Road Network Structure Research Articles

Location and path planning for urban emergency rescue by a hybrid clustering and ant colony algorithm approach

Rescue station setup and rescue path planning are two important tasks in urban emergency rescue. The former task ensures rescue response capability and the latter task provides effective rescue solutions. When emergencies occur in cities, evacuees are distributed along the urban road network. Rescue resources refer to rescue vehicles whose available number and capacity are both limited. With the constraints of rescue resources and the number of rescues, this paper aims to simultaneously optimize the tasks of rescue station setup and rescue path planning. In the addressed scenario, the priority of each evacuee is quantified as a weight value that is used as the main optimization objective. To solve the problem, a comprehensive urban emergency rescue planning approach is proposed. The proposed approach consists of components of road network processing, road network weight calculation, rescue station setup and rescue path planning. For the setup of rescue stations, this paper employs a clustering method to provide a set of high-quality candidate rescue stations for subsequent path planning based on the locations of evacuees and the road network structure. For rescue path planning, an improved ant colony optimization algorithm is developed. The proposed method is called the planning algorithm with clustering and improved ant colony optimization (PA-C-IACO). The proposed PA-C-IACO redefines the degree of heuristic and pheromone concentration increments for transfer between intersections in the ant colony algorithm and incorporates a reward mechanism during the pheromone update process. Experimental results on six different size datasets show that PA-C-IACO outperforms state-of-the-art algorithms and shows good robustness and feasibility.

Attention based spatio-temporal graph convolutional network with focal loss for crash risk evaluation on urban road traffic network based on multi-source risks

The urban road transportation has presented a high probability of crash occurrence, and the aim of the present study is to evaluate the crash risk for urban road networks. However, the irregular structure of urban road networks, the high-dimensional spatio-temporal correlations among multi-source risks (i.e., the contributing risks from traffic flow, meteorological conditions, road design, and so forth), and the issue of data imbalance have brought challenges to this topic. To solve these issues, an Attention based Spatio-Temporal Graph Convolutional Network (ASTGCN) model with focal loss function is used for the first time to evaluate crash risk on an urban road network. This work can be summarized as (1) adopting the spatio-temporal graph convolution structure to capture the spatio-temporal properties and characterize the multi-source risks; (2) utilizing an attention mechanism network to address the critical contributing risks during crash risk evaluation; (3) introducing the focal loss function to improve the model performance impacted by the imbalanced data; and (4) investigating the different contributions of multi-source risks to model performance. The evaluation performance is tested in a real-world urban road traffic network. The raw data consists of 1239 crash records with corresponding datasets of traffic flow characteristics, meteorological conditions, road attributes and the topological structure of the road network. At the same time, three baseline models Artificial Neural Network (ANN), Random Forest (RF), and Deep Spatio-Temporal Graph Convolutional Network (DSTGCN) are compared to the proposed ASTGCN on the same datasets. Overall, the results show that ASTGCN outperforms the baseline models in several evaluation metrics. ASTGCN with focal loss function further improves performance by tackling the issues of dataset imbalance. Additionally, it is also found that the traffic flow risk is most crucial to model performance. The findings of the present study indicate that the proposed model can efficiently evaluate dynamic crash risk for urban road networks, which will benefit the safety management of urban road transportation.

Retracted: Research on Optimal Route Planning for Self-Driving Tour Based on Road Network Structure

Retracted: Research on Optimal Route Planning for Self-Driving Tour Based on Road Network Structure

A Graph Neural Network (GNN)-Based Approach for Real-Time Estimation of Traffic Speed in Sustainable Smart Cities

