scholarly journals Recurrent and Non-recurrent Congestion Based Gridlock Detection on Chula-SSS Urban Road Network

10.29007/cxkb ◽  
2019 ◽  
Author(s):  
Ei Ei Mon ◽  
Hideya Ochiai ◽  
Chaiyachet Saivichit ◽  
Chaodit Aswakul
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Road Networks ◽  
Weather Conditions ◽  
Road Maintenance ◽  
Urban Road Network ◽  
School Buses ◽  
Traffic Jam ◽  
Urban Road ◽  
Negative Impacts

Traffic congestion on not only highways but also complex urban road networks has attracted the attention of many researchers. Traffic congestion growing in urban road net- works is an inevitably important problem especially for populated cities during rush hours. A traffic blockage can be realized as the source of traffic congestion, which can propagate to form queues and sometimes a gridlock. Traffic blockages are triggered by complicated factors ranging from temporal and spatial situations. Recurrent congestion is a traffic congestion that occurs during morning and evening rush hours e.g. from school buses and parent vehicles to drive their children to-and-from schools. In addition, unforeseen, unexpected events that can cause as non-recurrent traffic congestion e.g. car breakdowns, accidents, road maintenance, and severe weather conditions, which can disorder normal traffic flows and reduce road capacity. Traffic blockage may spread its negative impacts to neighbouring upstream and downstream links. And that can lead to the formation of congestion gridlock, which further reduce traffic flow efficiency in a complex urban road network. These problems are vital but often tough to resolve in urban road networks. In this paper, the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset has been used with Simulation of Urban Mobility (SUMO) to simulate recurrent and non-recurrent congestion cases. The detection is based on the information from simulated lane area detectors. For non-recurrent case, lanes are closed to simulate the gridlock occurrences. With the morning case of calibrated Chula-SSS dataset, both recurrent and nonrecurrent congestion based gridlock have been studied with upstream and downstream nearby detectors and preliminary results are herein reported upon the gridlock status as detected by using different combinations of traffic jam length and mean speed conditions at both the upstream and downstream detectors of every intersection within the critical looped road segments.

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.

scholarly journals Widespread Traffic Congestion Prediction for Urban Road Network Based on Synergetic Theory

2014 ◽  
Vol 2 (4) ◽  
pp. 366-371 ◽  
Author(s):  
Liangliang Zhang ◽  
Yuanhua Jia ◽  
Zhonghai Niu ◽  
Cheng Liao
Keyword(s):  
Predictive Models ◽  
Traffic Congestion ◽  
Road Network ◽  
Urban Road Network ◽  
Urban Road ◽  
Self Organized ◽  
Congestion Prediction ◽  
Self Organized Criticality ◽  
Synergetic Theory

AbstractThe traffic congestion often occurs in urban road network. When one of the sections becomes congested, it will lead to a series of congestions in other sections. The traffic congestion spreads rapidly until part of road network becomes congestion ultimately. In this case, the paper investigates the mechanism of the traffic congestion in urban road network and points out that subsystems of the traffic congestion always perform completive and cooperative functions in the process of traffic congestion. The process behaves in a manner of self-organized criticality, which can be forecasted. The paper also establishes synergetic predictive models based on self-organized criticality of the synergetic theory. Finally, the paper takes Beijing road network as an example to forecast the widespread traffic congestion. The result shows that the established models are accuracy, and the traffic congestion is featured of self-organized criticality.

Traffic Congestion Identification and Analysis of Urban Road Network Based on Granular Computing

2014 ◽  
Vol 641-642 ◽  
pp. 916-922 ◽  
Author(s):  
Chen Yao ◽  
Jiu Chun Gu ◽  
Qi Yang
Keyword(s):  
Rough Set ◽  
Traffic Congestion ◽  
Evaluation System ◽  
Road Network ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Quotient Space ◽  
Urban Road Network ◽  
Evaluation Indexes ◽  
Urban Road

This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associating with traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μ is larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.

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 A Multi-Criteria Based Approach to Prototyping Urban Road Networks

10.28945/3172 ◽  
2007 ◽  
Author(s):  
Atulya Nagar ◽  
Hissam Tawfik
Keyword(s):  
Road Network ◽  
Road Networks ◽  
Transportation Cost ◽  
Multiple Criteria ◽  
Urban Road Network ◽  
Urban Road ◽  
Spatial Quality

This paper presents an approach for analysing and prototyping urban road network routes based on multiple criteria, such as spatial quality, transportation cost and aesthetics, in order to evaluate the quality of people’s use of road networks. A prototype is developed to analyse routes and accessibility in road layouts from a number of design and user related perspectives. A case study is used to demonstrate the concept of multi-criteria assessment of road networks.

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.

scholarly journals Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1412
Author(s):  
Ei Ei Mon ◽  
Hideya Ochiai ◽  
Chaiyachet Saivichit ◽  
Chaodit Aswakul
Keyword(s):  
Road Network ◽  
Short Term Memory ◽  
Road Networks ◽  
Sample Sizes ◽  
Short Term ◽  
Urban Road Network ◽  
Term Memory ◽  
Urban Road ◽  
Wide Range ◽  
Long Short Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

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