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Journal articles on the topic 'Congestion modelling'

Author: Grafiati

Published: 30 June 2021

Last updated: 29 July 2025

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1

Bulckaen, Fabrizio, and Alberto Pench. "Modelling congestion." STUDI ECONOMICI, no. 106 (February 2013): 41–51. http://dx.doi.org/10.3280/ste2012-106003.

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2

Janic, Milan. "Modelling airport congestion charges." Transportation Planning and Technology 28, no. 1 (2005): 1–26. http://dx.doi.org/10.1080/0308106052000340369.

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3

Schneider, Volker, and Rainer Könnecke. "Congestion in Computational Evacuation Modelling." Collective Dynamics 5 (August 12, 2020): A102. http://dx.doi.org/10.17815/cd.2020.102.

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The time-based analysis of egress scenarios is a long-standing and well-established method to evaluate occupant safety. It is based on the necessary condition that the required egress time is smaller than the available egress time. The former is derived by the application of evacuation models, the latter by calculation of smoke and heat spread in the case of a fire incident. In the calculation of required egress time the time-dependent development of occupant density and consequently the emergence of congestion often play a crucial role. There is a demand to evaluate the development of local o

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4

Muslih, Muneer, Ahmed Abduljabbar, and Hasan Joni. "Review of traffic demand management strategies." IOP Conference Series: Earth and Environmental Science 1232, no. 1 (2023): 012055. http://dx.doi.org/10.1088/1755-1315/1232/1/012055.

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Abstract This review article seeks to determine the most effective strategies to reduce traffic congestion which have been implanted in different countries and cities around the world. In this review, previous research work was gathered focusing on approaches for mitigating traffic congestion issues. The paper discovered that there are two ways to control congestion which are through supply-side actions, and demand-side actions, the paper also discovered that traffic modelling and simulation software packages are crucial to decision-makers because of its capability to evaluate and simulate the

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5

Wang, Zongzhi, and Tao Chen. "Pedestrian Evacuation Modelling with Dynamics Congestion Avoidance." Collective Dynamics 5 (August 12, 2020): A87. http://dx.doi.org/10.17815/cd.2020.87.

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With the development of computer technology, pedestrian simulation becomes an efficient method to analyse evacuation efficiency under various scenarios. Some important and common behaviour of pedestrians, congestion detection and avoidance, which is seldom considered in pedestrian simulation complicatedly, are discussed in this paper. A modified cellular automata model considering dynamic congestion detection and avoidance is proposed and applied to simulate two different scenarios to demonstrate the effect of congestion avoidance behaviour, which have a significant improvement on evacuation e

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6

Melo, Rafael C., Julio E. Normey-Rico, and Jean-Marie Farines. "TCP modelling and predictive congestion control." IFAC Proceedings Volumes 42, no. 14 (2009): 72–77. http://dx.doi.org/10.3182/20090901-3-ro-4009.00009.

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7

Humphries, Michael Peter. "Modelling Congestion At Refuse Reception Installations." Waste Management & Research 4, no. 1 (1986): 279–91. http://dx.doi.org/10.1177/0734242x8600400134.

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8

Raheja, Tushar. "Modelling traffic congestion using queuing networks." Sadhana 35, no. 4 (2010): 427–31. http://dx.doi.org/10.1007/s12046-010-0033-x.

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9

Pollett, P. K. "Modelling congestion in closed queueing networks." International Transactions in Operational Research 7, no. 4-5 (2000): 319–30. http://dx.doi.org/10.1111/j.1475-3995.2000.tb00202.x.

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10

Chen, Yongjun, Ming Huang, Kaixuan Song, and Tengfei Wang. "Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering." Journal of Advanced Transportation 2023 (May 20, 2023): 1–12. http://dx.doi.org/10.1155/2023/7175863.

