure perception for road extraction in complex scenarios, thus improving road connectivity. The model achieved an F1 score and mean Intersection over Union (mIoU) of 85.6% and 81.2%, respectively, on UAV images of road scenes – marking improvements of 3.9% and 3.4% over the traditional U-Net model, thereby exhibiting superior automatic road extraction capabilities. Ultimately, the model facilitated refined modeling and visual analysis of road scenes, achieving high overall accuracy and detailed local restoration of the actual scene.

Open-source data alternatives for regenerating urban road fine-grained features

The extraction of urban road features provides indispensable support to numerous high-accurate applications such as autonomous driving and urban high-definition mapping. However, approaches mainly focus on road connectivity, while often overlooking finer details of urban road constituent structures. Data that captures road details, such as LiDAR, may not be always readily available. This article proposes an operational framework for mapping fine-grained urban road features by integrating open-source data (OSD). The geometric measurement method is successively presented using projective geometry and prior knowledge for urban road sections. And a feature generation strategy is introduced to achieve and express the fine-grained road features. Compared with the corresponding large-scale topographic map (LTM) and available optical remote sensing image (AORSI), the proposed method regenerated fine-grained features of urban roads with m-level. It provides a cost-effective innovative and alternative method to acquiring fine-grained road datasets in other data-scarce regions.

Determinants of Marketing of Handloom Products Across Heterogeneous Weaving Business Units: A Study of Rural Assam

Success in marketing plays an important role in the growth and extension of rural non-farm informal sectors. The present study made an attempt to understand the marketing success among diverse groups of handloom weaving business units and factors determining the marketing of handloom products through the lens of weaving business units in rural Assam. Besides descriptive and inferential statistics, multiple linear regression method was applied for the analysis of data. The results of the study show that the success of marketing in terms of sales volume and revenue was significantly higher among the master weavers. Factors like years of schooling of handloom weaving entrepreneurs, business experience, working hours, storage capacity, advance payment requirement, number of handloom competitors in village, transportation cost, communication expense, and proximity to market significantly influenced the marketing across heterogeneous weaving business units in the study area. Steps towards the improvement of rural market infrastructure and road connectivity by taking a slew of suitable policies by Central and state governments may allow the rural handloom weavers to get better access to the market, which in turn, may assist in the promotion of sales.

An analysis of the perceived effectiveness and upscaling potential of climate-smart agriculture interventions in the Sistan Plain, Iran

The deficient extension of climate-smart agriculture (CSA) interventions in developing regions, where farming livelihood is threatened by natural disasters like drought, is still a concerning dilemma. Development paradigms are not consistent with CSA competencies in providing food security and climatic resilience and often neglect livelihood indices regarding upscaling patterns in CSA development. This research investigated the effectiveness and upscaling potential of CSA interventions in the Sistan Plain in Sistan and Baluchistan province, Iran. A sample of 361 farmers was taken by stratified randomization with proportional allocation. The CSA effectiveness was measured by productivity, income, resilience, and impact mitigation, and the CSA upscaling potential was measured by technical feasibility, technology cost, gender inclusivity, and synergy with government plans. The factors determining CSA effectiveness and upscaling potential were specified by regression models considering farmer households’ livelihood indices. The results revealed that the variables of participation in education, the cultivation area, road connections, hygienic facilities, educational level, house type, farming water resources, cooking facilities, experience in farming, social recognition, and access to shared facilities determined the effectiveness of CSA practices. Also, CSA upscaling potential was found to be influenced by participation in education, the cultivation area, social recognition, farming water resources, house type, hygienic facilities, educational level, experience in farming, road connections, cooking facilities, and number of children.

Mapping coexisting hotspots of multidimensional food market (in)accessibility and climate vulnerability

With the increasing likelihood of agricultural production failures under a warmer global climate, the importance of markets in providing access to nutrient-dense foods (NDFs) through trade is predicted to grow. However, regions with relatively poor access to markets and supporting infrastructures (e.g. roads and storage facilities) are potentially ill-equipped to deal with both short-term hydrometeorological hazards such as droughts and floods, and longer-term shifts in agricultural productivity. Despite the increasing focus upon markets within academic and policymaking circles, a regional-scale assessment of these potentially coexisting hotspots of vulnerability has not been conducted. We conduct a two-stage geospatial analysis integrating three publicly available datasets across the Indian states of Bihar, Chhattisgarh, Jharkhand, and Odisha. Combining the 2011 national census with the new PMGSY-GeoSadak database, we conduct nearest neighbour analysis to measure multidimensional market inaccessibility by: (i) distance from a settlement to its nearest village, town or city with a market, (ii) distance from a settlement to its nearest major road, and (iii) distance from a settlement to its subdistrict headquarters. We then correlate these measures with India’s only district-wise assessment of climate vulnerability to identify hotspots of market inaccessibility and climate hazards. We find that the three market access measures are spatially autocorrelated and positively interrelated at the settlement (n = 129 555) and district (n = 107) levels, meaning that settlements located further from their nearest market tend to experience poorer road connectivity and access to the subdistrict economic hub. Approximately 18.5-million people live in districts with relatively high climate vulnerability and relatively high and multidimensional market inaccessibility. Hotspots of coexisting vulnerabilities are also disproportionately populated by ‘Schedule Castes and Schedule Tribes’ (SC/ST) communities. The identification of coexisting hotspots has important implications for the development of equitable and resilient markets that bolster NDF access for climate vulnerable and nutritionally insecure populations.

Response of road erosion to hydrological connectivity under a heavy rainstorm in an agricultural watershed on the Loess Plateau

Road network extension is closely associated with soil erosion in watersheds during frequent extreme rainfall events. Identifying potential risk areas that actively deliver surface runoff to road structures and channel networks is critical for comprehending hydrological processes and controlling road erosion. In this study, the road erosion (landslides, collapses, scour pits, and gullies on monitored roads) on the Loess Plateau during a heavy rainstorm was analyzed using field survey and unmanned aerial vehicle (UAV) data. We defined hydrological connections as the intersections of roads and channel networks, and the activated hydrological connections represent hydrological connectivity. The results of this study highlight the exacerbating effect of hydrological connectivity on road erosion during a rainstorm in an agricultural watershed in the Loess Plateau region. The results showed that the road erosion intensity ranged from 2.16 × 102 to 1.02 × 104 t/ha. Strong hydrological connectivity resulted in average soil erosion amounts of landslides, collapses, scour pits, and gullies in the downslope area that were 2.16, 1.78, 5.46, and 6.98 times greater than those without hydrological connectivity, respectively. The presence of hydrological connectivity generated a 1.88 times higher road surface erosion intensity than that without hydrological connectivity. The Upslope-Road connections created the most significant enhancement effect on road erosion, and up to 65.37 % landslide erosion and 41.97 % collapse erosion amounts were influenced by these connections.The enhancement effect of Upslope–Road connections on road erosion varied with different road types, this impact of Upslope–Road connections on road erosion were only observed on the landslide erosion of petroleum transport roads and the collapse erosion of agricultural roads. Conversely, Road–Downslope connections did not significantly impact road erosion (p > 0.05). To effectively control the negative effect of road erosion, hydrological connections should be identified and regulated to impede hydrological connectivity activation during rainstorms by modifying tillage activities, improving road maintenance, and strengthening drainage systems.