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Journal articles on the topic 'Traffic flow'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Elizabeth Philip, Babitha, and Jaseela K H. "Traffic Flow Modeling and Study of Traffic Congestion." International Journal of Scientific Engineering and Research 4, no. 1 (2016): 67–68. https://doi.org/10.70729/ijser15667.

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2

J, Cynthia, G. Sakthi Priya, C. Kevin Samuel, Suguna M, Senthil J, and S. Abraham Jebaraj. "Traffic Flow Forecasting Using Machine Learning Techniques." Webology 18, no. 04 (2021): 1512–26. http://dx.doi.org/10.14704/web/v18si04/web18295.

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Congestion due to traffic, results in wasted fuel, increase in pollution level, increase in travel time and vehicular queuing. Smart city initiatives are aimed to improve the quality of urban life. Intelligent Transportation System (ITS) provides solution for many smart city projects, as they capture real time data without any fixed infrastructure. The real-time prediction of traffic flow aids in alleviating congestion. Accurate and timely prediction on the future traffic flow helps individual travellers, public transport, and transport planning. Existing systems are designed to predict specif

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3

Pešić, Dalibor, Boris Antić, Emir Smailovic, and Bojana Todosijević. "The impact of the average traffic flow speed on occurrence risk of road crash." Put i saobraćaj 65, no. 2 (2019): 29–36. http://dx.doi.org/10.31075/pis.65.02.05.

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Traffic flow characteristics have a significant impact on occurrence risk of road crash. The most important characteristics of the traffic flow, the impact of which is the subject of numerous studies, are the traffc flow, density, average traffic flow speed, dispersion of traffic flow speeds, as well as the contents of vehicle in traffic flow. These characteristics are in strong correlation between each other, so changes in one parameter conditional make change of other parameters. Research shows that speed-related traffic flow parameters have a significant impact on occurrence risk of road cr

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4

Singh, Rahul, Kamini Rawat, and Aarti Kadiyan. "Simulation of Traffic Flow in Presence of Traffic Light using Cellular Automata." Indian Journal of Applied Research 4, no. 6 (2011): 191–93. http://dx.doi.org/10.15373/2249555x/june2014/60.

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5

Hrytsun, Oleh. "Impact of traffic volume and composition on the change in the speed of traffic flow." Transport technologies 2023, no. 1 (2023): 12–20. http://dx.doi.org/10.23939/tt2023.01.012.

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The problem of the change in the speed of traffic flow at different traffic volumes and compositions is researched in this study. The section of the road network with different geometric parameters (descent, ascent and horizontal section) was chosen for the study. The method of investigation of traffic flow`s speed and factors which have an impact on the reduction of road network capacity are analyzed. The change in the coefficients of the unevenness of traffic flow by hours of the day in the studied area was determined and a graph of the distribution of traffic volume by hours of the day was

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6

Milius, Susan. "Ant Traffic Flow." Science News 162, no. 25/26 (2002): 388. http://dx.doi.org/10.2307/4013963.

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7

Schmidt, Werner, Stephan Borgert, Albert Fleischmann, Lutz Heuser, Christian Müller, and Immanuel Schweizer. "Smart Traffic Flow." HMD Praxis der Wirtschaftsinformatik 52, no. 4 (2015): 585–96. http://dx.doi.org/10.1365/s40702-015-0146-0.

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8

Adhikari, Athma Ram. "Traffic flow models." Journal of Tikapur Multiple Campus 4, no. 4 (2018): 68–74. http://dx.doi.org/10.3126/jotmc.v4i4.70252.

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Traffic flow is the study of the movement of individual drivers and vehicles between two points and the interactions they make with one another.Traffic flow models can be used to simulate traffic, for instance to evaluate ex-ante the use of a new part of the infrastructure. Models can be categorized based on, firstly, representation of the traffic flow in terms of flows (macroscopic), groups of drivers (macroscopic) or individual drivers (microscopic) and, secondly, underlying behavioral theory, which can be based on characteristics of the flow (macroscopic) or individual drivers (microscopic

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9

Seton-Rogers, Sarah. "Altered traffic flow." Nature Reviews Cancer 15, no. 10 (2015): 574. http://dx.doi.org/10.1038/nrc4021.

