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Journal articles on the topic 'Physics of traffic'

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Published: 23 September 2022
Last updated: 28 September 2022

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1

Kerner, Boris S. "The physics of traffic." Physics World 12, no. 8 (August 1999): 25–30. http://dx.doi.org/10.1088/2058-7058/12/8/30.

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2

Grayling, A. C. "The physics of traffic." New Scientist 197, no. 2638 (January 2008): 48. http://dx.doi.org/10.1016/s0262-4079(08)60115-3.

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3

Schadschneider, Andreas. "Statistical physics of traffic flow." Physica A: Statistical Mechanics and its Applications 285, no. 1-2 (September 2000): 101–20. http://dx.doi.org/10.1016/s0378-4371(00)00274-0.

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4

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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5

Sando, Tom. "The physics of traffic accident investigation." Physics Teacher 27, no. 6 (September 1989): 475. http://dx.doi.org/10.1119/1.2342836.

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6

Ji, Jiahao, Jingyuan Wang, Zhe Jiang, Jiawei Jiang, and Hu Zhang. "STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4048–56. http://dx.doi.org/10.1609/aaai.v36i4.20322.

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High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio-Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications.
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7

Helbing, Dirk, and Kai Nagel. "The physics of traffic and regional development." Contemporary Physics 45, no. 5 (September 2004): 405–26. http://dx.doi.org/10.1080/00107510410001715944.

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8

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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9

Perkins Coppola, Matthew. "Talking and Writing to Learn: The Physics of Traffic Intersection Safety, Part One." Hoosier Science Teacher 41, no. 1 (February 15, 2018): 6–20. http://dx.doi.org/10.14434/thst.v41i123677.

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Physics students learn to engage in argument-based inquiry through mathematical modeling and analysis of real-world data collected from a traffic intersection in their own neighborhood. In this first part of the lesson, students focus on a single traffic intersection. Groups of students used equations of motion to construct simple mathematical models to describe how a driver approaches a yellow light at a traffic intersection. Students tested these mathematical models with a fictitious data set, then as a group collected and analyzed data from an actual traffic intersection of their choosing. Students determined the safety of the traffic intersection and presented their findings to their peers and invited members of the community. This practical research project set the stage for students (in Part Two) to tackle the larger question of whether cameras should be used to enforce traffic laws.
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10

Knospe, W., L. Santen, A. Schadschneider, and M. Schreckenberg. "A realistic two-lane traffic model for highway traffic." Journal of Physics A: Mathematical and General 35, no. 15 (April 8, 2002): 3369–88. http://dx.doi.org/10.1088/0305-4470/35/15/302.

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11

Ranaweera, Malith, A. Seneviratne, David Rey, Meead Saberi, and Vinayak V. Dixit. "Detection of anomalous vehicles using physics of traffic." Vehicular Communications 27 (January 2021): 100304. http://dx.doi.org/10.1016/j.vehcom.2020.100304.

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12

Sugiyama, Y., M. Kikuchi, A. Nakayama, K. Nishinari, A. Shibata, S. i. Tadaki, and S. Yukawa. "Traffic Flow as Physics of Many-Body System." IFAC Proceedings Volumes 36, no. 14 (August 2003): 335–40. http://dx.doi.org/10.1016/s1474-6670(17)32442-4.

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13

Schadschneider, Andreas. "Traffic flow: a statistical physics point of view." Physica A: Statistical Mechanics and its Applications 313, no. 1-2 (October 2002): 153–87. http://dx.doi.org/10.1016/s0378-4371(02)01036-1.

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14

Ross, Marc, Deena Patel, and Tom Wenzel. "Vehicle design and the physics of traffic safety." Physics Today 59, no. 1 (January 2006): 49–54. http://dx.doi.org/10.1063/1.2180177.

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15

Chowdhury, D., L. Santen, and A. Schadschneider. "Simulation of vehicular traffic: a statistical physics perspective." Computing in Science & Engineering 2, no. 5 (2000): 80–87. http://dx.doi.org/10.1109/5992.877404.

