Road Network Research Articles

The Extent of Agricultural Land Cover Change: The Case of Bautista, Pangasinan

Urban Expansion in Bautista periodically alters its land cover, significantly diminishing agricultural land in favor of built-up areas. Therefore, a comprehensive investigation of past and future land cover changes is essential for sustainable development. The main objective of this research is to determine the extent of agricultural land cover change and its spatial pattern from 2018 to 2022 and in the year 2027 in Bautista, Pangasinan. This study employed a descriptive research approach integrating time series data, spatial analysis, and simulation tools to evaluate the dynamic changes in agricultural land cover. The extent of agricultural land cover change decreased from 3,480.95 hectares in 2018 to 3,417.04 hectares in 2022, primarily near road networks and within the identified urban barangays of the municipality. The change exhibited a clustered distribution, as per the summary report from the Average Nearest Neighbor using the Geographic Information System. In contrast, built-up land cover change expanded by 60.68 hectares from 2018 to 2022, following an increasing trend. Furthermore, the projected agricultural land cover change is expected to decrease continuously from 3,417.039 hectares in 2022 to 3,406.875 hectares in 2027. The results indicate a conversion of agricultural land to built-up areas between 2018 and 2022 and a further decrease in 2027, reflecting a shift from agriculture to man-made structures. This research underscores the importance of monitoring land conversions and provides a basis for sustainable solutions to address the growing demand for urbanization without compromising the future extent of agricultural land cover in Bautista, Pangasinan.

Vertical price transmission in agricultural markets in Ghana

The purpose of this study was to investigate the extent of price transmission along the agri-food supply chain in selected regions in Ghana. The extent of transmission was used to assess the efficiency and effectiveness of the agricultural marketing systems in the area. Efficient marketing systems are expected to reduce the issue of regional price fluctuations, encourage fair income distribution throughout the supply chain, and improve food security. The preliminary time series stationarity/unit root and seasonality tests were performed using the Augmented Dickey-Fuller test (ADF)/Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) and Canova-Hansen seasonal stability tests respectively. Following the detection of statistically significant threshold effects by the Supremum Likelihood Ratio (Sup-LR) tests, the extent of price transmission was investigated using two-region threshold vector error correction models. The initial tests found that the monthly wholesale, retail, and producer price series for maize, cowpea, and yam used in the study were first difference stationary rather than seasonally integrated. The findings revealed that price signals in the agri-food markets were not fully transmitted across the supply chain. Thus, the extent of price transmission was generally low, creating unexploited arbitrage opportunities along the chain. The study identified high transfer costs (proxied by the threshold parameter) and actor-specific market power (as revealed by the Granger causality test) as the primary obstacles to effective market functioning in the area. Minimizing transfer costs through infrastructural development such as improvements in road networks linking farming communities and markets, access to price information, and regular stakeholder engagements could help improve the functioning of the agri-food markets.

Factors influencing risk of predation by subsidized predators on juvenile Mojave desert tortoises (Gopherus agassizii)

A persistent lack of juvenile recruitment in populations of the imperiled Mojave desert tortoise (Gopherus agassizii) has been attributed to artificially inflated populations of coyotes (Canis latrans) and common ravens (Corvus corax) in some areas. Subsidized by resources in the form of road-killed prey, movement corridors, or nesting perches provided by ever-expanding road and powerline networks, these “subsidized predators” threaten tortoises and other species thanks to subsidies that help support their populations in areas where they might otherwise have been less abundant. Here, we quantified the risk of predation from these subsidized predators on juvenile Mojave desert tortoises by quantifying attack rates on 3D-printed replicas. We evaluated predation risk as a function of replica size, distance from powerlines, and season—variables that can inform release strategies of head-started and translocated tortoises. We found significantly greater risk of predation from ravens in the spring than in the fall. Replica size did not significantly influence predation risk from ravens or coyotes. Replica distance from a powerline or road also did not significantly influence predation risk, supporting previous studies that have found the risk of predation can persist for up to 1.6 km from raven nesting structures. Our results support recommendations for releasing juvenile head-started and translocated tortoises in the fall and away from areas close to raven nesting structures. Our study provides insight into the interactions of prey and subsidized predators and helps guide management on potential mitigation strategies for minimizing the impacts of subsidized predators on species like the imperiled Mojave desert tortoise.

