Zeitschriftenartikel: „Congestion modelling“

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 occupant density and jam situations independent of the above time-based criterion. This is for example reflected in national guidelines and standards. It is however difficult to obtain general valid evaluation criteria for congestion due to the multitude of influencing parameter and the highly situation-dependent nature of the accompanying boundary conditions. In addition, prediction of localization and duration of congestion may differ from model to model if applied to equal scenarios. Furthermore, close inspection reveals the difficulty to define proper terms for a quantitative definition of congestion. This issue is further analysed in this paper based on three case studies.

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4

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 efficiency. The accuracy and efficacy of this model is verified through the comparison result which is conducted through commercial software, Pathfinder. The modified model shows that with the consideration of congestion avoidance behaviour properly, the evacuation efficiency is improved approximately 40% than the model proposed by this paper, without the consideration of congestion avoidance behaviour.

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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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6

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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7

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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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

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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10

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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11

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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12

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 insufficient to handle congestion occurring in the intersection region. Therefore, a new method of coordinated control is proposed in this paper based on mainline control using the variable speed limits and coordinated ramp metering. The coordinated control method is formulated considering the degree of crowdedness indexes and implemented based on the queue length, with the goal to relieve congestion in conventional intersection regions caused by merging region congestion. The coordinated control method is further evaluated based on an actual traffic network. The results show that the proposed method is effective in terms of improving traffic service quality and relieving intersection region congestion.

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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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14

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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15

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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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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17

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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18

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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19

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 paper we developed and validated a novel approximate analytical performance model of a multiple threshold Random Early Detection (RED) congestion control mechanism based on the principle of Maximum Entropy (ME). It can be employed at the wireless gateways/base stations to regulate the buffer management and bandwidth allocation. Closed form expressions for the state and blocking probabilities have also been characterized. Numerical examples have been presented for aggregate and marginal QoS measures, which show the credibility of the ME solution and its validation against simulation.

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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 choice behavior under the tradable credits scheme is also studied. Numerical examples are presented to demonstrate the model’s availability and explore the effects of the three schemes on traffic system’s performance. Results show congestion pricing would earn more social welfare than the other traffic measures. However, tradable credits scheme will give travelers more consumer surplus than congestion pricing. Travelers’ consumer surplus with congestion pricing is the minimum, which injures the travelers’ benefits. Tradable credits scheme is considered the best scenario by comparing the three scenarios’ efficiency.

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21

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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22

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 1ms with GPS clock distribution) and expert-system comments, resulting from the appropriate processing of the network data, accordingly filtered prior to arriving to the special-hardware-based capture buffer. In addition, the paper presents the statistical analysis model that we developed for the evaluation whether the data belonged to the specific (in this case, normal) cumulative distribution function, or whether two data sets exhibit the same statistical distribution - the conditio sine qua non for a TCP-stable interval. Having identified such stationary intervals, it was found that the measured-data-based congestion window values exhibited very good fitting (with satisfactory statistical significance) to the truncated normal distribution. Finally, an appropriate model was developed and applied, for estimating the relevant parameters of the congestion window distribution: its mean value and the variance. KEY WORDS: protocol analysis, TCP-IP, testing, traffic congestion, statistical analysis, parameter estimation

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23

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 change. Based on the PSR framework, various styles of drivers have been chosen to drive on the congested roads, and then traffic stream state, drivers’ physiology, and behavior characters have been measured via the appropriative equipment. Further, driver’s visual characteristics and lane changing characteristics are analyzed to determine the parameters of PSR framework. According to the PSR framework, the changing law of drivers’ characteristics in traffic congestion has been obtained to offer necessary logical space and systematic framework for traffic congestion management.

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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. Key facilities could be used to reflect the passenger density of a whole station. Secondly, the pedestrian density change law of each key traffic facility was analyzed using pedestrian simulation, and the load degree calculating method of each facility was defined, respectively, afterwards. Taking pedestrian density as basic data and gray clustering evaluation as algorithm, an index called Transit Station Congestion Index (TSCI) was constructed to reflect the congestion degree of transit stations. Finally, an evaluation demonstration was carried out with five typical transit transfer stations in Beijing, and the evaluation results show that TSCI can objectively reflect the congestion degree of transit stations.

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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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26

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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27

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 segment traffic congestion prediction was performed to determine the traffic congestion state. To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine-Hugoniot condition and an origin-destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time.

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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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29

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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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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31

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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32

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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33

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 applied to representative Canadian waste receiving facilities. A minimum cost approach is implemented to determine the preferred number of service channels for one application example. The sizing methodology developed should be useful to waste management engineers and planners for both the design of planned solid waste receiving facilities and the operational analysis of existing facilities for a range of projected conditions. Key words: waste management, facility sizing, simulation modelling.