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16

Ning, Hong-Xin, and Yu Xue. "Characteristics of synchronized traffic in mixed traffic flow." Chinese Physics B 21, no. 4 (April 2012): 040506. http://dx.doi.org/10.1088/1674-1056/21/4/040506.

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17

Fukui, Minoru, and Yoshihiro Ishibashi. "Evolution of Traffic Jam in Traffic Flow Model." Journal of the Physical Society of Japan 62, no. 11 (November 15, 1993): 3841–44. http://dx.doi.org/10.1143/jpsj.62.3841.

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18

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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19

TANG, TIE-QIAO, YAN LI, and HAI-JUN HUANG. "THE EFFECTS OF BUS STOP ON TRAFFIC FLOW." International Journal of Modern Physics C 20, no. 06 (June 2009): 941–52. http://dx.doi.org/10.1142/s0129183109014096.

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In this paper, we use the traffic flow model proposed by Tang et al. [Physica A387, 6845 (2008)] to study the effects of bus stop on traffic flow. Our numerical tests show that bus stop will have great effects on the stability of traffic flow and that the effects are related to the initial density and the number of bus stops. The numerical results are accordant with the real traffic, which shows that the model proposed by Tang et al. can describe some complex traffic phenomena resulted by bus stop.
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20

Ferrari, Pablo A., and Leonardo T. Rolla. "Slow-to-Start Traffic Model: Traffic Saturation and Scaling Limits." Journal of Statistical Physics 180, no. 1-6 (May 19, 2020): 935–53. http://dx.doi.org/10.1007/s10955-020-02555-7.

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21

Fouladvand, M. Ebrahim, Zeinab Sadjadi, and M. Reza Shaebani. "Optimized traffic flow at a single intersection: traffic responsive signalization." Journal of Physics A: Mathematical and General 37, no. 3 (January 6, 2004): 561–76. http://dx.doi.org/10.1088/0305-4470/37/3/002.

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22

Lee, H.-W., V. Popkov, and D. Kim. "Two-way traffic flow: Exactly solvable model of traffic jam." Journal of Physics A: Mathematical and General 30, no. 24 (December 21, 1997): 8497–513. http://dx.doi.org/10.1088/0305-4470/30/24/014.

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23

Carrasco, S., P. Medina, J. Rogan, and J. A. Valdivia. "Simulating the city traffic complexity induced by traffic light periods." Chaos: An Interdisciplinary Journal of Nonlinear Science 31, no. 4 (April 2021): 043111. http://dx.doi.org/10.1063/5.0041028.

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24

Nagatani, Takashi. "Spreading of Traffic Jam in a Traffic Flow Model." Journal of the Physical Society of Japan 62, no. 4 (April 15, 1993): 1085–88. http://dx.doi.org/10.1143/jpsj.62.1085.

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25

EBERSBACH, ANJA, and JOHANNES J. SCHNEIDER. "TWO-LANE TRAFFIC WITH PLACES OF OBSTRUCTION TO TRAFFIC." International Journal of Modern Physics C 15, no. 04 (May 2004): 535–44. http://dx.doi.org/10.1142/s0129183104006005.

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As the Nagel–Schreckenberg model (NaSch model) became known as a realistic approach to describe traffic flow on single-lane streets, this model was extended to two-lane traffic by several groups. On the base of our two-lane model, we will now investigate the impact of a place of obstruction, e.g., because of road works, on partial fractions, densities and mean velocities.
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26

MOUSSA, NAJEM. "SIMULATION STUDY OF TRAFFIC ACCIDENTS IN BIDIRECTIONAL TRAFFIC MODELS." International Journal of Modern Physics C 21, no. 12 (December 2010): 1501–15. http://dx.doi.org/10.1142/s0129183110016007.

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Conditions for the occurrence of bidirectional collisions are developed based on the Simon–Gutowitz bidirectional traffic model. Three types of dangerous situations can occur in this model. We analyze those corresponding to head-on collision; rear-end collision and lane-changing collision. Using Monte Carlo simulations, we compute the probability of the occurrence of these collisions for different values of the oncoming cars' density. It is found that the risk of collisions is important when the density of cars in one lane is small and that of the other lane is high enough. The influence of different proportions of heavy vehicles is also studied. We found that heavy vehicles cause an important reduction of traffic flow on the home lane and provoke an increase of the risk of car accidents.
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27

Nagatani, T. "Effect of traffic accident on jamming transition in traffic-flow model." Journal of Physics A: Mathematical and General 26, no. 19 (October 7, 1993): L1015—L1020. http://dx.doi.org/10.1088/0305-4470/26/19/008.