Expanding the Accessible Forest Areas by Improving Forest Road Standards and Utilizing Mobile Fire-fighting Teams

In Turkey, most of the roads (66%) in forests consist of Type-B secondary forest roads with limited technical standards. The lack of technical standards used on these roads and the lack of road structures and surface material limit the fire truck’s speed and increase the travel time of the firefighting team. Improving forest road standards will contribute to driving safety and increasing the design speed, thereby expanding the forest areas that can be reached within the critical response period, reducing possible wood raw material losses. There is no study in the national and international literature investigating the contribution of forest road standards improvement to firefighting activities and the effects on possible wood raw material losses due to forest fires. In this study, it was aimed to investigate the effects of improving forest road standards on expanding accessible forest areas within the critical response time, especially in forests with high fire risk, and thus reducing potential wood raw material losses. Antalya Forestry Regional Directorate, where forest fires are seen most frequently and the amount of burned area is the highest in Turkey, was selected as the study area as one of the forest regional directorates where forest fires are seen most frequently and the amount of burned area is the highest in Turkey. In the solution process, primarily the forest areas that can be reached by the firefighting team (including mobile teams used in emergencies) within the critical response time were determined by considering the existing road network in the study area. Then, the possible increase in the accessible forest areas was investigated when the road standards are improved. Within the scope of the study, the impact of mobile teams used in emergency situations on forest areas reached during the critical response period was also evaluated. According to the results, in the scenario where current road standards and stationary teams were evaluated, it was determined that only 59.54% of the forest areas could be reached by initial response teams during the critical response time, and if road standards were improved, this rate increased to 71.69%. On the other hand, in the scenario where current road standards and stationary and mobile teams were evaluated together, it was determined that 70.40% of the forest areas could be reached by initial response teams during the critical response time, and if road standards were improved, this rate increased to 78.17%. The results obtained have shown that improvements in road standards and the presence of mobile teams have a very effective role in combating forest fires.

A Comprehensive Analysis and Research on the Factors Affecting the Growth of Rural Trade in Cultural Communities

This article analyzes the elements that impact the stages of rural retailing development in the Hwange villages, namely in the areas of Jambezi, Mununa, Milonga, Ndlovu, Chikhanda, Mashake, and Monde. The study used a quantitative technique with a sample size of 400 people, recruited by stratified random sampling. The data analysis was performed using SPSS version 26.0. The survey's main findings reveal that rural residents have a preference for firms that value outstanding customer service and offer a broader range of recreational choices. Furthermore, the condition of road networks was recognized as a crucial element impacting the operational effectiveness of rural businesses. The presence of essential services such as water and electricity was discovered to have a substantial influence on the condition of the retail sector. The paper provides numerous recommendations based on the findings. The implementation of these recommendations is anticipated to stimulate economic expansion, boost living standards, and promote overall welfare in rural regions. By effectively dealing with the highlighted causes, the communities in Hwange have the opportunity to experience positive changes in their retailing industry, which will result in sustainable development and an enhanced quality of life.

Mapping the relationship between traffic accidents, road network configuration, and urban land use

Understanding the nature of traffic accidents in relation to urban access networks is crucial for building safer and more resilient cities. This paper examines the issue of traffic accidents through the lenses of urban configurational theory and urban land use. Three data layers were used in the study, including space syntax analysis conducted in Depthmap X, geotagged traffic accidents collected by the police department, and geotagged land-use data. The method involved superimposing these data layers and exploring potential correlations using a geographic information system (GIS). The findings indicate significant correlations between the spatial frequency of traffic accidents and the choice measure (at 2500 m), local integration, and active land use. The findings of this study can help inform planners and policymakers about the best location to implement safety measures to reduce the risk of traffic accidents in urban access networks.

A mathematical perspective on Romanisation: Modelling the Roman road activation process in ancient Tunisia.