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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 unreliable emission estimations. The first objective of this paper is to identify the errors that STA models introduce into an emission estimation. Then, considering the type and the nature of the errors, our aim is to suggest potential solutions. According to our findings, the main errors are related to STA inability of accurately modelling the level and the location of congestion. For this reason, we suggest and evaluate the postprocessing of STA outputs through quasidynamic network loading. Then, we evaluate our suggested approach using the HBEFA emission factors and a 19 km long motorway segment in Stockholm as a case study. Although, in terms of total emissions, the differences compared to the simple static case are not so vital, the postprocessor performs better regarding the spatial distribution of emissions. Considering the location-specific effects of traffic emissions, the latter may lead to substantial improvements in applications of emission modelling such as dispersion, air quality, and exposure modelling.

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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 that direct observations of network congestion agree with predictions of a model based on contact process.

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Nguyen-Phuoc, Duy Q., Graham Currie, Chris De Gruyter, Inhi Kim, and William Young. "Modelling the net traffic congestion impact of bus operations in Melbourne." Transportation Research Part A: Policy and Practice 117 (November 2018): 1–12. http://dx.doi.org/10.1016/j.tra.2018.08.005.

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37

Essadeq, Imane, Eleonore Dubail, and Eric Jeanniere. "Modelling Passenger Congestion in Transit System –Benchmark and Three Case Studies." Transportation Research Procedia 14 (2016): 1792–801. http://dx.doi.org/10.1016/j.trpro.2016.05.145.

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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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Otuoze, Suleiman Hassan, Dexter V. L. Hunt, and Ian Jefferson. "Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)." Sustainability 13, no. 3 (2021): 1371. http://dx.doi.org/10.3390/su13031371.

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Congestion has become part of everyday urban life, and resilience is very crucial to traffic vulnerability and sustainable urban mobility. This research employed a neural network as an adaptive artificially-intelligent application to study the complex domains of traffic vulnerability and the resilience of the transport system in Nigerian cities (Kano and Lagos). The input criteria to train and check the models for the neural resilience network are the demographic variables, the geospatial data, traffic parameters, and infrastructure inventories. The training targets were set as congestion elements (traffic volume, saturation degree and congestion indices), which are in line with the relevant design standards obtained from the literature. A multi-layer feed-forward and back-propagation model involving input–output and curve fitting (nftool) in the MATLAB R2019b software wizard was used. Three algorithms—including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and a Scaled Conjugate Gradient (SCG)—were selected for the simulation. LM converged easily with the Mean Squared Error (MSE) (2.675 × 10−3) and regression coefficient (R) (1.0) for the city of Lagos. Furthermore, the LM algorithm provided a better fit for the model training and for the overall validation of the Kano network analysis with MSE (4.424 × 10−1) and R (1.0). The model offers a modern method for the simulation of urban traffic and discrete congestion prediction.

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Yao, Qi, Lu Xu, and Qin Zhang. "Container Slot Allocation for Time-Sensitive Cargo in Maritime Transportation: A One-Phase Model with consideration of Port Congestion." Discrete Dynamics in Nature and Society 2021 (February 12, 2021): 1–11. http://dx.doi.org/10.1155/2021/6622291.

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This paper studies a container slot allocation problem with dynamic pricing for time-sensitive cargo considering port congestion. Time-sensitive cargo calls for express delivery as soon as possible, and hence a new pricing pattern is proposed considering port congestion. To solve the slot allocation with dynamic pricing issue, a one-phase allocation model which is from different points of view on slot allocation strategy is proposed to formulate this problem. Finally, numerical examples are carried out to test the applicability of the proposed model and solution algorithm.

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Gómez, Manuel A. "Public Spending in a Model of Endogenous Growth with Habit Formation." Discrete Dynamics in Nature and Society 2010 (2010): 1–22. http://dx.doi.org/10.1155/2010/514329.

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This paper introduces habit-forming preferences in a Barro-type endogenous growth model with productive public services. Government expenditure, which may be subject to congestion, is financed by distortionary income taxation. Different from the standard time-separable model, the presence of habits makes the economy feature transitional dynamics, which are solved in closed form. Setting the income tax so as to equate the elasticity of public services in production is shown to maximize both long-run growth and welfare as in the standard model. This second-best solution coincides with the first-best outcome only in the presence of proportional congestion.

