Road Network Structure Research Articles

TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting.

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely on fixed road network structures, failing to account for the dynamic spatial correlations that vary over time. To address this, we propose a Transformer-Enhanced Adaptive Graph Convolutional Network (TEA-GCN) that alternately learns temporal and spatial correlations in traffic data layer-by-layer. Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. Experimental results on two public traffic datasets demonstrate the effectiveness of the proposed model compared to other state-of-the-art traffic flow prediction methods.

Exploring the Effect of Road Network Structure on Inter-Regional Accessibility in a Diverse Road Network

Abstract. Accessibility analyses quantify the level of access to certain areas or opportunities, such as employment and healthcare facilities. Since public data is often aggregated at the level of regions, such as administrative units, it is useful to quantify accessibility between regions. Many factors influence inter-regional accessibility, most notably the accessibility metric used, and the way in which regions are chosen. This paper investigates the effects of road network structure on accessibility, using a previously developed inter-regional accessibility model that bases its accessibility metric on travel distance via the road network. This paper considers an area within the City of Tshwane municipality in South Africa. We investigate the effects of road structure in two ways. Firstly, regions are chosen based on the road network structure, which is done by extending a previously developed road network clustering algorithm for this novel use. Different spatial scales of regionalisation are considered, and the accessibility between these regions is compared to the accessibility between administrative units within the study area. Secondly, the effect of road network homogeneity on accessibility is investigated, where homogeneity corresponds to a uniform concentration of roads across a region. The results show that although road network homogeneity does not significantly correlate with accessibility, the way in which regions are chosen and their spatial scale has a strong effect on the results of the accessibility model. Our novel method of obtaining regions thus provides fresh insights into road-based accessibility within the City of Tshwane.

Research on Smart City Road Network Capacity Optimization Configuration Based on Deep Learning Algorithms

At present, the urban traffic system is faced with the problems of congestion and low efficiency, and the traditional methods have certain limitations when dealing with the complex urban road network. This paper aims to explore a new method of capacity Optimization of Road Networks in a Smart City environment and proposes a method based on a Graph Neural network, named “Smart City Road optimization Graph Neural Network” (SCRO-GNN). SCRO-GNN first collects and preprocesses multi-source data, including road network data, traffic flow, accident records, environmental factors, etc. The key node and edge features, including the number of lanes at the intersection, the traffic flow of the section, and the adjacency matrix of the road network are defined to characterize the structure of the road network. Then, the graph neural network model is constructed and trained to predict the traffic flow of different sections and evaluate the road capacity by using the graph structure of the road network. This paper tests the performance of SCRO-GNN on real road networks in multiple cities. The results show that, compared with the traditional traffic flow prediction model, SCRO-GNN can significantly improve the prediction accuracy, especially when dealing with the highly complex urban road network structure. Based on these predictions, the proposed optimization strategy performed well in reducing traffic congestion and improving the efficiency of road use. The research in this paper not only demonstrates the potential of graph neural networks in smart city road network optimization, but also provides a new direction for future traffic system research. The successful implementation of the SCRO-GNN approach is expected to provide more efficient and intelligent solutions for urban traffic management and planning.

Autonomous trajectory planning method for aircraft based on ripple diffusion algorithm

Aiming at the problem that route optimization in dynamic continuous airspace (non-discrete road network) is difficult to ensure optimality and has low computational efficiency, a non-circular ripple diffusion algorithm is proposed. Firstly, considering the influence of time-varying airflow and obstacle area in dynamic airspace, the airspace wind speed model, route network and dynamic network weight model are established, and 6 airspace environments and 4 road network structures with the influence of time-varying airflow and obstacle area are constructed. Then, the effects of traditional dynamic path optimization algorithm, co-evolutionary path optimization algorithm and non-circular ripple diffusion algorithm on route optimization are compared in 1000 experiments, taking the optimized path length, flight time and calculation time as indicators. The simulation results show that the traditional dynamic path optimization algorithm has the worst effect under the three indicators, and the calculation time of non-circular ripple diffusion algorithm is significantly less than that of the other two algorithms, which effectively improves the engineering calculation efficiency.