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28

Shi, Rongye, Zhaobin Mo, and Xuan Di. "Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 540–47. http://dx.doi.org/10.1609/aaai.v35i1.16132.

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Traffic state estimation (TSE) reconstructs the traffic variables (e.g., density or average velocity) on road segments using partially observed data, which is important for traffic managements. Traditional TSE approaches mainly bifurcate into two categories: model-driven and data-driven, and each of them has shortcomings. To mitigate these limitations, hybrid TSE methods, which combine both model-driven and data-driven, are becoming a promising solution. This paper introduces a hybrid framework, physics-informed deep learning (PIDL), to combine second-order traffic flow models and neural networks to solve the TSE problem. PIDL can encode traffic flow models into deep neural networks to regularize the learning process to achieve improved data efficiency and estimation accuracy. We focus on highway TSE with observed data from loop detectors and probe vehicles, using both density and average velocity as the traffic variables. With numerical examples, we show the use of PIDL to solve a popular second-order traffic flow model, i.e., a Greenshields-based Aw-Rascle-Zhang (ARZ) model, and discover the model parameters. We then evaluate the PIDL-based TSE method using the Next Generation SIMulation (NGSIM) dataset. Experimental results demonstrate the proposed PIDL-based approach to outperform advanced baseline methods in terms of data efficiency and estimation accuracy.
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29

Chowdhury, D. "Statistical physics of vehicular traffic and some related systems." Physics Reports 329, no. 4-6 (May 2000): 199–329. http://dx.doi.org/10.1016/s0370-1573(99)00117-9.

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30

Pereira, Mike, Annika Lang, and Balázs Kulcsár. "Short-term traffic prediction using physics-aware neural networks." Transportation Research Part C: Emerging Technologies 142 (September 2022): 103772. http://dx.doi.org/10.1016/j.trc.2022.103772.

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31

Csahok, Z., and T. Vicsek. "Traffic models with disorder." Journal of Physics A: Mathematical and General 27, no. 16 (August 21, 1994): L591—L596. http://dx.doi.org/10.1088/0305-4470/27/16/005.

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32

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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33

Li, Yuni, and Jianli Xiao. "Traffic peak period detection using traffic index cloud maps." Physica A: Statistical Mechanics and its Applications 553 (September 2020): 124277. http://dx.doi.org/10.1016/j.physa.2020.124277.

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34

TAN, HUILI, CHAOYING ZHANG, LINGJIANG KONG, and MUREN LIU. "TRAFFIC FLOW INFLUENCED BY TRAFFIC LIGHT AND TURNING PROBABILITY FOR A CROSSROAD." International Journal of Modern Physics B 18, no. 17n19 (July 30, 2004): 2658–62. http://dx.doi.org/10.1142/s0217979204025865.

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A cellular automaton model with open boundary condition for a crossroad system controlled by a traffic light is presented. The traffic flow and speed of the first part of the road are quite different from those of the second part behind the crossing. The impact of turning probabilities and the cycle times of traffic light on the flow are investigated.
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35

Araujo, Amilson, and Ivanderson Pereira da Silva. "Maker culture and educational robotics in physics teaching: developing an automated traffic light in high school." JOURNAL OF RESEARCH AND KNOWLEDGE SPREADING 1, no. 1 (December 29, 2020): 11654. http://dx.doi.org/10.20952/jrks1111654.