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Accurately predicting short-term congestions in ship traffic flow is important for water traffic safety and intelligent shipping. We propose a method for predicting the traffic flow of ships by applying the whale optimization algorithm to an extreme learning machine. The method considers external environmental uncertainty and complexity of ships navigating in traffic-intensive waters. First, the parameters of ship traffic flow are divided into multiple modal components using variational mode decomposition and extreme learning machine. The machine and the whale optimization algorithm constitute

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11

Han, Qi, Benedict Dellaert, Fred Van Raaij, and Harry Timmermans. "MODELLING STRATEGIC BEHAVIOUR IN ANTICIPATION OF CONGESTION." Transportmetrica 3, no. 2 (2007): 119–38. http://dx.doi.org/10.1080/18128600708685669.

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12

Cavalieri, Salvatore. "Modelling and analysing congestion in KNXnet/IP." Computer Standards & Interfaces 34, no. 3 (2012): 305–13. http://dx.doi.org/10.1016/j.csi.2011.10.005.

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13

Al-Kashoash, Hayder A. A., Fadoua Hassen, Harith Kharrufa, and Andrew H. Kemp. "Analytical modelling of congestion for 6LoWPAN networks." ICT Express 4, no. 4 (2018): 209–15. http://dx.doi.org/10.1016/j.icte.2017.11.001.

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14

Ma, Minghui, Qingfang Yang, Shidong Liang, and Yashi Wang. "A New Coordinated Control Method on the Intersection of Traffic Region." Discrete Dynamics in Nature and Society 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5985840.

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Intersection regions widely exist in road networks as crucial connections, and the congestion effects on traffic in the networks cannot be ignored. In general, a conventional intersection region includes a merging region and more than two ramps, whose congestion brings a serious negative effect on traffic service quality and satisfaction of drivers. Specifically, intersection region congestion is caused by congestion in merging regions. However, current control methods regarding coordination of the variable speed limits and ramp metering in order to solve merging region congestion are insuffic

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15

Pfeifer, Pascal, Jacob Tran, Simon Krahl, and Albert Moser. "Modelling of uncertainty for smart grid congestion management." CIRED - Open Access Proceedings Journal 2020, no. 1 (2020): 509–12. http://dx.doi.org/10.1049/oap-cired.2021.0103.

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16

Garnier, Antoine, Valérie Chavez Demoulin, Ari Pekka Hameri, Tapio Niemi, and Blaise Wasserfallen. "Patient flow congestion - predictive modelling to anticipate bottlenecks." International Journal of Healthcare Technology and Management 15, no. 4 (2016): 352. http://dx.doi.org/10.1504/ijhtm.2016.084137.

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17

Niemi, Tapio, Blaise Wasserfallen, Valérie Chavez Demoulin, Ari Pekka Hameri, and Antoine Garnier. "Patient flow congestion - predictive modelling to anticipate bottlenecks." International Journal of Healthcare Technology and Management 15, no. 4 (2016): 352. http://dx.doi.org/10.1504/ijhtm.2016.10005030.

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18

KRSTEVSKI, Petar. "Regional Transmission Network Modelling for Facilitating Congestion Management." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 6 (2015): 67–70. http://dx.doi.org/10.15199/48.2015.06.11.

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19

Mounier, Hugues, and Georges Bastin. "Compartmental Modelling for Congestion Control in Communication Networks." IFAC Proceedings Volumes 34, no. 6 (2001): 1159–64. http://dx.doi.org/10.1016/s1474-6670(17)35340-5.

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20

Jain, Sourabh, Sukhvir Singh Jain, and Gaurav Jain. "Traffic Congestion Modelling Based on Origin and Destination." Procedia Engineering 187 (2017): 442–50. http://dx.doi.org/10.1016/j.proeng.2017.04.398.

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21

Rao, Kant, and William L. Grenoble. "Modelling the Effects of Traffic Congestion on JIT." International Journal of Physical Distribution & Logistics Management 21, no. 2 (1991): 3–9. http://dx.doi.org/10.1108/09600039110005178.

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22

YAR, ASFAND-E., I. U. AWAN, and M. E. WOODWARD. "PERFORMANCE MODELLING OF TRAFFIC CONGESTION IN WIRELESS NETWORKS." Journal of Interconnection Networks 07, no. 01 (2006): 163–77. http://dx.doi.org/10.1142/s021926590600165x.

