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Journal articles on the topic 'Traffic jams'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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1

Jones, Rachel. "Axonal traffic jams." Nature Reviews Neuroscience 4, no. 11 (November 2003): 856–57. http://dx.doi.org/10.1038/nrn1268.

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2

Nagel, Kai, and Maya Paczuski. "Emergent traffic jams." Physical Review E 51, no. 4 (April 1, 1995): 2909–18. http://dx.doi.org/10.1103/physreve.51.2909.

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3

C, Keerthika, Narahari Greeshma, Priya Vyshnavi, Vyshnavi Kumar Reddy, K. Indhira, and V. M. Chandrasekaran. "Mathematical Model for Traffic Flow." International Journal of Engineering & Technology 7, no. 4.10 (October 2, 2018): 940. http://dx.doi.org/10.14419/ijet.v7i4.10.26631.

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Every year countless hours are lost in traffic jams. When the density of traffic is sufficiently high small disturbances in vehicle’s accelerations can cause phantom traffic jams. We can relate the traffic flow to mathematics and physics like that of liquids and gases. This paper presents mathematical model for phantom jams and Gauss Jordan elimination for traffic flow.
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4

Gupta, Himadri Shikhar, and Ramakrishna Ramaswamy. "Backbones of traffic jams." Journal of Physics A: Mathematical and General 29, no. 21 (November 7, 1996): L547—L553. http://dx.doi.org/10.1088/0305-4470/29/21/003.

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5

Conway, Leslie, Derek Wood, Andrew M. O' Neill, Kristopher E. Daly, Erkan Tüzel, and Jennifer Ross. "Microtubule Motor Traffic Jams." Biophysical Journal 102, no. 3 (January 2012): 368a. http://dx.doi.org/10.1016/j.bpj.2011.11.2011.

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6

Li, Shu Bin, Yong Lin, Hua Ling Ren, and Jian Cheng Long. "Research on Control Strategies and Optimal Signal Timing for Traffic Congestion Based on Prediction." Applied Mechanics and Materials 40-41 (November 2010): 858–65. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.858.

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Designing effective control strategies to traffic jams is an important measure to solve the problem of traffic jams in urban traffic network. Most of the green ratio models clear off crowded traffic flow synchronously at each approach of signal intersection in oversaturated traffic, but ignore the difference of traffic flows in different traffic state, and lead to the queue becoming longer and longer at oversaturated signal intersection. In this paper, the cell transmission model is applied to the propagation of traffic jams, the formation and dissipation of traffic jams in urban traffic network. A new method for predicting the traffic states is proposed, and the future traffic states can be achieved, according to the spatial structure of traffic jam propagation. We use an idea of traffic priority as an management measure to design the optimal green ratio in advance, and the improved green ratio model can realize the goal of preventing traffic congestion and clearing off traffic blockage quickly. Simulation results show that the proposed strategies with appropriate application can effectively control jam dissipation and prevent traffic congestion formation.
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7

Melville, David B., and Ela W. Knapik. "Traffic jams in fish bones." Cell Adhesion & Migration 5, no. 2 (March 2011): 114–18. http://dx.doi.org/10.4161/cam.5.2.14377.

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8

Li, Tong. "Nonlinear dynamics of traffic jams." Physica D: Nonlinear Phenomena 207, no. 1-2 (July 2005): 41–51. http://dx.doi.org/10.1016/j.physd.2005.05.011.

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9

Orosz, Gábor, R. Eddie Wilson, and Gábor Stépán. "Traffic jams: dynamics and control." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1928 (October 13, 2010): 4455–79. http://dx.doi.org/10.1098/rsta.2010.0205.