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In this study we explore the didactic potential of educational robotics for the construction of an interdisciplinary project for teaching Physics and teaching Mathematics, in the context of basic education. It is an account of interdisciplinary experience in which, through the pedagogy of projects, the use of robots in the construction of timed traffic lights (one for pedestrians and one for cars) was evidenced. This proposal involved students from the 1st, 2nd and 3rd years of high school, aged between 13 and 18 years, from the State School Álvaro Paes, located in the city of Coité do Nóia-AL. The project emerged from the initiative of teachers of Physics and Mathematics, in conjunction with the Robotics Group of the State Network of Alagoas, and in this sense took the concept of kinematics. The activities were developed on Saturdays during the academic year of 2017. It was evident, from this experience, that the contextualization of the classes through robotics projects, make the classes motivating, since the students were involved with the possibility of creating, from the concepts of Physics and Mathematics, in the moments, involving and bringing the subjects closer to a scientific practice. As an educational product, an article was prepared with an account of the practice of a robotics project developed in the classroom. Initially, the training of work teams for the construction and development of tasks in class was carried out. Then the students participated in robotics classes, understanding its fundamentals and its application within the disciplines of Physics and Mathematics, introducing the subject of traffic lights for the Traffic Light.
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36

Munigety, Caleb Ronald. "Conformity and stability analysis of a modified spring–mass–damper system dynamics-based car-following model." International Journal of Modern Physics B 33, no. 06 (March 10, 2019): 1950025. http://dx.doi.org/10.1142/s0217979219500255.

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Modeling the dynamics of a traffic system involves using the principles of both physical and social sciences since it is composed of vehicles as well as drivers. A novel car-following model is proposed in this paper by incorporating the socio-psychological aspects of drivers into the dynamics of a purely physics-based spring–mass–damper mechanical system to represent the driver–vehicle longitudinal movements in a traffic stream. The crux of this model is that a traffic system can be viewed as various masses interacting with each other by means of springs and dampers attached between them. While the spring and damping constants represent the driver behavioral parameters, the mass component represents the vehicle characteristics. The proposed model when tested for its ability to capture the traffic system dynamics both at micro, driver, and macro, stream, levels behaved pragmatically. The stability analysis carried out using perturbation method also revealed that the proposed model is both locally and asymptotically stable.
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37

Petrović, Aleksandra, Nataša Gospić, Nebojša Arsić, and Osman Lindov. "Innovative approach to traffic safety education: TRAFSAF project." Tehnika 77, no. 1 (2022): 83–86. http://dx.doi.org/10.5937/tehnika2201083p.

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The problems of traffic safety and the profession of traffic safety are universal problems that require constant strengthening of the system of education and innovation both in syllabuses and in technologies and techniques applied in the field of traffic safety. The purpose of the innovative approach to the education program is to educate undergraduate and graduate students in the field of traffic safety, in accordance with the needs of society, which should provide an interdisciplinary and multidisciplinary approach to safety of all road users, technical solutions and social response to traffic accidents. Traffic safety problems are complex problems, which is why the competencies and skills acquired by engineers should provide knowledge and a deeper understanding of risk while strengthening the applied and practical skills. Innovative study programs must have a common basis for all directions, with a deep knowledge of mathematics, physics and mechanics with an emphasis on a holistic and interdisciplinary approach to the use of engineering in improving traffic safety.
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38

Li Sheng-Chun, Kong Ling-Jiang, Liu Mu-Ren, and Zheng Rong-Sen. "The effects of intelligent traffic light on the crossing traffic flow." Acta Physica Sinica 58, no. 4 (2009): 2266. http://dx.doi.org/10.7498/aps.58.2266.

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39

Li, Zhi-Peng, Fu-Qiang Liu, and Jian Sun. "A lattice traffic model with consideration of preceding mixture traffic information." Chinese Physics B 20, no. 8 (August 2011): 088901. http://dx.doi.org/10.1088/1674-1056/20/8/088901.

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40

Zhou, Nan. "The Future Integrated Traffic Management System to Optimize the Automobile Traffic." Journal of Physics: Conference Series 1972, no. 1 (July 1, 2021): 012097. http://dx.doi.org/10.1088/1742-6596/1972/1/012097.

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41

Zhang, Lele, Caley Finn, Timothy M. Garoni, and Jan de Gier. "Behaviour of traffic on a link with traffic light boundaries." Physica A: Statistical Mechanics and its Applications 503 (August 2018): 116–38. http://dx.doi.org/10.1016/j.physa.2018.02.201.