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Evolution in Wireless Technologies and Networks imposes a greater need for network support as current congestion control and avoidance techniques are mainly designed for wired networks. The current performance evaluation techniques proposed for wireless networks are not able to achieve optimal performance to guarantee desired Quality of Service (QoS) standards. Thus, the new schemes such as Active Queue Management (AQM) are needed to be adaptive to dynamic wireless networks and bursty traffic conditions to help in avoiding severe performance degradation in wireless environment. Thus, in this p

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23

Akopov, A. S., and L. A. Beklaryan. "Agent-Based Modelling of Interacting Unmanned Vehicles Dynamics with the FLAME GPU." Programmnaya Ingeneria 14, no. 3 (2023): 110–22. http://dx.doi.org/10.17587/prin.14.110-122.

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The article presents an approach to modelling traffic flows based on the intelligent analysis of unmanned ground vehicles (UGVs) dynamics within of which a novel decision-making system on maneuvering for agent-UGVs is designed. The proposed system uses clustering methods for an estimation of a traffic congestion density on alternative routes of the digital road network (DRN) to detour problem areas and minimisе risks of traffic accidents appearing. For the first time, the spatial dynamics of interacting UGVs is modelled with complex schemes for constructing the DRNs, including multiple interse

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24

Gao, Ge, and Jun Hu. "Optimal Tradable Credits Scheme and Congestion Pricing with the Efficiency Analysis to Congestion." Discrete Dynamics in Nature and Society 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/801979.

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We allow for three traffic scenarios: the tradable credits scheme, congestion pricing, and no traffic measure. The utility functions of different modes (car, bus, and bicycle) are developed by considering the income’s impact on travelers’ behaviors. Their purpose is to analyze the demand distribution of different modes. A social optimization model is built aiming at maximizing the social welfare. The optimal tradable credits scheme (distribution of credits, credits charging, and the credit price), congestion pricing fees, bus frequency, and bus fare are obtained by solving the model. Mode choi

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25

Adaramola, O. J., and J. R. Olasina. "Machine Learning-Driven Congestion Prediction in Mobile Ad-Hoc Networks Through Modelling Approaches." International Journal of Scientific Research in Computer Science and Engineering 13, no. 2 (2025): 30–38. https://doi.org/10.26438/ijsrcse.v13i2.666.

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Mobile Ad Hoc Networks (MANETs) operate autonomously through decentralized configurations for military as well as emergency and academic applications. The adaptable network structure and unstable nature of MANETs result in major traffic jam occurrences when network activity is at the peak. This research studies the congestion issue of MANETs by implementing network simulation with Machine Learning analytics to identify and control traffic congestion effectively. The investigation employed OPNET 14.5v to simulate office scenarios that contained five, ten and fifteen mobile nodes to study conges

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26

Faizan, Afroz, and Bramjeet Singh Er. "Congestion Modelling and Level of Service Assessment of Urban Roads in Mixed Traffic Conditions." International Journal of Innovative Research in Computer Science & Technology (IJIRCST) 11, no. 1 (2023): 17–20. https://doi.org/10.5281/zenodo.7660909.

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In urban locations around the world, traffic congestion is a problem. Congestion has increased as a result of inadequate public transportation, stalled development of the road infrastructure, and increased use of private transportation by the public to meet the demand for travel. In developing nations like India, the issue has become more complex due to heterogeneous traffic on subpar road and control conditions. Travel delays and greater vehicle emissions are a result of traffic congestion. In developed nations, studies have been done to quantify congestion in order to assess the current cond

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27

Lipovac, Vlatko, Vedran Batoš, and Boris Nemšić. "Testing TCP Traffic Congestion by Distributed Protocol Analysis and Statistical Modelling." PROMET - Traffic&Transportation 21, no. 4 (2012): 259–68. http://dx.doi.org/10.7307/ptt.v21i4.235.

