scholarly journals HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

2016 ◽  
Vol XLI-B2 ◽  
pp. 159-164 ◽  
Author(s):  
B. Anbaroglu ◽  
B. Heydecker ◽  
T. Cheng
Keyword(s):  
Traffic Congestion ◽  
Travel Demand ◽  
Evaluation Criteria ◽  
Road Networks ◽  
Daily Basis ◽  
Detection Methods ◽  
Just In Time ◽  
Urban Road ◽  
Time Estimates ◽  
Congestion Detection

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

scholarly journals HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS

2016 ◽  
Vol XLI-B2 ◽  
pp. 159-164
Author(s):  
B. Anbaroglu ◽  
B. Heydecker ◽  
T. Cheng
Keyword(s):  
Traffic Congestion ◽  
Travel Demand ◽  
Evaluation Criteria ◽  
Road Networks ◽  
Daily Basis ◽  
Detection Methods ◽  
Just In Time ◽  
Urban Road ◽  
Time Estimates ◽  
Congestion Detection

Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revise their existing plans to mitigate its effects. Space-time clusters of high link journey time estimates correspond to non-recurrent congestion events. Existing research, however, has not considered the effect of travel demand on the effectiveness of non-recurrent congestion detection methods. Therefore, this paper investigates how travel demand affects detection of non-recurrent traffic congestion detection on urban road networks. Travel demand has been classified into three categories as low, normal and high. The experiments are carried out on London’s urban road network, and the results demonstrate the necessity to adjust the relative importance of the component evaluation criteria depending on the travel demand level.

scholarly journals Non-recurrent traffic congestion detection on heterogeneous urban road networks

2015 ◽  
Vol 11 (9) ◽  
pp. 754-771 ◽  
Author(s):  
Berk Anbaroğlu ◽  
Tao Cheng ◽  
Benjamin Heydecker
Keyword(s):  
Traffic Congestion ◽  
Road Networks ◽  
Urban Road ◽  
Congestion Detection

scholarly journals A Hybrid Approach Based on Variational Mode Decomposition for Analyzing and Predicting Urban Travel Speed

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Eui-Jin Kim ◽  
Ho-Chul Park ◽  
Seung-Young Kho ◽  
Dong-Kyu Kim
Keyword(s):  
Correlation Analysis ◽  
Hybrid Model ◽  
Travel Demand ◽  
Hybrid Approach ◽  
Road Networks ◽  
Travel Speed ◽  
Variational Mode Decomposition ◽  
Urban Road ◽  
Urban Travel ◽  
Mode Decomposition

Predicting travel speeds on urban road networks is a challenging subject due to its uncertainty stemming from travel demand, geometric condition, traffic signals, and other exogenous factors. This uncertainty appears as nonlinearity, nonstationarity, and volatility in traffic data, and it also creates a spatiotemporal heterogeneity of link travel speed by interacting with neighbor links. In this study, we propose a hybrid model using variational mode decomposition (VMD) to investigate and mitigate the uncertainty of urban travel speeds. The VMD allows the travel speed data to be divided into orthogonal and oscillatory sub-signals, called modes. The regular components are extracted as the low-frequency modes, and the irregular components presenting uncertainty are transformed into a combination of modes, which is more predictable than the original uncertainty. For the prediction, the VMD decomposes the travel speed data into modes, and these modes are predicted and summed to represent the predicted travel speed. The evaluation results on urban road networks show that, the proposed hybrid model outperforms the benchmark models both in the congested and in the overall conditions. The improvement in performance increases significantly over specific link-days, which generally are hard to predict. To explain the significant variance of the prediction performance according to each link and each day, the correlation analysis between the properties of modes and the performance of the model are conducted. The results on correlation analysis show that the more variance of nondaily pattern is explained through the modes, the easier it was to predict the speed. Based on the results, discussions on the interpretation on the correlation analysis and future research are presented.

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 Robustness Analysis of Urban Road Networks from Topological and Operational Perspectives

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Wen-Long Shang ◽  
Yanyan Chen ◽  
Chengcheng Song ◽  
Washington Y. Ochieng
Keyword(s):  
Travel Demand ◽  
Price Of Anarchy ◽  
Robustness Analysis ◽  
Road Networks ◽  
User Equilibrium ◽  
Topological Indices ◽  
Complex Network Theory ◽  
Urban Road ◽  
The Relationship ◽  
Important Nodes

This study comprehensively analyses the robustness of urban road networks through topological indices based on the complex network theory and operational indices based on traffic assignment theory: User Equilibrium (UE), System Optimum (SO), and Price of Anarchy (POA). Analysing topological indices may pin down the most important nodes for URNs from the perspective of connectivity, while more sophisticated operational indices are helpful to examine the importance of nodes for URNs by taking into account link capacity, travel demand, and drivers’ behaviour. The previous way is calculated in a static way, which reduces the computation times and increases the efficiency for quick assessment of the robustness of URNs, while the latter is in a dynamic way, namely, calculating is based on removal of individual nodes, although this way is more likely to capture realistic meanings but consumes huge amount of time. The efforts made in this study try to find the relationship between topological and operational indices so as to assist the assessment of robustness of URNs to local disruptions. Seven realistic urban road networks such as Sioux Falls and Anaheim are used as network examples, and results show that different indices reflect robustness characteristics of urban road networks from different ways, and rank correlations between any two indices are poor although small network such as Sioux Falls have better correlations than others.

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