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10

Jones, W. D. "Forecasting traffic flow." IEEE Spectrum 38, no. 1 (2001): 90–91. http://dx.doi.org/10.1109/6.901153.

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11

Pogrebnyak, M. A. "Traffic Flow Model." Mathematical Models and Computer Simulations 15, no. 3 (2023): 436–44. http://dx.doi.org/10.1134/s2070048223030146.

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12

Ganesh, V., M. Mohansairam, P. Manoj Kumar, K. Laxmi Bharadwaj, and Md Asif Baba. "A Study on Saturated Traffic Flow at Signalized Intersections Under Mixed Traffic Conditions." International Journal of Research Publication and Reviews 4, no. 3 (2023): 2939–46. http://dx.doi.org/10.55248/gengpi.2023.4.33649.

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13

Junevičius, Raimundas, and Marijonas Bogdevičius. "DETERMINATION OF TRAFFIC FLOW PARAMETERS IN DIFFERENT TRAFFIC FLOW INTERACTION CASES." TRANSPORT 22, no. 3 (2007): 236–39. http://dx.doi.org/10.3846/16484142.2007.9638131.

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Modelling of straight road section consisting of one traffic line gives the opportunity to simulate “follow the car” system. In general it looks like a line of vehicles, going one after another. Kinetic theory, used in this paper describes traffic flow system as a straight unbroken line with limited flow speed and concentration. Such model also gives the opportunity to derive traffic lines intersections. For example, intersection could be derived like a point with traffic lines coming and outgoing from this point by only changing boundary conditions. Mathematical model is built using character

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14

Ahmed, Afzal, Mir Shabbar Ali, and Toor Ansari. "Modelling Heterogeneous and Undisciplined Traffic Flow using Cell Transmission Model." International Journal of Traffic and Transportation Management 02, no. 01 (2020): 01–05. http://dx.doi.org/10.5383/jttm.02.01.001.

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This research calibrates Cell Transmission Model (CTM) for heterogeneous and non-lane disciplined traffic, as observed in Pakistan and some other developing countries by constructing a flow-density fundamental traffic flow diagram. Currently, most of the traffic simulation packages used for such heterogonous and non-lane-disciplined traffic are not calibrated for local traffic conditions and most of the traffic flow models are developed for comparatively less heterogeneous and lane-disciplined traffic. The flow-density fundamental traffic flow diagram is developed based on extensive field data

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15

Shulong, Dong, Li Chenchen, Fu Yuan, Wang Xiaolin, and Song Ziwen. "The effect of traffic signal countdown on traffic flow." Journal of Scientific and Engineering Research 8, no. 7 (2021): 90–95. https://doi.org/10.5281/zenodo.10608820.

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<strong>Abstract</strong> In order to reveal the impact of signal countdown on traffic operation, this paper quantifies its substantial impact on traffic operation from both efficiency and safety. It is found that the impact on traffic flow is different in two different situations: low saturation flow and high saturation flow the same. Therefore, based on the conclusion of this study, this paper proposes the reference conditions for the signal countdown setting, which provides a theoretical basis for the setting of the countdown, and improves the safety and traffic efficiency of the intersecti

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16

Eissler, Christian, and Stefan Kaufmann. "Model calibration to simulate driving recommendations for traffic flow optimization in oversaturated city traffic." International Journal of Traffic and Transportation Management 02, no. 02 (2020): 01–08. http://dx.doi.org/10.5383/jttm.02.02.001.

