scholarly journals Discussion on the Sustainable Development Pattern of Road Network Structure of Cities with a Population of 1-2 Million in China

2021 ◽  
Vol 237 ◽  
pp. 04018
Author(s):  
Zhengmin Wen ◽  
Zhenqiang Li ◽  
Wenshuo Luo ◽  
Yuxin Fu ◽  
Juan Quan ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Network Structure ◽  
Road Network ◽  
Urban Traffic ◽  
Outer Ring ◽  
Development Pattern ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Advantages And Disadvantages

The urban road network system is the main carrier of urban traffic. The constraint factor that urban road network has on traffic leads to major urban traffic problems. In this paper, a questionnaire survey and the Delphi method are determined to determine the case cities with a population of 1-2 million at home and abroad. Literature research and comparative, analytical, and inductive research methods are used to compare, analyse, and evaluate the advantages and disadvantages of the road network structure of the case cities, to summarize the development law of the urban road network, and to propose a sustainable development structure model for each development stage of urban road network: the mode made of ring freeway + trunk street (the embryonic stage), the mode made of ring freeway + expressway + trunk street (the incubation stage), the mode made of outer ring freeway + outer ring expressway and radial expressway + trunk street (the mature stage). The innovative point of this paper is to put forward a sustainable development pattern for the road network structure of cities with a population of 1-2 million in China.

scholarly journals GIS-Based Urban Road Network Accessibility Modeling Using MLR, ANN and ANFIS Methods

2021 ◽  
Vol 22 (1) ◽  
pp. 15-28
Author(s):  
K. Sai Sahitya ◽  
Csrk Prasad
Keyword(s):  
Network Structure ◽  
Road Network ◽  
Structural Parameters ◽  
Transportation Network ◽  
Sustainable Transportation ◽  
Geographical Information ◽  
Urban Road Network ◽  
Inference System ◽  
Urban Road ◽  
The Road

Abstract A sustainable transportation system is possible only through an efficient evaluation of transportation network performance. The efficiency of the transport network structure is analyzed in terms of its connectivity, accessibility, network development, and spatial pattern. This study primarily aims to propose a methodology for modeling the accessibility based on the structural parameters of the urban road network. Accessibility depends on the arrangement of the urban road network structure. The influence of the structural parameters on the accessibility is modeled using Multiple Linear Regression (MLR) analysis. The study attempts to introduce two methods of Artificial Intelligence (AI) namely Artificial Neural Networks (ANN) and Adaptive network-based neuro-fuzzy inference system (ANFIS) in modeling the urban road network accessibility. The study also focuses on comparing the results obtained from MLR, ANN and ANFIS modeling techniques in predicting the accessibility. The results of the study present that the structural parameters of the road network have a considerable impact on accessibility. ANFIS method has shown the best performance in modeling the road network accessibility with a MAPE value of 0.287%. The present study adopted Geographical Information Systems (GIS) to quantify, extract and analyze different features of the urban transportation network structure. The combination of GIS, ANN, and ANFIS help in improved decision-making. The results of the study may be used by transportation planning authorities to implement better planning practices in order to improve accessibility.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals Impact of Road Network Topology on Public Transportation Development

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Minzhi Chen ◽  
Fan Wu ◽  
Min Yin ◽  
Jiangang Xu
Keyword(s):  
Public Transportation ◽  
Road Network ◽  
Road Networks ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Layout Structure ◽  
Per Capita ◽  
Transportation Development ◽  
The Impact

Planning of road networks is fundamental for public transportation. The impact of road network density on public transportation has been extensively studied, but few studies in this regard involved evaluation indicators for connectivity and layout of road networks. With 29 cities in China as the study cases, this paper quantifies the layout structure of the road network based on the network’s betweenness centralization and establishes a multivariate linear regression model to perform regression of the logarithm of the frequency of per capita public transportation on betweenness centralization. It is found in the present work that there is a significant correlation between the layout structure of an urban road network and the residents’ utilization degree of public transportation. A greater betweenness centralization of the urban road network, namely a more centralized road network, means a higher frequency of per capita public transportation of urban residents and a higher degree of the residents’ utilization of public transportation. In the development of public transportation, centralized and axial-shaped layout structures of road networks can be promoted to improve the utilization of public transportation.

Spatial Analysis Based Urban Road Network Partition Method for Macroscopic Evaluation

2011 ◽  
Vol 97-98 ◽  
pp. 512-517
Author(s):  
Wen Jie Zou ◽  
Jian Cheng Weng ◽  
Jian Rong ◽  
Wei Zhou
Keyword(s):  
Spatial Analysis ◽  
Road Network ◽  
Urban Road Network ◽  
Network Partition ◽  
Urban Road ◽  
The Road ◽  
Network Operation ◽  
Partition Method ◽  
Operation Status

In order to improve the reliability of urban road network operation evaluation, the road network regional Partition methods were launched in this paper. The geographic grid was introduced first, and a 4-level road network model was defined. Then, the spatial analysis based urban road network division method was proposed by analyzing the characteristics of road network operation. This method can reflect the influence between adjacent regional units, and improve the reliability of urban road network division. Finally, this research took a certain area in Beijing as a case study, and divided the road network as several regional units. Macroscopic evaluation result shows that it is effective for scientifically describing the road network operation status.