Planning effective routes and monitoring vehicle traffic are essential for creating sustainable smart cities. Accurate speed prediction is a key component of these efforts, as it aids in alleviating traffic congestion. While their physical proximity is important, the interconnection of these road segments is what significantly contributes to the increase of traffic congestion. This interconnectedness poses a significant challenge to increasing prediction accuracy. To address this, we propose a novel approach based on Deep Graph Neural Networks (DGNNs), which represent the connectedness of road sections as a graph using Graph Neural Networks (GNNs). In this study, we implement the proposed approach, called STGGAN, for real-time traffic-speed estimation using two different actual traffic datasets: PeMSD4 and PeMSD8. The experimental results validate the prediction accuracy values of 96.67% and 98.75% for the PeMSD4 and PeMSD8 datasets, respectively. The computation of mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) also shows a progressive decline in these error values with increasing iteration count, demonstrating the success of the suggested technique. To confirm the feasibility, reliability, and applicability of the suggested STGGAN technique, we also perform a comparison analysis, including several statistical, analytical, and machine-learning- and deep-learning-based approaches. Our work contributes significantly to the field of traffic-speed estimation by considering the structure and characteristics of road networks through the implementation of DGNNs. The proposed technique trains a neural network to accurately predict traffic flow using data from the entire road network. Additionally, we extend DGNNs by incorporating Gated Graph Attention Network (GGAN) blocks, enabling the modification of the input and output to sequential graphs. The prediction accuracy of the proposed model based on DGNNs is thoroughly evaluated through extensive tests on real-world datasets, providing a comprehensive comparison with existing state-of-the-art models for traffic-flow forecasting.

Modeling freight truck-related traffic crash hazards with uncertainties: A framework of interpretable Bayesian neural network with stochastic variational inference

Due to the increasing demand for goods movement, externalities from freight mobility have attracted much concern among local citizens and policymakers. Freight truck-related crash is one of these externalities and impacts urban freight transportation most drastically. Previous studies have mainly focused on correlation analyses of influencing factors based on crash density/count data, but have paid little attention to the inherent uncertainties of freight truck-related crashes (FTCs) from a spatial perspective. While establishing an interpretable analysis model for freight truck-related accidents that considers uncertainties is of great significance for promoting the robust development of urban freight transportation systems. Hence, this study proposes the concept of FTC hazard (FTCH), and employs the Bayesian neural network (BNN) model based on stochastic variational inference to model uncertainty. Considering the difficulty in interpreting deep learning-based models, this study introduces the local interpretable modelagnostic explanation (LIME) model into the analysis framework to explain the results of the neural network model. This study then verifies the feasibility of the proposed analysis framework using data from California from 2011 to 2020. Results show that FTCHs can be effectively modeled by predicting confidence intervals for effects of built environment factors, in particular demographics, land use, and road network structure. Results based on LIME values indicate the spatial heterogeneity in influence mechanisms on FTCHs between areas within the metropolitan regions and alongside the freeways. These findings may help transport planners and logistic managers develop more effective measures to avoid potential negative effects brought by FTCHs in local communities.

Recommendation of urban vehicle driving routes under traffic congestion: A traffic congestion regulation method considering road network equilibrium

Vehicle travel route selection is the main task of the traveler information service system in the intelligent transportation system. In this study, a dynamic graph convolutional neural network (DGCN) was constructed to perform multi-time-step travel time prediction for each road section of a road network, and vehicle travel route recommendation was performed according to travel demand and in consideration of factors such as avoiding congestion areas and balancing the road network. The road network partitioning was performed by a built weighted network model based on road network structure through the GN (Grivan-Newman) algorithm, which helps to identify the traffic status of congested areas. The results showed that the route selection recommendation for the avoidance of the congested area reduced the congestion equilibrium index of the urban road network. Therefore, considering the congestion avoidance mechanism from the global level of the road network can reduce the overall congestion degree.

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference

A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human behavior and high data requirements. In this work, we propose a deep-learning framework PL-TARMI, which leverages easily accessible data to achieve accurate fine-grained traffic crash risk map inference. Specifically, we integrate the satellite image and road network image, combine with other accessible data (e.g., point of interest distribution, human mobility data, traffic data, etc.) as input, and finally obtain the pixel-level traffic crash risk map, which could provide more reasonable traffic crash prevention guidance with a lower cost. Extensive experiments on real-world datasets demonstrate the effectiveness of PL-TARMI.

Knowledge Expansion and Consolidation for Continual Traffic Prediction With Expanding Graphs

Accurate traffic prediction plays a vital role in intelligent transport managements and applications. However, in the vast majority of existing works, the focus is mainly on modeling spatiotemporal correlations in static traffic networks. Thus, the continuous expansion and evolution of traffic networks are ignored. In this work, we study the problem of traffic prediction with expanding road network structures under the continual learning paradigm. Considering the model prediction performance, efficiency, and data accessibility, a SpatioTemporal Knowledge Expansion and Consolidation (STKEC) framework is proposed. This framework contains an influence-based knowledge expansion strategy to help the spatiotemporal learning model integrate new spatiotemporal traffic patterns and a memory-augmented knowledge consolidation mechanism to preserve the learned spatiotemporal patterns without accessing the data in previous graphs. Extensive experiments are conducted on a large-scale dataset and verify the superior performance of STKEC in continual traffic prediction.