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In this paper, a solution is proposed for testing TCP congestion window process in a real-life network situation during stationary time intervals. With this respect, the architecture of hardware and expert-system-based distributed protocol analysis is presented that we used for data acquisition and testing, conducted on a major network with live traffic (Electronic Financial Transactions data transfer), as well as the appropriate algorithm for estimating the actual congestion window size from the measured data that mainly included decoding with precise time-stamps (100ns resolution locally and

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28

Neuts, Marcel F. "Proc symp on congestion theory." Communications in Statistics. Stochastic Models 6, no. 3 (1990): 445–70. http://dx.doi.org/10.1080/15326349908807156.

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29

Qi, Weiwei, Yulong Pei, Mo Song, and Yiming Bie. "Pattern Analysis of Driver’s “Pressure-State-Response” in Traffic Congestion." Discrete Dynamics in Nature and Society 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/853845.

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Traffic congestion, which has a direct impact on the driver’s mood and action, has become a serious problem in rush hours in most cities of China. Currently, the study about driver’s mood and action in traffic congestion is scarce, so it is necessary to work on the relationship among driver’s mood and action and traffic congestion. And the PSR (pressure-state-response) framework is established to describe that relationship. Here, PSR framework is composed of a three-level logical structure, which is composed of traffic congestion environment, drivers’ physiology change, and drivers’ behavior c

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30

Wang, Shu-wei, Li-shan Sun, Jian Rong, and Zi-fan Yang. "Transit Station Congestion Index Research Based on Pedestrian Simulation and Gray Clustering Evaluation." Discrete Dynamics in Nature and Society 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/891048.

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A congestion phenomenon in a transit station could lead to low transfer efficiency as well as a hidden danger. Effective management of congestion phenomenon shall help to reduce the efficiency decline and danger risk. However, due to the difficulty in acquiring microcosmic pedestrian density, existing researches lack quantitative indicators to reflect congestion degree. This paper aims to solve this problem. Firstly, platform, stair, transfer tunnel, auto fare collection (AFC) machine, and security check machine were chosen as key traffic facilities through large amounts of field investigation

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31

Nguyen, Duc-Binh, Chyi-Ren Dow, and Shiow-Fen Hwang. "An Efficient Traffic Congestion Monitoring System on Internet of Vehicles." Wireless Communications and Mobile Computing 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/9136813.

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Existing intelligent transport systems (ITS) do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles (IoV) environments. This paper proposes a traffic congestion monitoring system, which includes data collection, segmented structure establishment, traffic-flow modelling, local segment traffic congestion prediction, and origin-destination traffic congestion service for drivers. Macroscopic model-based traffic-flow factors were formalized on the basis of the analysis results. Fuzzy rules-based local segmen

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32

Suryani, E., R. A. Hendrawan, P. F. EAdipraja, A. Wibisono, and L. P. Dewi. "Modelling Reliability of Transportation Systems to Reduce Traffic Congestion." Journal of Physics: Conference Series 1196 (March 2019): 012029. http://dx.doi.org/10.1088/1742-6596/1196/1/012029.

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33

Shorten, Robert, Chris King, Fabian Wirth, and Douglas Leith. "Modelling TCP congestion control dynamics in drop-tail environments." Automatica 43, no. 3 (2007): 441–49. http://dx.doi.org/10.1016/j.automatica.2006.07.026.

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34

Tsanakas, Nikolaos, Joakim Ekström, and Johan Olstam. "Estimating Emissions from Static Traffic Models: Problems and Solutions." Journal of Advanced Transportation 2020 (February 1, 2020): 1–17. http://dx.doi.org/10.1155/2020/5401792.

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In large urban areas, the estimation of vehicular traffic emissions is commonly based on the outputs of transport planning models, such as Static Traffic Assignment (STA) models. However, such models, being used in a strategic context, imply some important simplifications regarding the variation of traffic conditions, and their outputs are heavily aggregated in time. In addition, dynamic traffic flow phenomena, such as queue spillback, cannot be captured, leading to inaccurate modelling of congestion. As congestion is strongly correlated with increased emission rates, using STA may lead to unr

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35

Kanyepe, James. "The Nexus between Residential Density, Travel Behavior and Traffic Congestion in Developing Metropolitans: A Case Study of Harare, Zimbabwe." Journal of Contemporary Urban Affairs 7, no. 1 (2023): 103–17. http://dx.doi.org/10.25034/ijcua.2023.v7n1-7.

