GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network

Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

Complex disaster response framework to reduce urban disaster vulnerability

Cities are vulnerable to a range of disasters that can occur simultaneously due to their complexity. Therefore, an effective disaster response plan is needed to reduce the disaster vulnerability of cities. In particular, evacuation route management is important for reducing the losses from a disaster. Efficient disaster response can be realized by searching for suitable evacuation routes and effective road network management. In this paper, we propose a disaster response framework based on a multilayered road network structure and evacuation routes based on our road network. The suggested road structure consists of three layers for the effective management of the network. An A* algorithm-based search for multiple evacuation routes under different conditions in response to an individual disaster on the configured road map provides a safe route for evacuees. As a result, the damage caused by disasters in urban areas can be ameliorated.

[Identifying Relationship Between Nutrient Contents in Road-Deposited Sediment and Urban Basic Elements Based on Kernel Density Estimation]

Understanding the spatial connection between urban basic elements and pollutants in road-deposited sediment (RDS) is beneficial to the quantitative explanation of source areas and the precise management of urban nonpoint source pollution. The objective of this study was to explore the spatial connection between urban basic elements and nutrient contents in RDS, using Hanyang district of Wuhan city as a case study area. Total nitrogen (TN), total phosphorus (TP), exchangeable phosphorus (Ex-P), aluminum-bound phosphorus (Al-P), iron-bound phosphorus (Fe-P), occluded phosphorus (Oc-P), apatite phosphorus (Ca-P), detrital apatite phosphorus (De-P), and organophosphorus (Or-P) were chosen as representative pollutants, using the kernel density of the road network and seven groups of points of interest to represent the spatial distribution of urban basic elements. Through correlation analysis, the relationship between urban basic elements and nutrient contents in RDS was examined. The results showed that nutrient contents in RDS from Hanyang varied significantly with location. Specifically, Al-P, Oc-P, and Or-P had greater spatial variability in contrast with that of Ex-P and Fe-P, and the spatial variabilities of Ca-P and De-P both varied considerably with the particle size of the RDS. All types of urban basic elements and nutrient contents in RDS were correlated to different extents. Ex-P, Al-P, Oc-P, and Or-P were negatively correlated with the kernel density of most urban basic elements, whereas TN, TP, Fe-P, Ca-P, and De-P showed a positive correlation with the kernel density of most urban basic elements. TN, Al-P, Fe-P, Ca-P, and Or-P appeared to have much stronger correlation with the kernel density of urban basic elements compared to other selected pollutants, whereas the extent of correlation between them was influenced by the particle size of the RDS. In general, the correlation between nutrient contents in RDS and the kernel density of urban basic elements was more significant when the particle size of the RDS was under 450 μm and more robust when the particle size of the RDS was under 150 μm.

Investigating the Invasion Pattern of the Alien Plant Solanum elaeagnifolium Cav. (Silverleaf Nightshade): Environmental and Human-Induced Drivers

Invasive alien plant species have impacts on nature conservation, ecosystem services and agricultural production. To identify environmental and human-related drivers of the invasion of Solanum elaeagnifolium (Solanaceae)—one of the worst alien invasive plants worldwide—we conducted an extensive drive-by survey across the Greek territory (presence/absence data; all national major multilane highways; 12–25% of the remaining road network; driven 3–5 times during 2000–2020). These data were linked in GIS with (i) physical environmental attributes (elevation, climate, soil properties) and (ii) type and intensity of human-related activities (land uses, settlements and road type). Compared to previous records, our survey showed that the range of S. elaeagnifolium increased by 1750% during the last decades, doubling its main distribution centers and reaching higher elevations. Our study revealed that the presence of S. elaeagnifolium is associated with (i) higher maximum temperatures and precipitation in summer and low precipitation in winter, as well as with (ii) soil disturbance related to agricultural activities, settlements and road networks, thus facilitating its spread mainly at low altitudes. Our study elucidates the current invasion pattern of S. elaeagnifolium and highlights the urgent need for its widespread monitoring, at least in the noninvaded areas in Greece that have been surveyed in this study. Preventative measures and integrative initiatives should be implemented quickly, and urgently incorporated into current agricultural, road network and conservation-management regimes.

Enhancing Sustainability and Resilience through Multi-Level Infrastructure Planning

Resilient planning demands not only resilient actions, but also resilient implementation, which promotes adaptive capacity for the attainment of the planned objectives. This requires, in the case of multi-level infrastructure systems, the simultaneous pursuit of bottom-up infrastructure planning for the promotion of adaptive capacity, and of top-down approaches for the achievement of global objectives and the reduction of structural vulnerabilities and imbalances. Though several authors have pointed out the need to balance bottom-up flexibility with top-down hierarchical control for better plan implementation, very few methods have yet been developed with this aim, least of all with a multi-objective perspective. This work addressed this lack by including, for the first time, the mitigation of urban vulnerability, the improvement of road network condition, and the minimization of the economic cost as objectives in a resilient planning process in which both actions and their implementation are planned for a controlled, sustainable development. Building on Urban planning support system (UPSS), a previously developed planning tool, the improved planning support system affords a planning alternative over the Spanish road network, with the best multi-objective balance between optimization, risk, and opportunity. The planning process then formalizes local adaptive capacity as the capacity to vary the selected planning alternative within certain limits, and global risk control as the duties that should be achieved in exchange. Finally, by means of multi-objective optimization, the method reveals the multi-objective trade-offs between local opportunity, global risk, and rights and duties at local scale, thus providing deeper understanding for better informed decision-making.

Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City

Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban expansion and have significant influences on human activities, the environment, and socioeconomic development. Understanding the urban road network expansion pattern and its corresponding social and environmental effects is a reasonable way to optimize comprehensive urban planning and keep the city sustainable. This paper analyzes the spatiotemporal dynamics of urban road growth and uses spatial statistic models to describe its spatial patterns in rapid developing cities through a case study of Nanjing, China. A kernel density estimation model is used to describe the spatiotemporal distribution patterns of the road network. A geographically weighted regression (GWR) is applied to generate the social and environmental variance influenced by the urban road network expansion. The results reveal that the distribution of the road network shows a morphological character of two horizontal and one vertical concentration lines. From 2012 to 2016, the density of the urban road network increased significantly and developed some obvious focus centers. The development of the urban road network had a strong correlation with socioeconomic and environmental factors, which however, influenced it at different degrees in different districts. This study enhances the understanding of the effects of socio-economic and environmental factors on urban road network expansion, a significant indicator of urban expansion, in different circumstances. The study will provide useful understanding and knowledge to planning departments and other decision makers to maintain sustainable development.

A century of sprawl in the United States

The urban street network is one of the most permanent features of cities. Once laid down, the pattern of streets determines urban form and the level of sprawl for decades to come. We present a high-resolution time series of urban sprawl, as measured through street network connectivity, in the United States from 1920 to 2012. Sprawl started well before private car ownership was dominant and grew steadily until the mid-1990s. Over the last two decades, however, new streets have become significantly more connected and grid-like; the peak in street-network sprawl in the United States occurred in ∼ 1994. By one measure of connectivity, the mean nodal degree of intersections, sprawl fell by ∼ 9% between 1994 and 2012. We analyze spatial variation in these changes and demonstrate the persistence of sprawl. Places that were built with a low-connectivity street network tend to stay that way, even as the network expands. We also find suggestive evidence that local government policies impact sprawl, as the largest increases in connectivity have occurred in places with policies to promote gridded streets and similar New Urbanist design principles. We provide for public use a county-level version of our street-network sprawl dataset comprising a time series of nearly 100 y.