Romanisation is a multi-faceted historical phenomenon with profound and lasting cultural impact on the ancient world. In the modern-day territory of Tunisia, this is particularly manifest during the first four centuries AD, under the reign of the Roman Empire. We derive a reduced, operational concept of Romanisation as a cultural diffusion process that is observable in the archaeological remains of the Roman era settlement system. We then introduce a novel mathematical model that computes spatio-temporal approximations for the Romanisation of the settlement system. The model is based on the concept of temporal road activation and makes minimal assumptions regarding input data quality. The results of our study contribute to the understanding of the time dynamics of the region's road network, under the influence of Romanisation. Our model can be applied in similar archaeological research scenarios, to generate spatio-temporal backbones for the analysis of otherwise intractably complex social processes.

Urban Spatiotemporal Event Prediction Using Convolutional Neural Network and Road Feature Fusion Network

The security challenges faced by smart cities are attracting more attention from more people. Criminal activities and disasters can have a significant impact on the stability of a city, resulting in a loss of safety and property for its residents. Therefore, predicting the occurrence of urban events in advance is of utmost importance. However, current methods fail to consider the impact of road information on the distribution of cases and the fusion of information at different scales. In order to solve the above problems, an urban spatiotemporal event prediction method based on a convolutional neural network (CNN) and road feature fusion network (FFN) named CNN-rFFN is proposed in this paper. The method is divided into two stages: The first stage constructs feature map and structure of CNN then selects the optimal feature map and number of CNN layers. The second stage extracts urban road network information using multiscale convolution and incorporates the extracted road network feature information into the CNN. Some comparison experiments are conducted on the 2018–2019 urban patrol events dataset in Zhengzhou City, China. The CNN-rFFN method has an R2 value of 0.9430, which is higher than the CNN, CNN-LSTM, Dilated-CNN, ResNet, and ST-ResNet algorithms. The experimental results demonstrate that the CNN-rFFN method has better performance than other methods.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.

The choice of algorithms for solving a multi-agent routing problem based on solving related problems

The paper considers the problem of reducing the complexity of $NP$-hard problems by using related problems for which an optimal or acceptable solution is already known. For multi-agent routing tasks, a technique is used based on network clustering consistent with traveling salesman routes on each cluster and constructing routes that take into account the limitation of delivery time windows. A mathematical model is presented that corresponds to a block of pseudo-Boolean conditional optimization with constraints in the form of disjunctive normal forms that allows polynomial solvability and a block of time constraints. The results of choosing metaheuristics based on related problems are used in a program for the delivery of goods by many agents to consumers located at the vertices of the regional infrastructure road network.

Delivery Route Scheduling of Heterogeneous Robotic System with Customers Satisfaction by Using Multi-Objective Artificial Bee Colony Algorithm

This study addresses the route scheduling problem for the heterogeneous robotic delivery system (HRDS) that perform delivery tasks in an urban environment. The HRDS comprises two distinct types of vehicles: an unmanned ground vehicle (UGV), which is constrained by road networks, and an unmanned aerial vehicle (UAV), which is capable of traversing terrain but has limitations in terms of energy and payload. The problem is formulated as an optimal route scheduling problem in a road network, where the goal is to find the route with minimum delivery cost and maximum customer satisfaction (CS) enabling the UAV to deliver packages to customers. We propose a new method of route scheduling based on an improved artificial bee colony algorithm (ABC) and the non-dominated sorting genetic algorithm II (NSGA-II) that provides the optimal delivery route. The effectiveness and superiority of the method we proposed are demonstrated by comparison in simulations. Moreover, the physical experiments further validate the practicality of the model and method.

Supporting Fire Response: Advanced Spatial Data Analytics for Hydrant Access Assessment

ABSTRACTCalifornia faces increasing wildfire frequency and severity, necessitating effective firefighting strategies along with well‐located infrastructure. Hydrants are vital for urban safety and must comply with established standards to adequately protect people and property. One such standard involves hydrant spacing, critical for ensuring effective emergency response in the event of a fire. Traditional geographic information systems techniques are not well suited to fully assess hydrant networks, especially in the context of spacing given complex road network geographies. This paper introduces an innovative approach to assess hydrant spacing. The developed technique is applied in the evaluation of infrastructure in Santa Barbara, California. The results reveal significant code violations along streets throughout the region, suggesting clustered pockets of vulnerability and risk. Potential strategies emerge from this analysis to enhance firefighting preparedness, offering guidance to local agencies regarding hydrant system limitations across this fire‐prone region.