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Long queues at signals cause fuel-consuming stop-and-go traffic. Empirical measurements have shown that driving behaviour can have an important impact on queue length and thus on the occurrence of stop and go traffic. This led to the question of whether even a few vehicles can have a measurable influence on the traffic situation in congested city traffic. In this work we use a complete microscopic spatiotemporal measurement of congested city traffic at a signal to i) calibrate a both longitudinal and latitudinal driving model and then to ii) examine how changes in single vehicle's driving beha

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17

Lembhe, Pankaj. "Dynamic Toll Pricing Models and Traffic Flow Optimization." International Journal of Science and Research (IJSR) 9, no. 11 (2020): 1716–22. http://dx.doi.org/10.21275/sr24314032137.

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18

Royko, Yuriy, Yurii Yevchuk, and Romana Bura. "Minimization of traffic delay in traffic flows with coordinated control." Transport technologies 2021, no. 2 (2021): 30–41. http://dx.doi.org/10.23939/tt2021.02.030.

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The method and results of transport research, carried out by field research method, on the determination of the main indicators of traffic flows with significant unevenness of the movement on the arterial street in conditions of coordinated control is reviewed in the paper. Time parameters of traffic light control for which a reduction in traffic delay is achieved in direct and opposite traffic flow by the change of permissive signal depending on traffic intensity are determined using the simulation method. Change (increase) of the duration of the permissive signal provides uninterrupted movem

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19

Khan, Sarosh I., and Pawan Maini. "Modeling Heterogeneous Traffic Flow." Transportation Research Record: Journal of the Transportation Research Board 1678, no. 1 (1999): 234–41. http://dx.doi.org/10.3141/1678-28.

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20

Kåhrström, Christina Tobin. "eDNA directs traffic flow." Nature Reviews Microbiology 11, no. 8 (2013): 508. http://dx.doi.org/10.1038/nrmicro3082.

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21

Helbing, Dirk. "Fundamentals of traffic flow." Physical Review E 55, no. 3 (1997): 3735–38. http://dx.doi.org/10.1103/physreve.55.3735.

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22

Kronjäger, Winfried, and Peter Konhäuser. "Applied Traffic Flow Simulation." IFAC Proceedings Volumes 30, no. 8 (1997): 777–80. http://dx.doi.org/10.1016/s1474-6670(17)43916-4.

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23

Mueller, K. L. "Regulating TLR Traffic Flow." Science 339, no. 6122 (2013): 885. http://dx.doi.org/10.1126/science.339.6122.885-c.

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24

Fulari, Shrikant, Ajitha Thankappan, Lelitha Vanajakshi, and Shankar Subramanian. "Traffic flow estimation at error prone locations using dynamic traffic flow modeling." Transportation Letters 11, no. 1 (2017): 43–53. http://dx.doi.org/10.1080/19427867.2016.1271761.

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25

Chen, Li, Linjiang Zheng, Jie Yang, Dong Xia, and Weining Liu. "Short-term traffic flow prediction: From the perspective of traffic flow decomposition." Neurocomputing 413 (November 2020): 444–56. http://dx.doi.org/10.1016/j.neucom.2020.07.009.

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26

Kaleniuk, Maksym, Oleg Furman, and Taras Postranskyy. "Influence of traffic flow intensity on environmental noise pollution." Transport technologies 2021, no. 1 (2021): 39–49. http://dx.doi.org/10.23939/tt2021.01.039.

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The modern urban environment, with the development of industry, the growth of the vehicle's number on the roads, and the increase in the density of buildings, is increasingly capable of negatively affect the health and well-being of the city's population. Among the factors influencing the environment is noise pollution, namely man-made noise - unwanted and harmful sounds created as a result of human activities. Today, noise is one of the most common factors of pollution among all others. The most common source of noise pollution is transport, including cars and trucks, buses, railways, airplan

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27

Yamuna, S. "Study of Traffic Flow Characteristics for Heterogeneous Traffic." IOSR Journal of Engineering 4, no. 5 (2014): 41–51. http://dx.doi.org/10.9790/3021-04514151.

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28

AKIYAMA, Takamasa, and Chun Fu SHAO. "Traffic Information Service and Fuzzy Traffic Flow Analysis." Journal of Japan Society for Fuzzy Theory and Systems 11, no. 2 (1999): 246–58. http://dx.doi.org/10.3156/jfuzzy.11.2_62.