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There is a noticeable dearth of empirical studies on the mediating role of travel behaviour on the influence of residential density on traffic congestion in developing countries, particularly in Zimbabwe. To address this gap, this study examined the effect of residential density on traffic congestion and delved into the potential mediating role of travel behaviour in this relationship. This study used data from a sample of 384 households in the Harare metropolitan province of Zimbabwe. Structural equation modelling was used to test the research hypotheses. The findings indicate that residentia

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36

Miranda, G., H. P. Luna, R. S. de Camargo, and L. R. Pinto. "Tree network design avoiding congestion." Applied Mathematical Modelling 35, no. 9 (2011): 4175–88. http://dx.doi.org/10.1016/j.apm.2011.02.046.

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37

Arey, M. Jimi, and Brian W. Baetz. "Simulation modelling for the sizing of solid waste receiving facilities." Canadian Journal of Civil Engineering 20, no. 2 (1993): 220–27. http://dx.doi.org/10.1139/l93-027.

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Solid waste receiving facilities such as recycling centres, transfer stations, incinerators, and landfills may experience significant traffic congestion and waste storage buildup problems due to the random arrival patterns of waste delivery vehicles. The provision of an increased number of service channels and increased waste storage area will reduce the congestion and buildup problems and the associated costs, but will also incur increased capital and operating costs. A simulation modelling approach for evaluating system performance is described in this paper, and the modelling approach is ap

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38

Bakhirev, Igor A., Oleg V. Vassiliev, Sergey V. Ovchinnikov, and Anton A. Chernyshov. "Study of traffic congestion in the city of Moscow based on an analysis of reasons and effects of induced demand." Journal «Izvestiya vuzov. Investitsiyi. Stroyitelstvo. Nedvizhimost» 10, no. 4 (2020): 634–43. http://dx.doi.org/10.21285/2227-2917-2020-4-634-643.

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This paper presents the results of the research project “Modelling of traffic flows along the street-road network of urban and regional significance of the city of Moscow with the identification of areas operating in overload mode”. The project was realized using the method of static macro-modelling of traffic flows, the EMME software package based on the initial data for the period from 2015 to 2019, as well as field surveys of traffic flows. On the basis of the obtained results, the congestion threshold for various sections of the road network was calculated along with the proportion of cong

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39

Dimitriou, Loukas, and Antony Stathopoulos. "Modelling Dynamic Urban Road Networks Performance under Congestion Pricing Strategies." IFAC Proceedings Volumes 41, no. 2 (2008): 13079–84. http://dx.doi.org/10.3182/20080706-5-kr-1001.02211.

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40

Fiems, Dieter, and Balakrishna J. Prabhu. "Macroscopic modelling and analysis of flows during rush-hour congestion." Performance Evaluation 149-150 (September 2021): 102218. http://dx.doi.org/10.1016/j.peva.2021.102218.

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41

Purnawansyah, Purnawansyah. "Congestion Predictive Modelling on Network Dataset Using Ensemble Deep Learning." Journal of Applied Data Sciences 5, no. 4 (2024): 1597–613. https://doi.org/10.47738/jads.v5i4.333.

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42

Chatté, F., B. Ducourthial, D. Nace, and S. I. Niculescu. "Fluid modelling of packet switching networks: perspectives for congestion control." International Journal of Systems Science 34, no. 10-11 (2003): 585–97. http://dx.doi.org/10.1080/00207720310001614899.

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43

Wallace, T. Daniel, and Abdallah Shami. "Concurrent Multipath Transfer Using SCTP: Modelling and Congestion Window Management." IEEE Transactions on Mobile Computing 13, no. 11 (2014): 2510–23. http://dx.doi.org/10.1109/tmc.2014.2307330.

