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Barrier effect is a road‐related impact affecting several animal populations. It can be caused by behavioural responses towards roads (surface and/or gap avoidance), associated emissions (traffic‐emissions avoidance) and/or circulating vehicles (vehicle avoidance). Most studies so far have described road‐effect zones along major roads, without determining the actual factor inducing the behavioural response. The purpose of the present study was to assess the factors potentially causing road‐effect zones in a heterogeneous road network (with variations in road width, road surface and traffic volume) and eventually to estimate the reduction of habitat quality imposed by roads within a protected area (Doñana Biosphere Reserve, Spain). As model species, we used two ungulates, red deer Cervus elaphus and wild boar Sus scrofa. We surveyed the presence of both species along 200‐m transects. All transects started and were perpendicular to reference roads (those with a traffic volume above 10 cars per day), often intersecting unpaved minor roads with virtually no traffic. The presence probability of both species was mainly affected by the distance to the nearest road (in most cases unpaved roads without traffic), but also by the proximity to reference roads. Red deer presence was also affected by the traffic volume of the nearest reference road. At a regional scale, the overall road network within the protected area imposes a reduction in presence probability of 40% for red deer and 55% for wild boar. A road network optimization, decommissioning unused and unpaved roads, would re‐establish almost entirely the potential habitat quality (91% for both species). Synthesis and applications. We found that both study species avoided roads regardless of their surface or traffic volume, suggesting a response due to gap avoidance which may be based on the association between linear infrastructures and the possibility of vehicles occurring along them. The overall behavioural response can substantially decrease habitat quality over large scales, including the conservation value of protected areas. For this reason, we recommend road network optimization by road decommissioning to mitigate the impact of roads at a regional scale, with potential positive effects at ecosystem level.

Road infrastructure is essential for the moving goods and people. Given its logistical importance around the world, a deeper understanding of its network structure can improve connectivity, efficiency, and safety. When viewed through the lens of network science, this system reveals patterns and structural properties that can justify improvements and changes. It also strengthens the ability to plan for control, disaster recovery, and future investments. Brazil is highly dependent on road mobility, with approximately 75,000 kilometers of federal roads that serve as the main national and regional corridors. This paper provides an in-depth analysis of these road networks by modeling the federal system as weighted networks using road segments at both national and regional levels. First, we examine connectivity and topology using the distance between nodes as the primary weight. Next, we incorporate additional metrics: number of incidents, vehicle flow, incidents per kilometer, and flow per road lane. We then focus on community detection to identify clusters of road segments that form cohesive groups within both the national and regional networks. Additionally, we simulate resilience and vulnerability by removing selected nodes and edges to assess the impact of natural disasters on the network. Our findings aim to improve understanding of Brazil’s segmented federal road structure, enabling comparison with other models and providing actionable insights for assessing impacts and improving infrastructure.

With the advancement of urbanization and the proposal of sustainable development goals, the complexity and vulnerability of urban transportation systems have become increasingly prominent, and their reliability is directly related to the sustainable operation of urban transportation. The reliability of urban road networks, characterized by their dynamic nature, multi-scale characteristics, and anti-interference capabilities, directly restricts the functional guarantee of urban traffic and the efficiency of emergency response. To address the limitations of existing road network connectivity reliability assessment methods in representing time dynamics and modeling failure correlation, this study proposes a road network reliability assessment method based on a Dynamic Bayesian Network (DBN) by constructing a probabilistic reasoning model that integrates cascading failure characteristics. First, the connectivity reliability of the road network under random and targeted attack strategies was evaluated using a Monte Carlo simulation, revealing the impact of different attack strategies on network reliability. Subsequently, the congestion delay index is used as the standard of road section failure, considering the failure distribution and mutual dependence of road sections over time, a cascade failure mechanism is introduced, and a time-varying reliability assessment model based on a DBN is constructed. The effectiveness of the proposed method was verified through a case study of a partial road network in Dalian. The results show that ignoring cascading effects can significantly overestimate the reliability of the road network, especially during peak traffic hours, where such deviations may mask the real paralysis risks of the network. In contrast, the method proposed in this study fully considers time dynamics and failure correlation and can better capture the reliability of the road network under various dynamic conditions, providing a scientific basis for the sustainable planning and emergency management of urban traffic systems.

The widespread use of GPS navigations and trip planning on web has aroused considerable interests in fast and scalable path query processing. Existing research has mostly focused on static route optimization where the traffic network is assumed to be stable. Nevertheless, in most cases, route planning is in the presence of frequent updates to the traffic graph due to the dynamic nature of traffic network, and such updates always greatly affect the performance of route planning. Most existing methods, however, cannot efficiently support traffic aware route planning. In this paper, two efficient strategies are proposed to handle this problem. We analyze the traffic condition on the road network and explore spatio-temporal knowledge to guide effective route planning. In particular, several effective techniques are employed to avoid both unnecessary calculations on huge graph and excessive re-calculations caused by traffic condition updates. A comprehensive experiment is also conducted to evaluate the performance of our proposed strategies.