A Memory-augmented Conditional Neural Process model for traffic prediction

This paper presents the first neural process-based model for traffic prediction, the Memory-augmented Conditional Neural Process (MemCNP). Spatio-temporal traffic prediction involves predicting future traffic patterns based on historical traffic data and the road network structure. This problem remains a challenge due to the dynamic and heterogeneous nature of urban traffic. Existing models often struggle to capture these complexities, particularly in data-limited scenarios. To address these limitations, our model presents a novel framework for uncertainty estimation based on the conditional neural process, and further incorporates a memory network module designed to acquire a representative contextual reference, thereby improving model performance under complex data distributions. By integrating the conditional neural process and the memory network, MemCNP enables the learning of the most representative contexts through iterative updates, enhancing the model’s generalisability. This allows our model to be applicable beyond car traffic, effectively handling diverse real-world traffic scenarios, including urban non-motorised traffic such as cycling, which is essential for advancing more sustainable transportation systems. This is demonstrated by comprehensive experimental results on six benchmark datasets (PeMS04, PeMS07, PeMS08, NYCTaxi, CHIBike, and T-Drive) against existing state-of-the-art traffic prediction models, where MemCNP demonstrates superior performance. Additionally, through ablation and reliability studies, we provide a comprehensive analysis of the model’s effectiveness.

SAGE-GSAN: A graph-based method for estimating urban taxi CO emissions using street view images

Accurately predicting the carbon emissions of urban street vehicles is a current challenge in the field of urban transportation. This study proposed a new SAGE-GSAN model (Graph SAmple and aggreGatE - Graph Spatial Attention Network) to solve this problem. It combines graph neural networks with streetscape features and road network structure to predict the traffic carbon monoxide emissions at the street level. The method takes the street view images, the 5075 roads network structure in Wuhan and 19 street visual elements as the input features, and the carbon monoxide emissions obtained from the driving trajectories as the prediction data. The method achieved an experimental accuracy of 81.4% in predicting carbon monoxide emissions from street cabs. This study also compares the prediction results of traditional neural networks and analyzes the effects of different street-level features and graph convolution layers on the prediction accuracy. The results of this study show that the graph neural network and attention mechanism techniques could solve the fine-grained carbon emission prediction problem at the urban street level effectively. The model code is shared at the: https://github.com/zou9229/CO_Predict_Code.

Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression

This paper studies the transportation of emergency supplies for mountain wildfire suppression by trucks, drones, and motorcycles. The primary objective is to minimize the time of emergency supplies delivery. By examining the structure of mountainous road networks, the involvement of motorcycles and unmanned aerial vehicles (drones) in collaborative delivery operations, the risk associated with fire spread, and the optimal location for supplies distribution, this problem has been formulated as a collaborative truck–drone–motorcycle location-routing robust optimization model. The aim of introducing a robust optimization model is to ensure the feasibility of the routes under uncertain demand conditions. A box uncertainty set is employed to describe the uncertainty of emergent demands. An improved adaptive large neighborhood search (IALNS) algorithm is developed to solve this problem. A delete operator, a collaborative truck–drone delivery strategy, and a location strategy are introduced to handle various constraints involved in the problem, search the optimal location of the supplies distribution point, and compute vehicle routings. Our method has been compared with several existing algorithms, such as genetic algorithm, simulated annealing algorithm, tabu search algorithm, and whale optimization algorithm. Their average gap with IALNS are respectively 50.64%, 21.20%, 32.59%, and 19.13%. The experimental results demonstrate the efficacy and efficiency of the proposed model and algorithm.

Integrating query data for enhanced traffic forecasting: A Spatio-Temporal Graph Attention Convolution Network approach with delay modeling

Accurate prediction of road traffic conditions is essential for the effectiveness of Intelligent Transportation Systems (ITS) and the advancement of smart cities. While existing methodologies mainly focus on the complex structures of road networks and consider factors like weather, holidays, and accidents, the impact of social events is often overlooked. Queries, representing user requests in navigation applications, offer valuable insights into the popularity of social events and their effects on the road network. In this paper, we introduce the Query-aware Spatio-Temporal Graph Attention Convolution Network (QA-STGACN), which utilizes query data to model the impacts of events and forecast traffic conditions. Initially, spatial and temporal blocks are developed to extract spatial dependencies among road segments and capture traffic dynamics. A dynamic query-aware graph constructor then adaptively learns and retains periodic interactions between road segments and queries. Furthermore, three novel query-aware blocks – convolution, attention, and pattern transformation – are designed to uncover hidden dependencies between traffic conditions and query counts during events, as well as to model the delayed impacts of these counts on traffic. Ultimately, a gated fusion mechanism integrates the spatio-temporal correlations in road networks with the representations derived from queries. Comprehensive experiments on six datasets validate the superiority of QA-STGACN over robust baselines.