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This introductory paper reviews the current state-of-the-art scientific methods used for modelling, analysing and controlling the dynamics of vehicular traffic. Possible mechanisms underlying traffic jam formation and propagation are presented from a dynamical viewpoint. Stable and unstable motions are described that may give the skeleton of traffic dynamics, and the effects of driver behaviour are emphasized in determining the emergent state in a vehicular system. At appropriate points, references are provided to the papers published in the corresponding Theme Issue.
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10

Bons, Paul D., Albrecht Baur, Marlina A. Elburg, Matthias J. Lindhuber, Michael A. W. Marks, Alvar Soesoo, Boudewijn P. van Milligen, and Nicolas P. Walte. "Layered intrusions and traffic jams." Geology 43, no. 1 (January 2015): 71–74. http://dx.doi.org/10.1130/g36276.1.

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11

Knorr, F., D. Baselt, M. Schreckenberg, and M. Mauve. "Reducing Traffic Jams via VANETs." IEEE Transactions on Vehicular Technology 61, no. 8 (October 2012): 3490–98. http://dx.doi.org/10.1109/tvt.2012.2209690.

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12

Kerner, B. S., S. L. Klenov, and P. Konhäuser. "Asymptotic theory of traffic jams." Physical Review E 56, no. 4 (October 1, 1997): 4200–4216. http://dx.doi.org/10.1103/physreve.56.4200.

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13

Nagatani, Takashi. "The physics of traffic jams." Reports on Progress in Physics 65, no. 9 (August 13, 2002): 1331–86. http://dx.doi.org/10.1088/0034-4885/65/9/203.

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14

Crowcroft, J. "Traffic Jams on the Internet." Science 280, no. 5361 (April 10, 1998): 179f—179. http://dx.doi.org/10.1126/science.280.5361.179f.

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15

James, T. "How to cut traffic jams." Engineering & Technology 8, no. 1 (February 1, 2013): 44–47. http://dx.doi.org/10.1049/et.2013.0104.

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16

Kaulke, M., and S. Trimper. "Analytical approach to traffic jams." Journal of Physics A: Mathematical and General 28, no. 19 (October 7, 1995): 5445–49. http://dx.doi.org/10.1088/0305-4470/28/19/002.

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17

Finkelstein, Ilya J., and Eric C. Greene. "Molecular Traffic Jams on DNA." Annual Review of Biophysics 42, no. 1 (May 6, 2013): 241–63. http://dx.doi.org/10.1146/annurev-biophys-083012-130304.

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18

Nagel, Kai, and Hans J. Herrmann. "Deterministic models for traffic jams." Physica A: Statistical Mechanics and its Applications 199, no. 2 (October 1993): 254–69. http://dx.doi.org/10.1016/0378-4371(93)90006-p.

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19

NAGEL, KAI. "LIFE TIMES OF SIMULATED TRAFFIC JAMS." International Journal of Modern Physics C 05, no. 03 (June 1994): 567–80. http://dx.doi.org/10.1142/s012918319400074x.

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We study a model for freeway traffic which includes strong noise taking into account the fluctuations of individual driving behavior. The model shows emergent traffic jams with a self-similar appearance near the throughput maximum of the traffic. The lifetime distribution of these jams shows a short scaling regime, which gets considerably longer if one reduces the fluctuations when driving at maximum speed but leaves the fluctuations for slowing down or accelerating unchanged. The outflow from a traffic jam self-organizes into this state of maximum throughput.
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20

NAGATANI, TAKASHI. "SELF-ORGANIZED CRITICALITY IN 1D TRAFFIC FLOW." Fractals 04, no. 03 (September 1996): 279–83. http://dx.doi.org/10.1142/s0218348x96000388.

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Annihilation process of traffic jams is investigated in a one-dimensional traffic flow on a highway. The one-dimensional fully asymmetric exclusion model with open boundaries for parallel update is extended to take into account stochastic transition of cars, where a car moves ahead with transition probability pt. Near pt=1, the system is driven asymptotically into a steady state exhibiting a self-organized criticality. Traffic jams with various lifetimes (or sizes) appear and disappear by colliding with an empty wave. The typical lifetime <m> of traffic jams scales as [Formula: see text], where ∆pt=1−pt. It is shown that the cumulative lifetime distribution Nm(∆pt) satisfies the scaling form [Formula: see text].
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21

Hegyi, A., B. De Schutter, and J. Hellendoorn. "Optimal Control of Freeway Networks with Bottlenecks and Static Demand." Transportation Research Record: Journal of the Transportation Research Board 1925, no. 1 (January 2005): 29–37. http://dx.doi.org/10.1177/0361198105192500104.