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29

Lohmiller, Jochen. "Mixed Traffic Brings Traffic Flow to a Standstill." ATZ worldwide 124, no. 5 (2022): 42–45. http://dx.doi.org/10.1007/s38311-022-0814-y.

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30

Zhang, H. M., and W. L. Jin. "Kinematic Wave Traffic Flow Model for Mixed Traffic." Transportation Research Record: Journal of the Transportation Research Board 1802, no. 1 (2002): 197–204. http://dx.doi.org/10.3141/1802-22.

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The Lighthill-Whitham-Richards kinematic wave traffic flow model was extended to describe traffic with different types of vehicles, in which all types of vehicles are completely mixed and travel at the same group velocity. A study of such a model with two vehicle classes (e.g., passenger cars and trucks) showed that when both classes of traffic have identical freeflow speeds, the model (a) satisfies the first-in-first-out rule, (b) is anisotropic, and (c) has the usual shock and expansion waves and a family of contact waves. Different compositions of vehicle classes in this model propagate alo

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31

Kushchenko, L., and R. Dubrov. "Traffic flow modelling to reduce traffic block process." Актуальные направления научных исследований XXI века: теория и практика 3, no. 4 (2015): 356–59. http://dx.doi.org/10.12737/13965.

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32

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

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33

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

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34

Chronopoulos, A., A. Lyrintzis, P. Michalopoulos, C. Rhee, and P. Yi. "Traffic flow simulation through high order traffic modelling." Mathematical and Computer Modelling 17, no. 8 (1993): 11–22. http://dx.doi.org/10.1016/0895-7177(93)90150-w.

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35

Mohamed, S. Sawah, Aly Taie Shereen, Hasan Ibrahim Mohamed, and A. Hussein Shereen. "An accurate traffic flow prediction using long-short term memory and gated recurrent unit networks." Bulletin of Electrical Engineering and Informatics 12, no. 3 (2023): 1806~1816. https://doi.org/10.11591/eei.v12i3.5080.

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Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this

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36

Iwo, E.O., and S.E. Egop. "Evaluation and Prediction of Vehicular Flow in Port Harcourt." Journal of Transportation Engineering and Traffic Management 6, no. 2 (2025): 33–38. https://doi.org/10.5281/zenodo.15104377.

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<em>The growing number of vehicles, particularly those used for public transportation, is causing a drop in vehicle flow on the city's main roadways, resulting in a gradual increase in travel times. In Abuloma, Artillery, Borikiri, Choba, Iwofe, Mile 3 and Woji, as well as various intersections, bus stops, and overpasses, the average speed of all vehicle types was determined during the course of a 4-week study. This was accomplished by manually counting traffic at various road sections for 15 minutes, covering a distance of 0.035 km during peak hours, which are 7 am - 10 am and 3 pm - 6 pm. Th

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37

Slimani, Nadia, Ilham Slimani, Nawal Sbiti, and Mustapha Amghar. "Machine Learning and statistic predictive modeling for road traffic flow." International Journal of Traffic and Transportation Management 03, no. 01 (2021): 17–24. http://dx.doi.org/10.5383/jttm.03.01.003.

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Traffic forecasting is a research topic debated by several researchers affiliated to a range of disciplines. It is becoming increasingly important given the growth of motorized vehicles on the one hand, and the scarcity of lands for new transportation infrastructure on the other. Indeed, in the context of smart cities and with the uninterrupted increase of the number of vehicles, road congestion is taking up an important place in research. In this context, the ability to provide highly accurate traffic forecasts is of fundamental importance to manage traffic, especially in the context of smart

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38

Xiaoqing, Wang, Zhao Pengsheng, Fu Yuan, Dong Shulong, Du Yingcui, and Liu Benxing. "Traffic Flow Prediction for Different Flow Directions at Intersections." Journal of Scientific and Engineering Research 9, no. 8 (2022): 22–32. https://doi.org/10.5281/zenodo.10527816.