Investigation of the road network structure around rail transit stations

This study examined the connectivity of road networks around rail transit stations in İzmir, Türkiye, using intersection density and connected node ratio metrics. The analysis was conducted within 800 m, 600 m, and 400 m catchment areas around these stations, which were considered reasonable walking distances. Rail transit stations and road networks were digitized using ArcGIS Pro software. The research identified variations in connectivity scores among different stations and buffer zones. Stations in high-density areas like Konak and those near the ferry port showed higher connectivity scores, indicating well-integrated street networks that support multimodal transportation. In contrast, stations such as Ataşehir and Mavişehir, where intersection densities were lower, demonstrated significant connectivity challenges, underscoring the necessity for targeted urban planning interventions.

GEOSPATIAL MONITORING OF THE ROAD NETWORK OF THE DNIPROPETROV REGION

The development of the road transport network is considered to be one of the crucial factors of the sustainable development of the country and regions. The presence of an extensive and topologically efficient road network and their appropriate technical condition ensure not only transport accessibility of settlements and road safety and stability, but also the speed and volume of transportation. The geographical location of Dnipropetrovsk oblast determines the important role of the region's road transport network in the formation of both external and intra-regional socio-economic relations. The oblast's road transport network consists of a few tens of thousands of kilometres of roads of various types of surfaces, and it has a complex internal structure, specific organization, and functional orientation. Therefore, geospatial monitoring of roads in Dnipropetrovsk oblast is an effective instrument for solving a number of regional transportation problems. On the basis of crowdsourced geospatial data from Open Street Map using QGIS, the study of spatial and territorial features of networks (road length, level of spatial complexity, structure, provision, stability, density, etc.) of different types of roads both for the oblast as a whole and over its administrative districts was conducted. The calculation of the total length of the region's roads using geospatial analysis showed a figure of 70823 km (all types of pavement), of which 3005 km are roads of national importance (4% of the total length), 33219 km are roads of local importance (47%) and 34599 km are roads in settlements (49%). It has been established that the structure of the state-owned road network in the oblast varies from a centralized type in the western (with a central hub in the city of Kryvyi Rih), northern (with a central hub in the village of Mahdalynivka) and north-eastern (with a central hub in the city of Pavlohrad) parts to a distributed type in other regions. It should be noted that the centralized structure of the western part of the region is formed by national and regional roads, the northern part by territorial roads, and the north-eastern part by international and territorial roads. This plays an important role in assessing the efficiency and sustainability of the network. The length of state roads of the territorial type in the region significantly dominates the length of international and national roads, which, given the European integration orientation of Ukraine's economy, indicates shortcomings in the state of the region's road transport network. At the same time, almost all international roads pass through the oblast's cities and towns, which does not meet the requirements for this type of road. Therefore, given the favourable geographical location of the oblast, the formation of a new network of highways through its territory will ensure intensive socio-economic development of both the region and Ukraine as a whole. The average density of the national highway network is only 0.0049 km/km². This is several times less than the rate used in developed countries for citywide highways. The hot spots on the map are connected with the intersections of roads of different levels and are mostly located outside the territories of settlements. This allows us to conclude that the road network framework is a compromise between the historical location of settlements, natural and geographical features of the territory (terrain, hydrology, geological structure, etc.) and construction cost constraints. The length of local roads is several times longer than the length of national roads. As a result, their average density for the region is 1.04 km/km², with the highest concentration in the central parts of the region and the lowest in the east and south of the region. The analysis of the region's roads by administrative units (raions) confirmed their geospatial diversity. The longest roads are observed in the most economically developed districts – Dnipro and Kryvyi Rih raions, and the shortest – in Pavlohrad and Nikopol raions. The same tendency is typical for local roads and roads in the settlements of the raions. However, the longest roads of national importance are in Synelnykove, Dnipro and Kamianske raions. All types of roads in the raions of the eastern and north-eastern parts of the oblast (Novomoskovsk, Pavlohrad, and Synelnykove) are characterized by the highest figures for total density, demonstrating the best transport provision of roads in Dnipropetrovsk oblast in contrast to the western districts. The main reason for this is the low population size in the east of the oblast. Keywords: geospatial monitoring, road network, network structure, transport availability, road density, Dnipropetrovsk region.