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44

Phan, Duc C., and Long T. Truong. "Traffic Congestion and Safety: Mixed Effects on Total and Fatal Crashes." Sustainability 16, no. 20 (2024): 8911. http://dx.doi.org/10.3390/su16208911.

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This paper examines the effects of traffic congestion on total crashes, fatal or serious injury (FSI) crashes, and fatal-only crashes in peak periods using a zone-level safety analysis in Greater Melbourne. Bayesian mixed-effect negative binomial models are employed to investigate the relationship between a congestion index and the frequency of total and FSI crashes. In addition, Bayesian mixed-effect binary logistic models are adopted to explore the association between the congestion index and the likelihood of having fatal crashes in Statistical Area Level 2 (SA2) zones. Modelling results in

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45

Ozerova, Olha, Sergiy Lytvynenko, Roman Sushchenko, Yaroslav Zapara, Petro Ovchar, and Yuliana Lavrushchenko. "Factors Influencing the Modelling of Transport Flow Dynamics in Cities." Proceedings of the Bulgarian Academy of Sciences 75, no. 2 (2022): 259–65. http://dx.doi.org/10.7546/crabs.2022.02.11.

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The article defines the regularities in the influence on the traffic flows dynamics in cities using mathematical modelling, taking into account the historical features of this process, as well as the most relevant issues. In the queuing theory obstruction of free movement is considered a service, and the duration caused by this delay is called service duration, which is not applicable to the delay of transport at intersections. By means of mathematical modelling, the dependence of the influence of increasing the congestion degree on the increasein vehicle delays was determined. Results of stud

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46

Soriano, Nicole Beatrice, Adrian Benedict Villanueva, and Erika Mae Santiago. "Optimizing Urban Transportation Systems Using Simulation and Modelling." International Journal of Technology and Modeling 2, no. 1 (2022): 31–47. https://doi.org/10.63876/ijtm.v2i1.120.

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The rapid growth of urban populations has intensified the pressure on transportation infrastructure, leading to challenges such as traffic congestion, increased travel time, pollution, and reduced overall mobility. To address these issues, the use of simulation and modelling has emerged as a powerful approach in understanding and optimizing urban transportation systems. This study investigates how various simulation techniques—such as discrete-event simulation, agent-based modelling, and system dynamics—can be applied to analyze traffic patterns, test policy interventions, and predict system b

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47

FUKUDA, KENSUKE, HIDEKI TAKAYASU, and MISAKO TAKAYASU. "SPATIAL AND TEMPORAL BEHAVIOR OF CONGESTION IN INTERNET TRAFFIC." Fractals 07, no. 01 (1999): 23–31. http://dx.doi.org/10.1142/s0218348x99000049.

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We have developed an approach which allows the determination of relative congestion levels on nodes of TCP/IP computer network by observing test packet round trip time sequences for a series of routers located along the same path. We have also demonstrated the existence of strong spatial correlation of congestion levels. Propagation of congestion between neighboring routers can be observed directly. With increasing network load, distribution of congestion duration times changes from exponential to a distribution falling off slower than power law, via power law distribution. We thus conclude th

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48

Ata, Ayesha, Muhammad Adnan Khan, Sagheer Abbas, Gulzar Ahmad, and Areej Fatima. "MODELLING SMART ROAD TRAFFIC CONGESTION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES." Neural Network World 29, no. 2 (2019): 99–110. http://dx.doi.org/10.14311/nnw.2019.29.008.

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49

de Lange, Jordi, Sander Rieken, Laura Scarabosio, and Gabriel J. Lord. "Preventing congestion management by modelling cable temperatures: a real-world case." IET Conference Proceedings 2024, no. 5 (2025): 830–35. https://doi.org/10.1049/icp.2024.2007.

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50

Mameri, Soumia, Yacine Kissoum, and Mohammed Redjimi. "Mobile agents-based modelling for the vehicular network congestion problem resolution." International Journal of Simulation and Process Modelling 21, no. 1 (2023): 1–13. http://dx.doi.org/10.1504/ijspm.2023.139800.

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