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Stan, Ioan, Vasile Suciu, and Rodica Potolea. "Scalable Data Model for Traffic Congestion Avoidance in a Vehicle to Cloud Infrastructure." Sensors 21, no. 15 (2021): 5074. http://dx.doi.org/10.3390/s21155074.

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Traffic congestion experience in urban areas has negative impact on our daily lives by consuming our time and resources. Intelligent Transportation Systems can provide the necessary infrastructure to mitigate such challenges. In this paper, we propose a novel and scalable solution to model, store and control traffic data based on range query data structures (K-ary Interval Tree and K-ary Entry Point Tree) which allows data representation and handling in a way that better predicts and avoids traffic congestion in urban areas. Our experiments, validation scenarios, performance measurements and solution assessment were done on Brooklyn, New York traffic congestion simulation scenario and shown the validity, reliability, performance and scalability of the proposed solution in terms of time spent in traffic, run-time and memory usage. The experiments on the proposed data structures simulated up to 10,000 vehicles having microseconds time to access traffic information and below 1.5 s for congestion free route generation in complex scenarios. To the best of our knowledge, this is the first scalable approach that can be used to predict urban traffic and avoid congestion through range query data structure traffic modelling.

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Wydrowski, B., and M. Zukerman. "MaxNet: a congestion control architecture." IEEE Communications Letters 6, no. 11 (2002): 512–14. http://dx.doi.org/10.1109/lcomm.2002.805519.

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44

Ladin, Mohd Azizul, Siti Nurmasyittah Binti Ag Besar, Noor Sheena Herayati Harith, Nurmin Bolong, Ismail Saad, and Nazaruddin Taha. "PILOT STUDY FOR THE MODELLING OF CONGESTION PRICING IN KOTA KINABALU, SABAH." MATTER: International Journal of Science and Technology 6, no. 1 (2020): 33–44. http://dx.doi.org/10.20319/mijst.2020.61.3344.

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45

O', James, N. A. Brien, Anil Namdeo, Margaret Bell, and Paul Goodman. "A congestion sensitive approach to modelling road networks for air quality management." International Journal of Environment and Pollution 54, no. 2/3/4 (2014): 213. http://dx.doi.org/10.1504/ijep.2014.065122.

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Younes, Osama S. "Modelling and analysis of TCP congestion control mechanisms using stochastic reward nets." International Journal of Computing Science and Mathematics 10, no. 4 (2019): 390. http://dx.doi.org/10.1504/ijcsm.2019.10024355.

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47

Younes, Osama S. "Modelling and analysis of TCP congestion control mechanisms using stochastic reward nets." International Journal of Computing Science and Mathematics 10, no. 4 (2019): 390. http://dx.doi.org/10.1504/ijcsm.2019.102692.

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48

Elleuch, Wiam, Ali Wali, and Adel M. Alimi. "Neural congestion prediction system for trip modelling in heterogeneous spatio-temporal patterns." International Journal of Systems Science 51, no. 8 (2020): 1373–91. http://dx.doi.org/10.1080/00207721.2020.1760957.

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49

Mrgole, Anamarija L., Marko Čelan, and Beno Mesarec. "Forecasting of Congestion in Traffic Neural Network Modelling Using Duffing Holmes Oscillator." IOP Conference Series: Materials Science and Engineering 245 (October 2017): 042030. http://dx.doi.org/10.1088/1757-899x/245/4/042030.

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50

Zhang, Wei, Ruichun He, Qiang Xiao, and Changxi Ma. "Research on Strategy Control of Taxi Carpooling Detour Route under Uncertain Environment." Discrete Dynamics in Nature and Society 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/4702360.

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Annotation:

For the problem of route choice in taxi carpooling detour, considering the uncertainty of traffic and the characteristic of passengers’ noncomplete rationality, an evolutionary game model of taxi carpooling detour route is built, in which prospect theory is introduced and revenue of strategy is replaced by prospect value. The model reflects more really decision-making psychology of passengers. Then the stable strategies of the model are studied, and the influences of detour distance and traffic congestion on detour carpooling success are analyzed, respectively. The results show that when at least one route of which prospect values for two passenger sides are both positive exists, carpooling route can reach an agreement. The route is stable strategy of evolutionary game, and the passengers requiring short travel time tend to select the nondetour route. With the increase of detour distance and traffic congestion rate, the possibility of reaching an agreement decreases gradually; that is, possibility of carpooling failure increases. So taxi carpooling detour is possible under the certain condition, but some measures must be carried out such as constraints of detour distance and mitigation of traffic congestion to improve carpooling success probability. These conclusions have a certain guiding significance to the formulation of taxi carpooling policy.

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