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Optimally coordinated freeway traffic control for networks containing bottlenecks with capacity drop and hysteresis behavior is considered. Because of the multitude of traffic jams and the spatial and temporal relationships between control actions and traffic behavior, this problem is not as straightforward as that for local control. The order in which the measures are applied may be relevant, or it may be possible that not all jams can be resolved. In that case the best possible locations of jams should be determined. An approach to address these problems is developed in which a generalized representation of flow-limiting control measures and bottlenecks is used. Whether a certain set of control measures is sufficient to improve network performance is determined. The approach also supplies the necessary sequence of control actions and the necessary relocation of traffic jams to achieve the network state corresponding to the best achievable performance.
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22

Wright, Chris, and Penina Roberg. "The conceptual structure of traffic jams." Transport Policy 5, no. 1 (January 1998): 23–35. http://dx.doi.org/10.1016/s0967-070x(98)00006-7.

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23

Hurley, Jennifer M. "Cytoplasmic traffic jams affect circadian timing." Science Translational Medicine 12, no. 569 (November 11, 2020): eabf4681. http://dx.doi.org/10.1126/scitranslmed.abf4681.

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24

Peterson, I. "Circumventing Traffic Jams on the Internet." Science News 149, no. 12 (March 23, 1996): 181. http://dx.doi.org/10.2307/3979762.

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25

Schmidt, Hauke, and Peter Bock. "Detection of Traffic Jams Using ALISA." Computer-Aided Civil and Infrastructure Engineering 13, no. 5 (September 1998): 357–61. http://dx.doi.org/10.1111/0885-9507.00114.

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26

Sibbison, J. B. "USA: Traffic jams and the air." Lancet 336, no. 8727 (December 1990): 1371. http://dx.doi.org/10.1016/0140-6736(90)92912-2.

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27

Mun, Se-il. "Traffic jams and the congestion toll." Transportation Research Part B: Methodological 28, no. 5 (October 1994): 365–75. http://dx.doi.org/10.1016/0191-2615(94)90035-3.

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28

Shi, Y. F., and L. C. Yang. "Improved coupled map car-following model considering partial car-to-car communication and its jam analysis." Canadian Journal of Physics 95, no. 11 (November 2017): 1096–102. http://dx.doi.org/10.1139/cjp-2016-0639.

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The characteristics and the nonlinear phenomenon of traffic flow in the case of car-to-car communication (C2CC) are studied based on an improved coupled map car-following model. The model incorporates the modified optimal velocity function and appropriate control method. The conditions necessary to maintain the system stability and suppress traffic jams are obtained. To describe the car-following dynamics under C2CC accurately, different penetration rates of C2CC vehicles, such as 10%, 30%, and 60% are considered. The simulation results suggest that the improved model can effectively suppress traffic jams. The extent to which traffic jams are suppressed is increasing as the penetration rate increases. Moreover, the car-following stability has a noticeable improvement by analysing the time–space plots.
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29

JIANG, RUI, and QING-SONG WU. "MODIFIED COMFORTABLE DRIVING MODEL FOR CONGESTED TRAFFIC FLOW." International Journal of Modern Physics B 18, no. 14 (June 10, 2004): 1991–2001. http://dx.doi.org/10.1142/s021797920402518x.