Projecting traffic flows for road-based passenger transport in Europe for the analysis of climate impact

Road-based transport is a significant contributor to global transport emissions of greenhouse gases and local pollutants, thus contributing to the environmental impact of the mobility of people and goods. In order to develop strategies to mitigate this impact, it is necessary to build reliable emission inventories that include sector-specific emissions. Furthermore, the methods for creating these inventories should be applicable for forecasts and scenario calculations to facilitate the evaluation of pathways towards a more sustainable transport system. The study at hand proposes a model-based framework to predict travel demand and its spatial distribution for a bottom-up calculation of road transport emissions in Europe. With this framework, it is possible to calculate emissions based on the road network structure, traffic flows and vehicle types. In order to demonstrate the applicability of the framework for scenario calculations, it is applied to three exemplary scenarios where population data is modified. With the developed methodology, a tool for the large-scale assessment of emissions from road transport is provided, which is able to simulate the impact of socio-economic and economic changes on these emissions.

A Methodology for a Comprehensive Evaluation of an Urban Transport Network Structure Using Geographical Information Systems (GIS)

Abstract The transportation network is the most important component of an urban infrastructure. The efficiency of a region’s transportation system can be understood by the effectiveness of its transport network arrangement. The current study employs Geographical Information Systems (GIS) to assess the structure of a transport network in various clusters of the Hyderabad Metropolitan Area (HMA). The study focuses on a thorough assessment of the transport (or) road network structure in terms of various criteria such as connectivity, accessibility, maturity, and development. The study also categorizes each Transport Network Structural Evaluation Criteria (TNSEC) level as “very low, low, medium, high, or very high”. The goal of this research is to create a Road Network Structural Performance Index (RNSPI), which is usually used to measure the efficiency of a study area’s transportation network structure. The study’s findings serve as a foundation for city transportation planners to put efforts in the planning to enhance network facilities in the study region. The study also investigates the utility of Arc GIS software in assessing the structure of an urban transport network.

A Dual-Layer Complex Network-Based Quantitative Flood Vulnerability Assessment Method of Transportation Systems

Evaluating the vulnerability of urban transportation systems to flood disasters can provide scientific support for urban disaster prevention and mitigation. Current methods for assessing the flood vulnerability of urban roads often overlook the internal relationships within the complex spatial composition of road networks and surface structures. In this study, based on the theory of complex networks, a dual-layer network assessment model is established for evaluating the flood vulnerability of urban transportation systems by coupling basic geographic data with road network vector data. Unlike traditional methods, this model considers the complex relationship between road network structures and ground surfaces, uncovering a correlation between road network structure and road flood vulnerability. By utilizing this model, the flood vulnerability of road networks in Shenzhen, as well as the city’s spatial flood vulnerability, are quantitatively assessed. Based on the quantitative results, we create maps illustrating the distribution of road and spatial flood vulnerability in Shenzhen. The study results reflect that roads highly vulnerable to flooding are mainly located in the central urban area of the southwest, with the flood vulnerability spatially concentrated primarily in the northern and western regions. Using data from government reports, news stories, and other sources over the past five years, we compile recorded instances of urban waterlogging. The quantitative results of the model are consistent with the distribution trend in recorded waterlogging points, indicating that the model’s outcomes are authentic and reliable.