PEAK HOUR FACTOR AT URBAN ROAD NETWORK SYSTEM WITH FIXED HOURLY INTERVAL AND MOVING HOURLY INTERVAL (A CASE STUDY AT URBAN ROAD IN CIMAHI CITY)

2020 ◽  
Vol 6 (01) ◽  
pp. 29
Author(s):  
Hendra Hendrawan
Keyword(s):  
Urban Areas ◽  
Road Network ◽  
Resource Constraints ◽  
Important Variable ◽  
Network System ◽  
Urban Road Network ◽  
Hourly Interval ◽  
Urban Road ◽  
The Road

The Peak Hour Factor (PHF) is an important variable for determining road capacity. The value of PHF will vary greatly depending on location characteristics and classification of road functions. This study aims to obtain the estimated value of PHF in the urban road network system with variations in the classification of functions and types of roads. In addition this study also aims to obtain a method of approaching the PHF value near to fluctuations in traffic flow which has limited resources for surveys based on the duration specified in the traffic survey guidelines in Indonesia. The method used is descriptive statistical analysis and parametric test using Independent T sample test. The PHF is calculated based on Fixed Hourly Interval and Moving Hourly Interval and their inverse. The results of the study show the value of PHF in the road network system in urban areas for variations function and type of road that is in the range of 0.79 to 0.98 with an average of 0.91. Other findings show that the inverse method of Moving Hourly Interval can be used as an approach to obtain the PHF value under conditions of resource constraints

Influence Models of Urban Road Network Operation Performance Base on Spatial Autocorrelation Theory

2012 ◽  
Vol 253-255 ◽  
pp. 1922-1929
Author(s):  
Jian Cheng Weng ◽  
Wen Jie Zou ◽  
Jian Rong
Keyword(s):  
Spatial Autocorrelation ◽  
Road Network ◽  
Road Networks ◽  
Operation Performance ◽  
Urban Road Network ◽  
Spatial Characteristics ◽  
Urban Road ◽  
The Road ◽  
Network Operation ◽  
The Impact

In order to better identify the spatial influence between adjacent parts of road networks, the paper introduces the spatial autocorrelation theory in evaluating the operation performance of urban road networks. The research proposes several spatial correlation validation indicators to verify the spatial characteristics among the road networks. Based on the analysis of spatial characteristics, the relationship between operation performance and influencing factors under the impact of spatial effect is examined. Furthermore, a spatial autocorrelation based influence models at three traffic flow levels is developed by using the data from a partial urban road network in Beijing. The model analysis shows that the spatial autocorrelation model is more effective in analyzing the urban road network operation performance under the influence of various factors. This model will be beneficial in identifying traffic network problems and improving traffic operations of the urban road network.

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

2019 ◽  
Vol 16 (13) ◽  
pp. 2318 ◽  
Author(s):  
Ge Shi ◽  
Jie Shan ◽  
Liang Ding ◽  
Peng Ye ◽  
Yang Li ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Road Network ◽  
Urban Expansion ◽  
Eastern China ◽  
Estimation Model ◽  
Urban Road Network ◽  
Network Expansion ◽  
Urban Road ◽  
The Road

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.

scholarly journals Urban Road Network Emergency: An Integrative Vulnerability Identification Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Huaikun Xiang
Keyword(s):  
Network Traffic ◽  
Road Network ◽  
Road Networks ◽  
Likelihood Method ◽  
Survey Method ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Identification Method ◽  
The Impact

The vulnerability of an urban road network is affected by many factors, such as internal road network layout, network structure strength, and external destructive events, which have great uncertainty and complexity. Thus, there is still no unified and definite vulnerability analysis scheme available to cities. This paper proposes an integrative vulnerability identification method for urban road networks, which mainly relates to the vulnerability connotation and characteristics analysis of urban road networks during emergency, and vulnerability comprehensive evaluation indices design based on urban road network connectivity, traffic efficiency and performance, and an empirical study on a vulnerability identification method of an urban road network. In the empirical case, a real road network and traffic operation data were used from Science and Technology Park of Shenzhen City, China. In the context of one certain emergency scenario, the stated preference survey method and maximum likelihood method are used to solve the road users’ random travel choice behavior parameters; subsequently, based on the traffic equilibrium distribution prediction, the traffic vulnerability identification methods of the road network in this region were verified before and after the emergency. The method presented here not only considers the impact of network topology changes on road network traffic function during emergency but also considers the impact of dynamic changes in road network traffic demand on vulnerability; therefore, it is closer to the actual distribution of urban road network traffic vulnerability.

Road network link prediction model based on subgraph pattern

2020 ◽  
Vol 31 (06) ◽  
pp. 2050083
Author(s):  
Bin Wang ◽  
Xiaoxia Pan ◽  
Yilei Li ◽  
Jinfang Sheng ◽  
Jun Long ◽  
...  
Keyword(s):  
Prediction Model ◽  
Link Prediction ◽  
Prediction Accuracy ◽  
Road Network ◽  
Network Embedding ◽  
Urban Road Network ◽  
Urban Road ◽  
The Road ◽  
Network Link ◽  
Subgraph Pattern

Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

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