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In this paper, the concepts of "jammed status" and "jam headway" [X. B. Li, R. Jiang and Q. S. Wu, Phys. Rev.E68, 016117 (2003)] are introduced into the Modified Comfortable Driving (MCD) model [R. Jiang and Q. S. Wu, J. Phys.A36, 381 (2003)] to simulate the congested traffic flow including synchronized flow and wide moving jams. Using computer simulation, the fundamental diagram, the space–time plots, the time series of the density in the jams, the 1-min average data in the flow-density plane, the traffic patterns induced by red light are investigated. It is shown that the new model can describe both the synchronized flow and the sparse wide jams quite well.
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30

Goldmann, Kathrin, and Gernot Sieg. "Economic implications of phantom traffic jams: evidence from traffic experiments." Transportation Letters 12, no. 6 (April 29, 2019): 386–90. http://dx.doi.org/10.1080/19427867.2019.1611077.

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31

KUZNETSOV, A. V., A. A. AVRAMENKO, and D. G. BLINOV. "MODELING TRAFFIC JAMS IN SLOW AXONAL TRANSPORT." Journal of Mechanics in Medicine and Biology 10, no. 03 (September 2010): 445–65. http://dx.doi.org/10.1142/s0219519410003502.

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The purpose of this paper is to develop a model capable of simulating traffic jams in slow axonal transport. Slowing of slow axonal transport is an early sign of some neurodegenerative diseases. Axonal swellings observed near the end stage of such diseases may be an indication of traffic jams developing in axons that cause the slowing down of slow axonal transport. Traffic jams may result from misregulation of microtubule-associated proteins caused by an imbalance in intracellular signaling or by mutations of these proteins. This misregulation leads to a decay of microtubule tracks in axons, effectively reducing the number of "railway tracks" available for molecular-motor-assisted transport of intracellular organelles. In this paper, the decay of microtubule tracks is modeled by a reduction of the number density of microtubules in the central part of the axon. Simulation results indicate that the model predicts the build-up of the bell-shaped concentration wave, as the wave approaches the bottleneck (blockage) region. This increase in concentration will likely plug the bottleneck region resulting in a traffic jam that would hinder the slow axonal transport.
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32

Abishek. B, Ebenezer, Dr Arun Raaza, and Dr V. Rajendran. "Design & development of vehicular micro strip patch antenna for emergency vehicle." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 473. http://dx.doi.org/10.14419/ijet.v7i2.33.14813.

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Life threat events can occur during Traffic congested peak hours. Emergency vehicles are mostly stuck in the traffic jams because people are unaware of traffic rules, no separate lanes for emergency vehicles. Traffic jams in case of health emergencies and emergencies like fire accident lead to increase in death rate. The loss of precious life can be avoided by efficient traffic management system. In this paper a microstrip antenna is designed and developed with radiation characteristics best suited for the emergency vehicles. Emergency vehicle alert for short-range vehicular networks will be implemented using the fabricated antenna.
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33

Zhang, Limiao, Guanwen Zeng, Daqing Li, Hai-Jun Huang, H. Eugene Stanley, and Shlomo Havlin. "Scale-free resilience of real traffic jams." Proceedings of the National Academy of Sciences 116, no. 18 (April 12, 2019): 8673–78. http://dx.doi.org/10.1073/pnas.1814982116.

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The concept of resilience can be realized in natural and engineering systems, representing the ability of a system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation systems, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations are still missing. Here, we define city traffic resilience based on the spatiotemporal clusters of congestion in real traffic and find that the resilience follows a scale-free distribution in 2D city road networks and 1D highways with different exponents but similar exponents on different days and in different cities. The traffic resilience is also revealed to have a scaling relation between the cluster size of the spatiotemporal jam and its recovery duration independent of microscopic details. Our findings of universal traffic resilience can provide an indication toward better understanding and designing of these complex engineering systems under internal and external disturbances.
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34

Alaidi, Abdul Hadi M., Ibtisam A. Aljazaery, Haider TH Salim Alrikabi, Ibrahim Nasir Mahmood, and Faisal Theyab Abed. "Design and Implementation of a Smart Traffic Light Management System Controlled Wirelessly by Arduino." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 07 (May 6, 2020): 32. http://dx.doi.org/10.3991/ijim.v14i07.12823.