Road Network Traffic Flow Prediction Method Based on Graph Attention Networks

With the acceleration of urbanization and the continuous growth of transportation demand, the traffic management of smart city road networks has become increasingly complex and critical. Traffic flow prediction, as an important component of smart transportation systems, is of great significance for optimizing traffic planning and improving traffic efficiency. The study collected and preprocessed traffic data in the smart city road network, including multi-dimensional information such as traffic flow, road conditions, and meteorological data. Then, based on the idea of graph neural networks, we constructed the topological structure of the urban road network and abstracted elements such as roads and intersections into nodes, using edges to represent their connection relationships, thus forming a graph dataset. Next, we introduced an attention mechanism to extract more representative node features through the weighted aggregation of node features, thereby achieving effective modeling of urban road network traffic flow. During the model training phase, we used real traffic datasets for experimental verification and integrated various information such as time, space, and road features into the model. The experimental results show that compared to traditional methods, this research prediction method has achieved better performance in traffic flow prediction tasks, with higher prediction accuracy and robustness. It has stronger applicability and effectiveness in different traffic scenarios. By integrating multi-dimensional information and introducing attention mechanisms, this method has significant advantages in improving the accuracy and robustness of traffic flow prediction, and has important practical significance and application prospects for the construction of smart transportation systems and the development of smart cities.

Spatial Heterogeneity Analysis of Factors on Commuting Carbon Emissions: Evidence from the Shenzhen Metropolitan Area in China

As an essential part of daily life, commuting produces considerable carbon emissions and is currently receiving increased amounts of attention. Comprehensive explorations of carbon emissions and the spatial distribution of their effects based on previous studies are lacking. First, we adopt stepwise regression and geographically weighted regression (GWR) to explore the diverse impacts of carbon emissions on the different layers of metropolitan areas, employing factors from the perspectives of socioeconomics, transportation services, and road networks. Our findings show that optimizing the road network structure could be an effective approach to reducing carbon emissions from commuting, especially in the periphery of metropolitan areas. In addition, the mixed use of land contributes to reducing carbon emissions from commuting, especially in the central areas. Thus, the coverage of public transport should be improved, especially in peripheral regions. Policymakers should monitor the spatial heterogeneity of variables and develop suitable policies to adapt to the conditions of the different layers of metropolitan areas.

Transport accessibility and efficiency of PHC location in Kanyakumari district, Tamil Nadu

Transportation is an essential feature of modern economics. In general terms, as an economy grows and develops, it becomes more dependent on its transport sector. The efficient location of any PHC center mainly depends on the network pattern. The study area, Kanniyakumari, is the southernmost district of Tamil Nadu. Kanyakumari District lies between 77°15’ and 77°36’ of eastern longitude and 8°03’ and 8°35’ of northern longitude. The study area has 83 nodes and 120 edges in the transport network system. The main objectives of the paper are to analyze the variation in the degree of connectivity, accessibility, and nodal efficiency of the network pattern and to assess the relationship between road network structure and the location of the PHC center. (Primary Health Care). The present study depends on secondary data. The secondary sources were collected from various transport offices and institutions. Cartography mapping and buffer analysis are used by GIS software. The quantitative research approaches in network analysis were measures to determine the spatial structure, associate number, route development, nodal efficiency, and accessibility of a given PHC location. The Shimbels shortest path matrix was used to identify the health centrality of a node in the network, and its minimum accessibility was found for each PHC center. The detour index was computed based on knowing the route for existing network systems. The present study aims to understand the characteristics of the road transport network in relation to the structural location of the PHC of connectivity, accessibility, and route efficiency in Kanniyakumari district.

Delineating Source and Sink Zones of Trip Journeys in the Road Network Space

Source–sink zones refer to aggregated adjacent origins/destinations with homogeneous trip flow characteristics. Current relevant studies mostly detect source–sink zones based on outflow/inflow volumes without considering trip routes. Nevertheless, trip routes detail individuals’ journeys on road networks and give rise to relationships among human activities, road network structures, and land-use types. Therefore, this study developed a novel approach to delineate source–sink zones based on trip route aggregation on road networks. We first represented original trajectories using road segment sequences and applied the Latent Dirichlet Allocation (LDA) model to associate trajectories with route semantics. We then ran a hierarchical clustering operation to aggregate trajectories with similar route semantics. Finally, we adopted an adaptive multi-variable agglomeration strategy to associate the trajectory clusters with each traffic analysis zone to delineating source and sink zones, with a trajectory topic entropy defined as an indicator to analyze the dynamic impact between the road network and source–sink zones. We used taxi trajectories in Xiamen, China, to verify the effectiveness of the proposed method.

