scholarly journals Analysis of Urban Road Traffic Network Based on Complex Network

2016 ◽  
Vol 137 ◽  
pp. 537-546 ◽  
Author(s):  
Zhao Tian ◽  
Limin Jia ◽  
Honghui Dong ◽  
Fei Su ◽  
Zundong Zhang
Keyword(s):  
Complex Network ◽  
Road Traffic ◽  
Traffic Network ◽  
Urban Road

scholarly journals PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK

Transport ◽  
2018 ◽  
Vol 33 (4) ◽  
pp. 959-970 ◽  
Author(s):  
Tamás Tettamanti ◽  
Alfréd Csikós ◽  
Krisztián Balázs Kis ◽  
Zsolt János Viharos ◽  
István Varga
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Network ◽  
Data Handling ◽  
Urban Road ◽  
Traffic Sensors ◽  
Network Training ◽  
Artificial Neural Network Ann

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

Determination of key nodes in urban road traffic network

2014 ◽  
Author(s):  
Zhao Tian ◽  
Limin Jia ◽  
Honghui Dong ◽  
Zundong Zhang ◽  
Yanfang Yang ◽  
...  
Keyword(s):  
Road Traffic ◽  
Traffic Network ◽  
Urban Road ◽  
Key Nodes

scholarly journals Regional Patch Detection of Road Traffic Network

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Xia Zhu ◽  
Weidong Song ◽  
Lin Gao
Keyword(s):  
Complex Network ◽  
Road Traffic ◽  
Geographic Distance ◽  
Influence Factors ◽  
Impact Factors ◽  
Traffic Network ◽  
The Road ◽  
Key Points ◽  
Path Distance ◽  
Shortest Path Distance

Road traffic network (RTN) structure plays an important role in the field of complex network analysis. In this paper, we propose a regional patch detection method from RTN via community detection of complex network. Firstly, the refined Adapted PageRank algorithm, which combines with the influence factors of the location property weight, the geographic distance weight and the road level weight, is used to calculate the candidate ranking results of key nodes in the RTN. Secondly, the ranking result and the shortest path distance as two significant impact factors are used to select the key points of the RTN, and then the Adapted K-Means algorithm is applied to regional patch detection of the RTN. Finally, based on the experimental data of Zhangwu road traffic network, the analysis results are as follows: Zhangwu is divided into 9 functional structures with key node locations as the core. Regional patch structure is divided according to key points, and the RTN is actually divided into nine small functional communities. Nine functional regional patches constitute a new network structure, maintaining connectivity between the regional patches can improve the overall efficiency of the RTN.

scholarly journals Golden Ratio Genetic Algorithm Based Approach for Modelling and Analysis of the Capacity Expansion of Urban Road Traffic Network

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Lun Zhang ◽  
Meng Zhang ◽  
Wenchen Yang ◽  
Decun Dong
Keyword(s):  
Genetic Algorithm ◽  
Road Network ◽  
Road Traffic ◽  
Golden Ratio ◽  
Hybrid Genetic Algorithm ◽  
Capacity Expansion ◽  
Network Capacity ◽  
Traffic Network ◽  
Effective Measure ◽  
Urban Road

This paper presents the modelling and analysis of the capacity expansion of urban road traffic network (ICURTN). Thebilevel programming model is first employed to model the ICURTN, in which the utility of the entire network is maximized with the optimal utility of travelers’ route choice. Then, an improved hybrid genetic algorithm integrated with golden ratio (HGAGR) is developed to enhance the local search of simple genetic algorithms, and the proposed capacity expansion model is solved by the combination of the HGAGR and the Frank-Wolfe algorithm. Taking the traditional one-way network and bidirectional network as the study case, three numerical calculations are conducted to validate the presented model and algorithm, and the primary influencing factors on extended capacity model are analyzed. The calculation results indicate that capacity expansion of road network is an effective measure to enlarge the capacity of urban road network, especially on the condition of limited construction budget; the average computation time of the HGAGR is 122 seconds, which meets the real-time demand in the evaluation of the road network capacity.

Study on Pinning Control Methods of Urban Road Traffic Network

CICTP 2019 ◽  
2019 ◽  
Author(s):  
Lili Zheng ◽  
Yibin Zhang ◽  
Tongqiang Ding ◽  
Junming Hu ◽  
Xue Xiao ◽  
...  
Keyword(s):  
Road Traffic ◽  
Pinning Control ◽  
Traffic Network ◽  
Control Methods ◽  
Urban Road

Key links identification for urban road traffic network based on temporal-spatial distribution of traffic congestion

2019 ◽  
Vol 33 (25) ◽  
pp. 1950307
Author(s):  
Zhao Tian ◽  
Wei She ◽  
Shuang Li ◽  
You-Wei Wang ◽  
Wei Liu ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Road Traffic ◽  
Computing Time ◽  
Simulated Data ◽  
Time Interval ◽  
Traffic Network ◽  
Urban Road ◽  
Interval Coverage

Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy.

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