Boundary effects on topological characteristics of urban road networks

Urban road networks (URNs), as simplified views and important components of cities, have different structures, resulting in varying levels of transport efficiency, accessibility, resilience, and many socio-economic indicators. Thus, topological characteristics of URNs have received great attention in the literature, while existing studies have used various boundaries to extract URNs for analysis. This naturally leads to the question of whether topological patterns concluded using small-size boundaries keep consistent with those uncovered using commonly adopted administrative boundaries or daily travel range-based boundaries. This paper conducts a large-scale empirical analysis to reveal the boundary effects on 22 topological metrics of URNs across 363 cities in mainland China. Statistical results show that boundaries have negligible effects on the average node degree, edge density, orientation entropy of road segments, and the eccentricity for the shortest or fastest routes, while other metrics including the clustering coefficient, proportion of high-level road segments, and average edge length together with route-related metrics such as average angular deviation show significant differences between road networks extracted using different boundaries. In addition, the high-centrality components identified using varied boundaries show significant differences in terms of their locations, with only 21%–28% of high-centrality nodes overlapping between the road networks extracted using administrative and daily travel range-based boundaries. These findings provide useful insights to assist urban planning and better predict the influence of a road network structure on the movement of people and the flow of socio-economic activities, particularly in the context of rapid urbanization and the ever-increasing sprawl of road networks.

Infrastructures connecting people: A mechanistic model for terrestrial transportation networks

Terrestrial Transportation Infrastructures (TTIs) are shaped by both socio-political and geographical factors, hence encoding crucial information about how resources and power are distributed through a territory. Therefore, analysing the structure of pathway, railway or road networks allows us to gain a better understanding of the political and social organization of the communities that created and maintained them. Network science can provide extremely useful tools to address quantitatively this issue. Here, focussing on passengers transport, we propose a methodology to shed light on the processes and forces that moulded transportation infrastructures into their current configuration, without having to rely on any additional information besides the topology of the network and the distribution of the population. Our approach is based on a simple mechanistic model that implements a wide spectrum of decision-making mechanisms (representing different power distributions) which could have driven the growth of a TTI. Thus, by adjusting a few model parameters, it is possible to generate several synthetic transportation networks, and compare across them and against the empirical system under study. An illustrative case study (i.e. the railway system in Catalonia, a region in Spain) is also provided to showcase the application of the proposed methodology. Our preliminary results highlight the potential of our approach, thus calling for further research.

Displacement Measurement of Urban Ramp Bridge Model Using Monocular High-Speed Camera

Urban ramp bridge plays an important role in unblocking urban traffic and improving urban road network structure. Aiming at the limitations of traditional contact sensors in global measurement, this paper adopts non-contact, high frame rate, high-precision monocular high-speed video measurement technology to measure the dynamic response of the ramp bridge model loaded on the shaking table. The feasibility of video measurement in structure measurement is verified by comparing with the results of wire displacement meter, and the overall measurement accuracy is better than 0.1mm. The seismic effects of two different isolation bearings are compared and analyzed, and the test shows that the plate rubber bearing has a better damping effect.

Analyzing accessibility of car campgrounds through road network structure software in Beijing

The development of self-driving tourism and automobile campgrounds depends on the construction of road infrastructure. In this study, we utilized aeronautical reconnaissance coverage geographic information system (ArcGIS), spatial analysis technology and accessibility model to quantitatively examine the accessibility and spatial pattern of automobile campgrounds in Beijing. Our findings reveal that Beijing's automobile campgrounds exhibit a circular distribution pattern, with decreasing accessibility from the center to the periphery areas. The overall accessibility maintains high-level road directivity, with an average travel time to automobile campgrounds ranging from 0.028 to 3.37 h. Access to automobile campgrounds is higher in the central and southern-central regions compared to the northwest and southwest regions, highlighting a highly unbalanced spatial pattern of accessibility. Finally, we provide suggestions for optimizing the accessibility of automobile campgrounds in Beijing based on our research.