Fractal geometry has provided a new perspective for urban road network morphology research. This study systematically verifies and analyzes the spatial heterogeneity of fractal characteristics and influencing factors of urban road networks using spatial analysis. Here, Tokyo Metropolis was selected as a case, and the fractal dimensions of road networks were calculated. To determine the spatial heterogeneity in the relationship between fractal dimensions and influencing factors, we examined the spatial distribution characteristics of fractal dimensions using spatial autocorrelation analysis, selected population, build-up area density, and road network density as the explanatory variables, and established the global regression model and local regression model using ordinary least squares (OLS) and geographically weighted regression (GWR), respectively. The results indicated that the spatial distribution of fractal dimensions of the urban road network exhibited an obvious tendency toward geographical dependency. Considering the spatial heterogeneity in the relationship between the fractal characteristics of the road network and the influencing factors not only improves the reliability of analysis but also helps planners and decision-makers grasp the morphological characteristics of the urban road network and estimate the evolution of the road network, thereby promoting the development of urban road networks in a more orderly, efficient, and sustainable direction.

The focus of sustainable urban transportation development lies in realizing the untapped capacity potential of the existing road network and enhancing its operational efficiency without expanding its physical footprint. To quantify the supply capacity of road networks in mountainous cities, this paper converts the problem of solving the capacity of road networks into the problem of solving the minimum cut set in road networks from the perspective of road network capacity, using the idea of the auxiliary diagram method in graph theory. By improving the limitation that the auxiliary map method is only applicable to the single starting point and terminal point network, a regional road network capacity estimation model of a mountain city based on the auxiliary map is constructed. Combined with the actual regional road network, the model results presented in this paper show that the road network capacity calculated by the auxiliary graph method is 30,137 pcu/h. Using the improved traffic distribution simulation method, the network capacity is 38,776 pcu/h. Compared with the traffic distribution simulation method, the regional road network capacity model based on an auxiliary map proposed in this paper is more realistic.

This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world's knowledge of physical infrastructure in Africa. The AICD will provide a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It should also provide a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa. The AICD is based on an unprecedented effort to collect detailed economic and technical data on the infrastructure sectors in Africa. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. The first phase of the AICD focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. Under a second phase of the project, coverage is expanding to include as many of the additional African countries as possible.

In this paper, an optimisation model for recovery planning of road networks is presented in which both social and economic resilience is aimed to be achieved. The model is formulated as a bi-level multi-objective discrete network design problem which forms a non-convex mixed integer non-linear problem. Solved by a Branch and Bound method, the solution algorithm employs an outer approximation method to estimate the lower bound of each node in the Branch and Bound search tree. The solution algorithm exploits a unique approach for lower-bound computation dealing with a disrupted multi-class network that may not be able to satisfy the demand between all OD pairs due to damaged links. The model is assessed by applying it on the Sioux Falls network. It is also illustrated how the Pareto-optimal solutions achieved by the multi-objective optimisation can vary depending on the emphasis placed on different classes of vehicles.

Fractal geometry has been an important tool in multiple disciplines, but it is usually limited to the geometrical fractal: a shape made of parts similar to the whole in certain ways. Recently there has been increasing interest in structural fractality of complex networks (e.g., biological and Internet hyperlinks) for studying organizational principles and evolutionary rules. In this study, the structural fractality of road networks (a kind of complex geographical network) is examined for better understanding of the complexity and dynamics of the road system. Fifty road networks of the most populous counties in the United States have been used as cases. The Maximum Excluded Mass Burning (MEMB) algorithm rooted in physics has been employed to compute the fractal dimensions by covering the network structure with a series of box sizes (ℓ s ). It is found that road networks have structural fractality. The values of structural fractal dimension range from 2.94 to 4.90 spanning ℓ s = 5 to ℓ s = 15. To examine the self-similar property of the structure of a road network at several scales, small-world and scale-free analyses have been carried out. It was found that road networks are structural self-similarities. The fractal and self-similar structures in a road network help improve the efficiency of flow transmission in the system and keep a balance between chaos and order. These findings help us understand the complexity, organizational rules, and dynamic principles of an urban system and blur the borders across several disciplines. They also provide an empirical guide for urban design and transportation planning.

This study focused on pre-flood measures to estimate evacuation times impacted by flood depths and identify alternate routes to reduce loss of life and manage evacuation measures during flood disasters. Evacuation measures, including traffic characteristics, were reviewed according to different flood depths. Several scenarios were constructed for different flooding situations and traffic volumes. Evacuation times in the study area were evaluated and compared for all scenarios with reference to dry conditions. Results of network performance indicators compared to the dry situation showed that average speed dropped to 2 km/h, VHT rose above 200%, and VKT rose above 30%. Cumulative evacuee arrival percentage increased when flood levels were higher than 5 cm. Flood levels of 10–15, 15–20, 20–25, and 25–30 cm represented percentages of remaining evacuees at 9%, 19%, 49%, and 83%, respectively. Time taken to evacuate increased according to flood level. For flood depths of 5–30 cm, travel time increased by 40, 90, 260, and 670 min, respectively, suggesting the need for early evacuation before the flood situation becomes serious.