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<p>In Iraq, the number of people who own vehicles has grown up significantly. However, this increment in vehicles number doesn’t accomplished by a study of roads and intersections expansion. As a result, traffic jams became a big problem that led to long waiting time at each intersection, increased car accidents, pollution, and economic problems. To solve this problem a Smart Traffic Light System (STLS) has been implemented using Arduino, camera, IR sensor to overcome traffic jams problems in Kut city – Iraq.</p>
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35

Bai, Ke Zhao, Li Yang, Jian Huang, Rong Sen Zheng, and Hua Kuang. "Effects of the Visibility on Traffic Flow." Applied Mechanics and Materials 178-181 (May 2012): 1782–85. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1782.

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Based on the NaSch model, an extended cellular automaton model is proposed to simulate traffic flow by considering the effects of visibility. Under the open boundary condition, the influences of the injection probability, disappearance probability and visibility are discussed. The simulation results show that the injection probability and disappearance probability within a certain range have an important effect on the flux, density and velocity. And traffic jams often occur in poor visibility areas, which can become a road bottleneck. Furthermore, in order to effectively decrease the occurrence of traffic jams, the injection probability and disappearance probability should be set up reasonably.
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36

Baskov, V. N., D. A. Krasnikova, and E. I. Isaeva. "Effect of Driver Behaviour on Traffic Jams." World of Transport and Transportation 17, no. 4 (January 15, 2020): 272–81. http://dx.doi.org/10.30932/1992-3252-2019-17-4-272-281.

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Driving in a traffic flow implies involvement in difficult traffic situations that adversely affects response time of a driver, which in turn is considered when estimating stopping distance of a vehicle and determines road safety. This relationship shows the effect of driver behaviour in traffic flow on the road traffic situation. The objective of the study was to study behavioural factors that influence driver’s decisions. The study used methods of driver behaviour modelling, mathematical modelling, experimental studies of the mental and psychological functions of drivers. Modelling the driver’s behaviour, considering various combinations of many behavioural and other factors, leads to a large number of options for mathematical description of driver behaviour, which makes it difficult to use this approach to describe behaviour of drivers under the conditions of a real street-road network. The research has analysed several works devoted to the study of control action of drivers, using unknown coefficients, describing a model of movement of vehicles considering accuracy of their control. Driving through an unregulated intersection is considered as the most complex and informative version of driver’s behaviour. It is found that when modelling a traffic flow, it is necessary to take into account the degree of resoluteness of drivers (through determination of a coefficient of resoluteness which is a random variable that takes into account the probability distribution of the coefficient’s value in conjunction with the probability distribution of the function of traffic flow intensity). The distribution of the coefficient of resoluteness of drivers, obtained from experimental data, was subject to analysis. It is determined that the driving style affects formation of traffic congestion. The assessment of the driving style is made through conditional classification of driver behaviour on the road, namely marked by manifestation of aggression and timidity. When studying the behaviour of timid and aggressive drivers, several pairs of trajectories and the dynamics of the corresponding traffic flow density, were considered and calculated based on Edie’s model. It has been confirmed that traffic congestion has the greatest negative effect on choleric drivers and sanguine drivers. Besides, there is a relationship between the response time of a driver and the change in his functional condition. It is concluded that to improve road safety thanks to a more accurate assessment of possible risks of formation of congestion situations, it is necessary to consider behavioural characteristics and temperaments of the drivers.
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37

MENG, JIANPING, TAO SONG, LIYUN DONG, and SHIQIANG DAI. "STOCHASTIC CAR-FOLLOWING MODEL FOR EXPLAINING NONLINEAR TRAFFIC PHENOMENA." International Journal of Modern Physics B 25, no. 08 (March 30, 2011): 1111–20. http://dx.doi.org/10.1142/s0217979211058419.