Unraveling the Impact of Spatial Configuration on TOD Function Mix Use and Spatial Intensity: An Analysis of 47 Morning Top-Flow Stations in Beijing (2018–2020)

Urban rail transportation is an important public transportation network for realizing the dual carbon strategy and sustainable transportation development. A top-stream station is an important node in this network. This paper focuses on 47 top-stream station sites during the peak hours of workdays from 2018 to 2020 in Beijing (26 inbound and 22 outbound, with Beijing South Station as a double-stream station) and collects and analyzes information such as station area spatial texture, spatial organization efficiency, land use function, land use mix, POI (Point of Interest) distribution and functional mix, construction intensity, and population heat distribution. Through an analysis of the network topological structure characteristics of each station area, comparison of spatial efficiency differences, analysis of land use function composition and mix characteristics, and distribution of spatial construction intensity, this paper discusses the relationship between the spatial structure, spatial function, spatial intensity characteristics and key indicators of the built environment of station areas from the perspective of urban design. The conclusion shows that there is a close relationship between the function, structure, and strength of the overall built environment of the station domain at the theoretical level. The regression test, to some extent, confirms the close relationship between key indicators and expands the indicator system for measuring the fit relationship. The comparison between general station sites and headstream station sites shows that the fit relationship of indicators for headstream station sites is not completely the same as that for general station sites, indicating that the influencing factors for headstream station sites are diverse. This reminds investment, design, construction, and management teams in practice that the measurement and planning of the built environment space structure of the station domain should be based on local conditions and be closely related to the topological structure of the station domain’s rail network and road network structure. At the same time, whether the stability of the indicator system and the specific R-squared value have differences in various cities requires further verification. This paper explores and tries to raise questions about the research methodology of the built environment space structure, the measurement of the station domain, and the concepts of station-city coordination and development.

Inatorial forecasting method considering macro and micro characteristics of chaotic traffic flow

Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally, to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System (PeMS) are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this research is superior to those of the prevalent methods.

Missing Road Condition Imputation Using a Multi-View Heterogeneous Graph Network From GPS Trajectory

How to generate road conditions from urban GPS trajectory is an important problem in transportation systems. However, this computation process usually suffers from serious missing value problem due to the observation uncertainty or limited reports from crowdsourcing systems. Conventional tensor factorization approaches learn the spatio-temporal dependencies in a collaborative filtering way, which ignores the complex road network structure information and temporal heterogeneity. In this study, we propose a multi-view model with multiple aspects of prior knowledge to impute traffic state computed from a real-world trajectory dataset. More specifically, in the spatial view, rather than focusing on a specific type of road segment, we take the heterogeneity of road network into consideration and model the multiple relations of adjacent road segments. Meanwhile, the temporal pattern is also viewed as a heterogeneous graphical structure that discriminates the weekly/hourly adjacency in the temporal view. Finally, we fuse the above spatio-temporal features to provide a robust estimation under different sparse conditions. Intensive experiments on two types of missing scenarios (i.e., random and non-random) demonstrate that the proposed imputation method outperforms all the other state-of-the-art approaches. In addition, our model represents interpretable patterns for spatio-temporal graph analysis.

Accelerated and Refined Lane-Level Route-Planning Method Based on a New Road Network Model for Autonomous Vehicle Navigation

Lane-level route planning is a critical issue for a lane-level navigation system for autonomous vehicles. Current route-planning methods mainly focus on the road level and applying them directly to search for lane-level routes results in a reduction in search efficiency. In addition, previously developed lane-level methods lack consideration for vehicle characteristics and adaptability to multiple road network structures. To solve this issue, this study proposes an accelerated and refined lane-level route-planning algorithm based on a new lane-level road network model. First, five sub-layers are designed to refine the internal structure of the divided road and intersection areas so that the model can express multiple variations in road network structures. Then, a multi-level route-planning algorithm is designed for sequential planning at the road level, lane group level, lane section level, and lane level to reduce the search space and significantly improve routing efficiency. Last, an optimal lane determination algorithm considering traffic rules, vehicle characteristics, and optimization objectives is developed at the lane level to find the optimal lanes on roads with different configurations, including those with a constant or variable number of lanes while satisfying traffic rules and vehicle characteristics. Tests were performed on simulated road networks and a real road network. The results demonstrate the algorithm’s better adaptability to changing road network structures and vehicle characteristics compared with past hierarchical route planning, and its higher efficiency compared with direct route planning, past hierarchical route planning, and the Apollo route-planning method, which can better support autonomous vehicle navigation.

Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting

Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail to simultaneously capture the short-term and long-term temporal relations carried by the traffic flow data, and also suffer the over-smoothing problem. To overcome the problems, we propose a hierarchical traffic flow forecasting network by merging newly designed the long-term temporal Transformer network (LTT) and the spatio-temporal graph convolutional networks (STGC). Specifically, LTT aims to learn the long-term temporal relations among the traffic flow data, while the STGC module aims to capture the short-term temporal relations and spatial relations among the traffic flow data, respectively, via cascading between the one-dimensional convolution and the graph convolution. In addition, an attention fusion mechanism is proposed to combine the long-term with the short-term temporal relations as the input of the graph convolution layer in STGC, in order to mitigate the over-smoothing problem of GCN. Experimental results on three public traffic flow datasets prove the effectiveness and robustness of the proposed method.

Evaluation Methods and Optimization Strategies for Low-Carbon-Oriented Urban Road Network Structures: A Case Study of Shanghai

It is generally believed that the development of mass transportation is an effective and practical path by which to promote low-carbon development. As a form of infrastructure for the development of bus transits, urban road networks directly affect the planning and operation of a bus network. Existing studies have shown that urban road networks affect the layout effect of bus transit from aspects such as the topological network structure characteristics. Based on the existing foundation of research, this study aims to address evaluation methods for quantitative research on the topological structure of road networks, emphasizing that the topological structure characteristics are an important attribute affecting the morphological characteristics of the road network and also a key attribute affecting low-carbonization orientation. Furthermore, this study proposes an evaluation method for the low-carbon friendliness of urban road network structures and constructed a numerical comparison matrix. On this basis, the actual case of the central urban area of Shanghai was selected to conduct an empirical analysis of the evaluation of the low-carbon friendliness of the road network structure. Based on the street (township) as the regional unit scale, four types of evaluation results were obtained, corresponding to different levels of low-carbon friendliness. Based on the above analysis results, a low-carbon-oriented road network structure optimization strategy in Shanghai’s central urban area was further proposed to (1) develop countermeasures for low-carbon transportation development according to local conditions; (2) emphasize the key role of intersection forms in optimizing topology; (3) improve the connectivity level of important road sections; (4) encrypt the road network density in high-density development areas; (5) and combine this with other form of green transportation development to improve low-carbonization levels.

Spatio-temporal hierarchical MLP network for traffic forecasting

Traffic forecasting is an indispensable part of intelligent transportation systems. However, existing methods suffer from limited capability in capturing hierarchical temporal characteristics of the traffic time series. To be specific, they neglect the property that the time series is composed of trend-cyclical and seasonal parts. On the other hand, prior methods ignore the natural hierarchical structure of traffic road networks and thus fail to capture the macro spatial dependence of region networks. To address these issues, we propose a novel spatio-temporal hierarchical MLP network (STHMLP) for traffic forecasting. By adopting a decomposition architecture in the STHMLP, trend-cyclical and seasonal features are gradually grasped from multi-scale local compositions of traffic time series. For each scale of traffic time series, we design a fine module and a coarse module to extract spatio-temporal information from roads and regions, respectively. Specifically, the fine module utilizes spatial filters on the frequency domain features of traffic time series to efficiently capture fine-grained spatial dependencies. The coarse module adaptively coarsens road networks to region networks and captures coarse-grained spatial dependencies from region networks. Experiments on four real-world traffic datasets demonstrate the STHMLP outperforms state-of-the-art baselines on traffic forecasting.

MA-GCN: A Memory Augmented Graph Convolutional Network for traffic prediction

Traffic forecasting is a particularly challenging and important application direction in the field of spatial–temporal prediction. However, it is difficult for existing models to accurately capture the long time dependence of traffic data and the complex spatial dependence of road network. To solve these two issues, in this work, we propose a new deep learning framework — Memory Augmented Graph Convolutional Network (MA-GCN), which combines graph convolutional network (GCN) with differential neural computer (DNC). In the model, GCN is used to learn the complex road network structure to capture the spatial dependence, while DNC is applied to learn the long-term dynamic changes of traffic data to capture the long time dependence. Based on this, the traffic prediction is implemented, and the experimental evaluation is carried out on two public datasets, PeMSD4 and PeMSD8. The results show that the MA-GCN model is superior to the comparative models on several evaluation metrics.