Deep Learning based Road Traffic Assessment for Vehicle Rerouting: An Extensive Experimental Study of RetinaNet and YOLO Models

Currently, there is a notable prevalence of substantial traffic congestion and frequent vehicular accidents on roadways in contemporary times. Amalgamation of latest front-line technologies involving Internet of Things (IoT) and image classification has immense potential to advance the progress of a proficient traffic regulation system. To mitigate the occurrence of vehicular accidents, our research endeavors revolve around the comprehensive analysis of the prevailing road conditions. This meticulous examination allows us to effectively automate traffic routes orchestration, ensuring smooth vehicular movement across all lanes of the road network. The analysis of traffic patterns is conducted through the utilization of visual data images. The real time captured traffic images undergo processing using various object detection models named RetinaNet and the YOLO (You Only Look Once) models. A series of comparative evaluations suggests an improved traffic object identification capacity for the RetinaNet model as compared to all YOLO models.

More than one landslide per road kilometer – surveying and modeling mass movements along the Rishikesh–Joshimath (NH-7) highway, Uttarakhand, India

Abstract. The rapidly expanding Himalayan road network connects rural mountainous regions. However, the fragility of the landscape and poor road construction practices lead to frequent mass movements alongside roads. In this study, we investigate fully or partially road-blocking landslides along National Highway (NH-7) in Uttarakhand, India, between Rishikesh and Joshimath. Based on an inventory of >300 landslides along the ∼250 km long corridor following exceptionally high rainfall during September and October 2022, we identify the main controls on the spatial occurrence of mass-movement events. Our analysis and modeling approach conceptualizes landslides as a network-attached spatial point pattern. We evaluate different gridded rainfall products and infer the controls on landslide occurrence using Bayesian analysis of an inhomogeneous Poisson process model. Our results reveal that slope, rainfall amounts, lithology and road widening are the main controls on landslide occurrence. The individual effects of aggregated lithozones are consistent with previous assessments of landslide susceptibilities of rock types in the Himalayas. Our model spatially predicts landslide occurrences and can be adapted to other rainfall scenarios, thus having potential applications for efficiently allocating efforts for road maintenance. To this end, our results highlight the vulnerability of the Himalayan road network to landslides. Climate change and increasing exposure along this pilgrimage route will likely exacerbate landslide risk along the NH-7 in the future.

Understanding the role of street network configurations in the placement of illegitimately operating facilities

Understanding the role of street network configurations in the placement of illegitimately operating facilities

Narrow road extraction from high-resolution remote sensing images: SWGE-Net and MSIF-Net

ABSTRACT Accurate and complete road network extraction plays a critical role in urban planning, street navigation, and emergency response. At present, narrow roads are a main feature in most public road datasets. However, the continuity and boundary completeness of the extraction results for these narrow roads are relatively poor, due to their varied shapes, uneven spatial distribution, and the presence of various interfering elements. To address these issues, this study introduces a novel network, the Self-weighted Global Context Road Extraction Network (SWGE-Net), which integrates a dilate block and an improved coordinate attention mechanism to effectively capture the complex details and spatial information of narrow roads. Furthermore, most public road training datasets often lack labels for very narrow roads, this omission leads to poor extraction results for these roads in test datasets. In order to further improve the extraction capability for unlabeled, extremely narrow roads, this study introduces another network called the Multi-scale Information Fusion Road Extraction Network (MSIF-Net), which uses the same encoders as SWGE-Net and has a special module for merging information at different scales. This module, with a dilate block and pyramid pooling-based decoder, makes the network better at recognizing and combining features of different sizes. Experimental results indicate that SWGE-Net outperforms the baseline network with road IoU scores of 71.57% and 60.67% on the DeepGlobe and CHN6-CUG road datasets, respectively an improvement of 18.51% and 5.40%. Meanwhile, MSIF-Net not only exceeds the baseline in road IoU scores for both datasets, but also achieves the best performance in extracting unlabeled, extremely narrow roads in qualitative experiments.

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.