Correlation Analysis of Urban Road Network Structure and Spatial Distribution of Tourism Service Facilities at Multi-Scales Based on Tourists’ Travel Preferences

Harbin, as a popular tourist city in China, and the host of the 2025 Asian Winter Games, boasts rich tourism resources and significant potential for further development. The structure of the urban road network is a crucial factor influencing the spatial distribution of tourism service facilities. However, the current research on the correlation between the two, analyzed at multiple scales based on tourists’ travel preferences, is not sufficient. First, utilizing the questionnaire survey method to analyze tourists’ travel preferences and combining it with the theory of 15-min life circle, we determine the study scales at 500 m, 1000 m, 3000 m, and 5000 m. Secondly, the integration value and choice value of roads in the main urban area of Harbin are analyzed based on the theory of spatial syntax. The spatial distribution characteristics of tourism service facilities are then revealed through kernel density analysis. Finally, the correlation between the road network structure and the distribution of various types of tourism service facilities in Harbin at different scales is determined through buffer analysis and Pearson bivariate correlation analysis. The results show that: (1) Integration value plays a significant positive role in promoting the clustering of tourism service facilities, especially tending to cluster in areas with high integration value formed at a scale of 500 m; (2) At the scale of 3000 m, the distribution of tourism service facilities exhibits a significant correlation with the choice value; (3) The correlation between dining, shopping, and entertainment facilities and the integration value decreases with the increase in scale, whereas the spatial distribution of accommodation and attraction facilities does not exhibit a regular pattern with changes in integration value. In addition, this paper also puts forward targeted suggestions for optimizing the urban road network structure, reasonably locating tourism service facilities, and implementing balanced regional development. The contribution of this study is that it will help improve tourists’ travel experience in the city and provide scientific support for promoting the overall sustainable development of tourism in Harbin.

How Much Do Urban Terminal Delivery Paths Depend on Urban Roads – A Research Based on Bipartite Graph Network

The structural deficiencies of the terminal delivery path often make it the main culprit of urban traffic congestion and environmental pollution. Traditional studies of express networks regarded them as an independent entity, ignoring the endogenous role of urban road network morphology and structure. To solve this problem, this paper explored the spatial dependency of terminal delivery routes in Xi'an City based on the idea of a bipartite graph network. A spatial dependency matrix of delivery paths–urban roads was constructed by abstracting delivery paths as node-set A and urban roads as node-set B. In addition, three spatial dependencies indexes, including degree centrality, betweenness centrality and closeness centrality were introduced to analyse the coupling features of these two objects. The results show that these dependency measures can reflect the coupling features of urban terminal delivery paths and urban roads. Firstly, degree centrality demonstrates terminal delivery path coverage and coupling hierarchy and scale-free nature. Secondly, betweenness centrality presents the road utilisation balance of terminal delivery paths. Thirdly, closeness centrality explains how easy it is for delivery paths to connect with others.

Graph convolutional networks for street network analysis with a case study of urban polycentricity in Chinese cities

Graph theory effectively explains urban structures via street–street connectivity. However, systematic comparisons of street structures across cities remain challenging. This study employs graph convolutional networks (GCNs) to analyze street network structures. A two-branch GCN was used as the backbone to extract comparable features among street networks. The proposed approach was used to examine the structures of different urban road networks in a case study of polycentricity prediction across 298 Chinese cities. The model transformed approximately 4.5-million street segments into natural streets to create urban street graphs, which were subsequently analyzed to extract local and global embeddings. The extracted embeddings – with a portion labeled with a known urban polycentricity score – were used to predict the score for each city through a single-layer perceptron (SLP) model. Our results show consistency between the predicted polycentricity scores based on the derived street embeddings and those based on the population. Thus, the proposed GCN-based method can effectively predict the complexity and interconnection of street networks in different cities. This innovative integration of GCNs into urban studies demonstrates that deep learning techniques can analyze and comprehend the intricate patterns of street networks on a large scale.