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<strong>Abstract</strong> As the number of motor vehicles increases year by year, urban traffic problems are becoming more and more prominent, and the traffic efficiency at intersections is low. In this paper, BP neural network and LSTM neural network are used to predict the traffic in different flow directions of the intersection, and the corresponding signal timing optimization is carried out according to the predicted value. Then use SUMO software to simulate the signal timing scheme before and after optimization. Finally, the LSTM neural network prediction result is the best, and the avera

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39

Botirbek, Hamrakulov. "EVALUATION OF TRANSPORT FLOW USING DEEP LEARNING METHODS OF SATELLITE IMAGES." International Journal of Advance Scientific Research 4, no. 4 (2024): 74–78. http://dx.doi.org/10.37547/ijasr-04-04-13.

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The exponential growth of urbanization has led to increasing problems in traffic management, which require innovative solutions for efficient estimation of traffic flow. Deep learning methods are emerging as a powerful tool for processing and analyzing satellite images, and are being used for traffic flow estimation. This paper describes deep learning-based methods for traffic flow estimation using satellite imagery.

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40

Polishuk, Volodymyr, Stanislav Popov, Inna Vyhovska, Serhii Yanishevskiy, and Liudmyla Nahrebelna. "Energy-based approach to the assessment of traffic flow." Transport technologies 2024, no. 2 (2024): 23–32. http://dx.doi.org/10.23939/tt2024.02.023.

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This article focuses on modeling vehicle acceleration noise in different road conditions, emphasizing urban, highway, and rural roads in Ukraine. Acceleration noise, which refers to the fluctuations in a vehicle's acceleration, is a critical factor in vehicle safety, fuel efficiency, and driving comfort. The research aims to improve current vehicle dynamics models by integrating multi-body dynamics and machine learning algorithms, allowing for more precise predictions of acceleration variability in real-time. The study is based on the existing literature, showing that road surface quality sign

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41

Jiang, Jixiao, Anastasia Alexandrovna Feofilova, Anastasia Gennad’evna Shevtsova, and Victoria Vladimirovna Vasilyeva. "TRAFFIC FLOW PREDICTION BASED ON CONVOLUTIONAL NEURAL NETWORK MODEL." World of transport and technological machines 87, no. 4-1 (2024): 126–33. https://doi.org/10.33979/2073-7432-2024-4-1(87)-126-133.

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Traffic flow prediction mainly uses traffic flow data obtained by intelligent transportation systems to predict future traffic flows to better plan traffic. Because the traffic flow prediction model based on neural network can predict the traffic status of a single road section very well. Therefore, in view of the complex and uncertain characteristics of urban road traffic flow, this paper uses traffic flow short-term prediction theory and convolutional neural network (CNN) to analyze and predict urban road traffic flow.

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42

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 (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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43

Zhuang, Siqing, Yihua Liu, Weihao Wang, Shaojie Guo, and Daiheng Ni. "Traffic Flow Theory for Waterway Traffic: Current Challenges and Countermeasures." Journal of Marine Science and Engineering 12, no. 12 (2024): 2254. https://doi.org/10.3390/jmse12122254.

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Researchers are increasingly turning to roadway traffic flow theory to propose effective solutions for challenges such as traffic congestion and low efficiency in waterway transportation. However, since roadway traffic flow theory was originally developed for highway transportation, its direct application to waterways raises questions due to the inherent differences between the two modes of transportation. Meanwhile, research results and methodologies from other transportation modes can provide valuable insights for studying waterway traffic flow theory. Addressing these questions is essential

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44

Guo, Yanhong, Huan Zhou, Zihan Wan, Junjie Yang, Xiaohong Wang, and Juan Li. "Road traffic flow prediction based on neural Network." Mathematical Modeling and Algorithm Application 2, no. 2 (2024): 49–54. http://dx.doi.org/10.54097/zp8ch042.