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42

Nagatani, Takashi. "Vehicular traffic through a self-similar sequence of traffic lights." Physica A: Statistical Mechanics and its Applications 386, no. 1 (December 2007): 381–87. http://dx.doi.org/10.1016/j.physa.2007.07.042.

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43

Vaidya, Princy, Dr Sanjay Haridas, and Avinash Ikhar. "Public Transportation System using Swarm Technology." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 2 (July 30, 2022): 113–16. http://dx.doi.org/10.35940/ijrte.b7158.0711222.

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Pedestrians, ridden or herded animals, vehicles, streetcars, and buses are all instances of road users who travel alone or in groups on public roadways. Both informal and official standards are included in the phrase "road regulations." standards and legislation that have emerged over time to help keep traffic flowing smoothly and efficiently. The informal rules and legislation that have developed over time to facilitate the orderly and timely flow of traffic are known as rules of the road. In structured traffic, terms like priorities, lanes, right-of-way, and traffic management have specific definitions. Heavy motor vehicles (cars, trucks), other vehicles (mopeds, bicycles), and pedestrians are the three types of traffic. Some countries have complicated and detailed traffic laws, while others rely on common sense and driver cooperation. In terms of travel, the organization t gives a better mix of safety and efficiency. Road work, garbage, and street collisions can all obstruct traffic flow and transform it into a chaotic mess. On heavily packed freeways, a minor disruption will persist, a phenomenon known as traffic waves. A complete breakdown of organization can result in gridlock and traffic congestion. In simulations of organized traffic, stochastic processes, queuing theory, and mathematical physics equations are widely used.
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44

Baker, R. G. V. "Towards a physics of Internet traffic in a geographic network." Physica A: Statistical Mechanics and its Applications 391, no. 4 (February 2012): 1133–48. http://dx.doi.org/10.1016/j.physa.2011.10.002.

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45

Rastgoftar, Hossein, and Ella Atkins. "Physics-Based Freely Scalable Continuum Deformation for UAS Traffic Coordination." IEEE Transactions on Control of Network Systems 7, no. 2 (June 2020): 532–44. http://dx.doi.org/10.1109/tcns.2019.2954521.

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46

Mahnke, R., and J. Kaupužs. "Stochastic theory of freeway traffic." Physical Review E 59, no. 1 (January 1, 1999): 117–25. http://dx.doi.org/10.1103/physreve.59.117.

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47

Zhang, Liangliang, Yuanhua Jia, Dongye Sun, and Yang Yang. "A fuzzy weighted c-means classification method for traffic flow state division." Modern Physics Letters B 35, no. 20 (June 22, 2021): 2150341. http://dx.doi.org/10.1142/s0217984921503413.

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Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.
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48

Sun, Jiayang. "Mathematical Modeling of Traffic." Journal of Physics: Conference Series 2012, no. 1 (September 1, 2021): 012060. http://dx.doi.org/10.1088/1742-6596/2012/1/012060.

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49

Paszkiewicz, Andrzej, Bartosz Pawłowicz, Bartosz Trybus, and Mateusz Salach. "Traffic Intersection Lane Control Using Radio Frequency Identification and 5G Communication." Energies 14, no. 23 (December 2, 2021): 8066. http://dx.doi.org/10.3390/en14238066.

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This article deals with automated urban traffic management, and proposes a new comprehensive infrastructure solution for dynamic traffic direction switching at intersection lines. It was assumed that the currently used solutions based on video monitoring are unreliable. Therefore, the Radio Frequency IDentification (RFID) technique was introduced, in which vehicles are counted and, if necessary, identified in order to estimate the flows on individual lanes. The data is acquired in real time using fifth-generation wireless communications (5G). The Pots and Ising models derived from the theory of statistical physics were used in a novel way to determine the state of direction traffic lights. The models were verified by simulations using data collected from real traffic observations. The results were presented for two exemplary intersections.
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50

Levine, E., G. Ziv, L. Gray, and D. Mukamel. "Phase Transitions in Traffic Models." Journal of Statistical Physics 117, no. 5-6 (December 2004): 819–30. http://dx.doi.org/10.1007/s10955-004-5706-6.

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