Navigating Public–Private Partnership success: A cross‐country study of key factors in Nigeria and South Africa

AbstractPublic–private partnership (PPP) has delivered expected results on a number of significant projects. Yet, despite the growing appeal, problems have been encountered which have either resulted in slow progress or the total failure of such arrangements. This study offers a comparison between Nigeria and South Africa focusing on several identified critical success factors (CSFs) for PPPs. We rely on secondary data to compare Nigeria and South Africa based on the top ten ranking CSFs identified in the literature. Findings reveal that both countries account for the highest number of PPP projects in Africa but have not recorded huge successes as a result of their shortcomings in important CSFs responsible for the success of PPPs. Nigeria, for instance, is still being challenged with poor energy supply, bad road networks, and ineffective waste management practices, among others. South Africa appears to have had better success in PPP delivery due to a suitable financial market, stable economic policy, favorable legal framework, and a transparent procurement process. We conclude by highlighting the key CSFs for PPPs in Nigeria and South Africa and emphasize the need to address the identified shortcomings. The relevance of this study is in its contribution to the understanding of CSF in PPP projects thereby offering useful insights for policy makers and administrators in improving the success rate of PPPs and enhancing the delivery of public services in these countries.Related ArticlesIkeanyibe, Okechukwu Marcellus. 2018. “Bureaucratic Politics and the Implementation of Liberalization Reforms in Nigeria: A Study of the Unbundling and Reorganization of the Nigerian National Petroleum Corporation.” Politics & Policy 46(2): 263–94. https://doi.org/10.1111/polp.12249.Iwuoha, Victor Chidubem, Nneka Ifeoma Okafor, and Emmanuel Ifeadike. 2022. “State Regulation of Nigeria's Maritime Ports: Exploring the Impact of Port Concession on both the Regulator and the Operators.” Politics & Policy 50(5): 1032–52. https://doi.org/10.1111/polp.12495.McNamara, Madeleine W., John C. Morris, and Martin Mayer. 2014. “Expanding the Universe of Multi‐Organizational Arrangements: Contingent Coordination and the Deepwater Horizon Transportation Challenges.” Politics & Policy 42(3): 346–68. https://doi.org/10.1111/polp.12073.

Variational Autoencoders and Generative Adversarial Networks for Multivariate Scenario Generation

When making decisions with lasting implications over a medium to long timeframe, it is essential to consider not only the most probable scenario, possibly obtained through a forecasting model, but also a range of potential outcomes. This approach allows for effective risk mitigation across a spectrum of scenarios, including less probable ones, and enhances the resilience of planning strategies. In this paper, we demonstrate the development of a generative model capable of learning the multivariate joint probability distribution of link speeds on a road network, using real sensor data. To further enhance the performance of our Generative Adversarial Network model, we employed a Variational AutoEncoder for pre-training the generator network. Experimental results, conducted on three distinct benchmark datasets, highlight the potential of the proposed model in generating new scenario samples of multivariate variables. The Wasserstein distance between the generated distribution and the real data, confirms the good performance of our model compared to state-of-the-art models, based on copulae. The proposed model has shown its ability to generate scenarios that preserve correlations among variables, while producing samples that faithfully represent the empirical marginal distributions.

A comparison between bridge-related and non-bridge crashes: severity, vehicle involvement, and motorists’ characteristics

Objectives Bridges, though representing a small portion of transport networks by length, are still prone to traffic crashes. Despite extensive research on bridge-related crashes globally, there’s a scarcity of studies exploring differences between bridge- and non-bridge-related crashes. Thus, this paper attempts to add to the current knowledge by comparing bridge-related crashes and non-bridge crashes using a case study in Victoria, Australia. Methods By utilizing crash data in Victoria between January 2006 and April 2023; the Chi-squared test is conducted to examine differences between crash types. Partial proportion odds models are then employed to establish if bridge-related crashes tend to be more severe. Results In general, the analysis reveals that bridge-related crashes tend to result in more severe injury outcomes compared to non-bridge crashes. Furthermore, there is evidence that bridge-related crashes are more likely to be single-vehicle, to involve a heavy vehicle as well as young/male motorists, and to occur on high-speed roads, regional areas and at nighttime than non-bridge crashes. Conclusions A targeted program aiming at enhancing traffic safety at bridges is recommended due to the higher severity of bridge-related crashes. Considering the presence of bridges on the road network, and particularly in regional areas where many are in poorer conditions, it is essential to consider retrofitting as well as low-cost solutions in order to complement motorist education and awareness programs. Specific groups with higher risks of being involved in bridge-related crashes, such as young male motorists, should be targeted in these programs.