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The key to the implementation of urban traffic flow guidance system is to forecast the road traffic flow. This thesis mainly studies the prediction of traffic flow by neural network and the logical prediction of traffic flow. When the traffic flow information is regarded as a time series, the traffic flow can be regarded as a random time series, using the correlation between the data and the internal connection between the adjacent data. BP algorithm has a strong ability to deal with nonlinear problems, imitate bionic learning and self-organization, and occupies a certain advantage in dealing

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45

Hosseini, Seyed Hadi, Behzad Moshiri, Ashkan Rahimi-Kian, and Babak Nadjar Araabi. "Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction." PROMET - Traffic&Transportation 26, no. 5 (2014): 393–403. http://dx.doi.org/10.7307/ptt.v26i5.1429.

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Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was tested against data loss, changes in weather conditions, traffic congestion, and accidents. The outcomes we

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46

Zha, Yi Hui. "Intelligent Control System on Traffic Flow under the Keep-Right-Except-to-Pass Rule." Advanced Materials Research 951 (May 2014): 19–24. http://dx.doi.org/10.4028/www.scientific.net/amr.951.19.

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This paper studied the traffic flow under the keep-right-except-to-pass rule on freeways and the effect of intelligent control system on traffic flow. Through the analysis of the basic traffic flow equation, we developed the traffic flow-traffic density model, obtained the maximum traffic flow, determined the model parameters and tested the model. We defined an indicator reflecting of the degree of traffic flow distribution according to the Gini coefficient in economics and built an effect evaluation model to study the effect of intelligent control system on traffic flow.

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47

Xu, Zheng, Jiaqiang Yuan, Liqiang Yu, Guanghui Wang, and Mingwei Zhu. "Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management." International Journal of Computer Science and Information Technology 2, no. 1 (2024): 18–27. http://dx.doi.org/10.62051/ijcsit.v2n1.03.

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With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex tempora

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48

Dong, Yu Bo. "Discussion on Urban Road Traffic Congestion Algorithm for Automatically Determining." Advanced Materials Research 926-930 (May 2014): 3790–93. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.3790.

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Compared with the expressway, most of the traffic flow in urban road network can be denoted as interrupted traffic flow. Based on the current employed equipment for traffic flow collection and traffic signal control in urban roads, different types of traffic flow in urban roads are analyzed with the traffic flow arrival/departure model in transportation engineering. Mathematical models complying with traffic flow changes are utilized to match the traffic flow in both entry and exit road blocks, thus, enabled the automatic detection of traffic incident. This algorithm provides a measurement for

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49

Ke, Wang. "Combined Traffic Flow Prediction Based on Graph Convolution." International Journal of Computer Applications Technology and Research 11, no. 05 (2022): 170–74. http://dx.doi.org/10.7753/ijcatr1105.1004.

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Traffic flow data has strong temporal and spatial correlation. The traffic flow in the previous moment will affect the traffic flow in the next moment, and the traffic flow in the upstream and downstream will affect each other in space. . In order to alleviate traffic congestion and improve the accuracy of traffic flow prediction, this paper proposes a combined traffic flow prediction model C GCN based on graph convolution. product to extract the temporal features of the traffic flow. The experimental results show that the prediction effect of the C- GCN combination prediction model is better.

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50

Gu, Jian, Zining Zeng, Shun Li, Wei Jing, and Fuan Huang. "The Application of Cusp Catastrophe Theory to Analyze Road Traffic Potential Characteristics, Considering Headway, with Urban Data." Mathematics 13, no. 4 (2025): 652. https://doi.org/10.3390/math13040652.

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Local data are essential for the analysis of abrupt conditions in traffic flow. In this research, we address the shortcomings of real-time traffic flow detection data, which inadequately represent the density characteristics of vehicular traffic. To mitigate this issue, we establish mathematical relationships among traffic flow density, time headway, and vehicle speed, based on the fundamental characteristics of traffic flow dynamics. This framework allows for the estimation of density across various road segments. Recognizing the nonlinear dynamics that characterize traffic flow, we introduce

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