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There is a common time parameter for representing the sensitivity or the lag (response) time of drivers in many car-following models. In the viewpoint of traffic psychology, this parameter could be considered as the perception–response time (PRT). Generally, this parameter is set to be a constant in previous models. However, PRT is actually not a constant but a random variable described by the lognormal distribution. Thus the probability can be naturally introduced into car-following models by recovering the probability of PRT. For demonstrating this idea, a specific stochastic model is constructed based on the optimal velocity model. By conducting simulations under periodic boundary conditions, it is found that some important traffic phenomena, such as the hysteresis and phantom traffic jams phenomena, can be reproduced more realistically. Especially, an interesting experimental feature of traffic jams, i.e., two moving jams propagating in parallel with constant speed stably and sustainably, is successfully captured by the present model.
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38

Kennedy, David. "Card Games, Roughhousing, Traffic Jams & Thunderstorms." Thinking: The Journal of Philosophy for Children 16, no. 4 (2003): 33–36. http://dx.doi.org/10.5840/thinking200316414.

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39

Blank, M. "Dynamics of Traffic Jams: Order and Chaos." Moscow Mathematical Journal 1, no. 1 (2001): 1–26. http://dx.doi.org/10.17323/1609-4514-2001-1-1-1-26.

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40

van Dooren, G. G., R. F. Waller, G. I. McFadden, K. A. Joiner, and D. S. Roos. "Traffic Jams: Protein Transport in Plasmodium falciparum." Parasitology Today 16, no. 10 (October 2000): 421–27. http://dx.doi.org/10.1016/s0169-4758(00)01792-0.

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41

CW. "Taking an Uber may worsen traffic jams." New Scientist 242, no. 3230 (May 2019): 19. http://dx.doi.org/10.1016/s0262-4079(19)30896-6.

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42

Quek, Wei Liang, and Lock Yue Chew. "Mechanism of Traffic Jams at Speed Bottlenecks." Procedia Computer Science 29 (2014): 289–98. http://dx.doi.org/10.1016/j.procs.2014.05.026.

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43

MURAMATSU, Masakuni, and Takashi NAGATANI. "Simulation and Linear Stability of Traffic Jams." Transactions of the Japan Society of Mechanical Engineers Series B 65, no. 633 (1999): 1599–606. http://dx.doi.org/10.1299/kikaib.65.1599.

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44

Nagatani, Takashi. "Propagation of Jams in Congested Traffic Flow." Journal of the Physical Society of Japan 65, no. 7 (July 15, 1996): 2333–36. http://dx.doi.org/10.1143/jpsj.65.2333.

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45

Chakrabarti, Bikas K. "A fiber bundle model of traffic jams." Physica A: Statistical Mechanics and its Applications 372, no. 1 (December 2006): 162–66. http://dx.doi.org/10.1016/j.physa.2006.05.003.

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46

Kerner, B. S., and H. Rehborn. "Experimental features and characteristics of traffic jams." Physical Review E 53, no. 2 (February 1, 1996): R1297—R1300. http://dx.doi.org/10.1103/physreve.53.r1297.

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47

Krause, Sebastian M., Lars Habel, Thomas Guhr, and Michael Schreckenberg. "The importance of antipersistence for traffic jams." EPL (Europhysics Letters) 118, no. 3 (May 1, 2017): 38005. http://dx.doi.org/10.1209/0295-5075/118/38005.

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48

Resnick, Mitchel. "Unblocking the traffic jams in corporate thinking." Complexity 3, no. 4 (March 1998): 27–30. http://dx.doi.org/10.1002/(sici)1099-0526(199803/04)3:4<27::aid-cplx5>3.0.co;2-h.

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49

Kurtze, Douglas A., and Daniel C. Hong. "Traffic jams, granular flow, and soliton selection." Physical Review E 52, no. 1 (July 1, 1995): 218–21. http://dx.doi.org/10.1103/physreve.52.218.

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50

Ross, J. L. "The impacts of molecular motor traffic jams." Proceedings of the National Academy of Sciences 109, no. 16 (April 9, 2012): 5911–12. http://dx.doi.org/10.1073/pnas.1203542109.

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