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Journal articles on the topic 'Urban traffic flow'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Guseynov, Sharif E., and Alexander V. Berezhnoy. "MODELLING OF URBAN TRAFFIC FLOW." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 15, 2017): 109. http://dx.doi.org/10.17770/etr2017vol1.2632.

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In this paper non-deterministic motion of urban traffic is studied under certain assumptions. Based on those assumptions discrete and continuous mathematical models are developed: continuous model is written as the Cauchy initial-value problem for the integro-differential equation, whence among other things it is obtained the Fokker-Planck equation. Besides, the sufficient condition ensuring the mathematical legitimacy of the developed continuous model is formulated.

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2

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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3

Chen, Da Shan. "Characteristic Parameters Identification of Macro Traffic Flow on Urban Expressway." Applied Mechanics and Materials 409-410 (September 2013): 1209–12. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1209.

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The macroscopic traffic flow parameters characteristic is an important research content in traffic flow theory. Urban expressway plays an important role in the urban road network. It is gradually shifting from large-scale infrastructure-oriented to refinement of traffic management. With the growing of traffic demand and much more traffic congestion and accidents, integrated active traffic management should be involved in urban expressway management on the back ground of car-road coordination. As the backbone road network, traffic flow characteristic parameters have great value for the control

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4

Liu, Zeyu, and Gongping Yang. "Large-scale traffic flow simulation based on intelligent PSO." MATEC Web of Conferences 355 (2022): 02010. http://dx.doi.org/10.1051/matecconf/202235502010.

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With the rapid development of urban traffic, a large number of vehicles in cities not only bring convenience to people, but also bring a series of traffic problems, including traffic congestion and high traffic accident rates. Driving speed and waiting time of vehicles are two important factors of traffic problems. To simulate the real urban road traffic flow, a one-dimensional traffic flow grid model was proposed, which considered the nearest and next neighbour car at the same time, and connected the front and rear neighbour cars to optimize the traffic flow. The experiment results showed tha

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Zeng, Junwei, Yongsheng Qian, Bingbing Wang, Tingjuan Wang, and Xuting Wei. "The Impact of Traffic Crashes on Urban Network Traffic Flow." Sustainability 11, no. 14 (2019): 3956. http://dx.doi.org/10.3390/su11143956.

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This paper aims to investigate the impact of occasional traffic crashes on the urban traffic network flow. Toward this purpose, an extended model of coupled Nagel–Schreckenberg (NaSch) and Biham–Middleton–Levine (BML) models is presented. This extended model not only improves the initial conditions of the coupled models, but also gives the definition of traffic crashes and their spatial/time distribution. Further, we simulated the impact of the number of traffic crashes, their time distribution, and their spatial distribution on urban network traffic flow. This research contributes to the comp

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Yu, Yi, Hui Gong, and Xianglun Mo. "Analysis of Traffic Flow Equilibrium in Urban Road Network Based on Floating Vehicle Data." E3S Web of Conferences 261 (2021): 03026. http://dx.doi.org/10.1051/e3sconf/202126103026.

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Based on the floating vehicle data, this paper analyzes the equilibrium of urban road network traffic flow. This can guide traffic flow distribution and provide reliable basis for traffic control. This paper starts with the quantitative analysis of the traffic network equilibrium, on the basis of verifying the validity of the floating car data, divides the urban road network into regions, and constructs an analysis model of the traffic flow equilibrium of the urban road network. The urban road traffic distribution model is constructed in accordance with the number of road traffic segments. On

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7

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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Liu, Xuelian, Huazhi Yuan, Jie Hu, and Xinlong Jiao. "Single-Point Adaptive Control Method for Urban Mixed Traffic Flow." Journal of Advanced Transportation 2020 (September 14, 2020): 1–7. http://dx.doi.org/10.1155/2020/8827824.

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In urban traffic, motor vehicles, nonmotor vehicles, pedestrians, and other traffic are mixed, which seriously affects the urban traffic efficiency. At the intersections of urban roads especially, the priority setting of various travel modes is crucial to improve the urban traffic efficiency. In the urban hybrid traffic system, the architecture and algorithm flowchart of the single-point adaptive control system for urban hybrid traffic flow are presented. The control effect of this method is verified by VISSIM software. The simulation results show that the traffic efficiency of the priority ob

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Qin, Jiayu, Gang Mei, and Lei Xiao. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning." Sustainability 13, no. 1 (2020): 266. http://dx.doi.org/10.3390/su13010266.

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Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an act

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Abramova, Liudmyla, Valerii Shyrin, Hennadii Ptytsia, and Serhii Kapinus. "Dynamic control over traffic flow under urban traffic conditions." Eastern-European Journal of Enterprise Technologies 4, no. 3 (106) (2020): 34–43. http://dx.doi.org/10.15587/1729-4061.2020.210170.

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11

Li, Lifang. "Research on Traffic Flow Mathematical Model in Urban Traffic." International Journal of Smart Home 9, no. 11 (2015): 123–32. http://dx.doi.org/10.14257/ijsh.2015.9.11.14.

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Guo, Qiangqiang, and Xuegang (Jeff) Ban. "Network multiscale urban traffic control with mixed traffic flow." Transportation Research Part B: Methodological 185 (July 2024): 102963. http://dx.doi.org/10.1016/j.trb.2024.102963.

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Liudmyla, Abramova, Shyrin Valerii, Ptytsia Hennadii, and Kapinus Serhii. "DYNAMIC CONTROL OVER TRAFFIC FLOW UNDER URBAN TRAFFIC CONDITIONS." Eastern-European Journal of Enterprise Technologies 4, no. 3 (106) (2020): 34–43. https://doi.org/10.15587/1729-4061.2020.210170.

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This article deals with solving topical issues of improving traffic effectiveness in major cities. The main traffic problem in major cities is a decrease in the throughput capacity of a street-road network and an increase in unpredictable travel time. The conducted study determined that the main reason for a decrease in the throughput capacity of a street-road network is the existence of traffic congestion modes and the formation of a "shock wave" with its spreading toward the oncoming traffic flow. To solve this problem, the dynamics of a change of parameters of a transport flow bas

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14

Yang, Tianrun. "Simulation of Urban Dynamic Traffic Network Based on Greedy Algorithm." Highlights in Science, Engineering and Technology 78 (December 26, 2023): 214–24. http://dx.doi.org/10.54097/0bywgb71.

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This study explores the intricate dynamics of urban traffic flow under varying degrees of intersection and road section carrying capacity. We simulate a city-wide traffic network and examine the impact of extreme situations such as a decline in urban road carrying capacity on the entire traffic system. Our experimental procedure involved simulating traffic flow through intersections and road sections, artificially reducing their maximum carrying capacity, and observing the resultant changes in traffic patterns. Our data reveals a robust correlation between the decrease in carrying capacity and

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Maghrour Zefreh, Mohammad, and Adam Torok. "Theoretical Comparison of the Effects of Different Traffic Conditions on Urban Road Traffic Noise." Journal of Advanced Transportation 2018 (October 15, 2018): 1–11. http://dx.doi.org/10.1155/2018/7949574.

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Road traffic noise is one of the most relevant sources in the environmental noise pollution of the urban areas where dynamics of the traffic flow are much more complicated than uninterrupted traffic flows. It is evident that different traffic conditions would play the role in the urban traffic flow considering the dynamic nature of the traffic flow on one hand and presence of traffic lights, roundabouts, etc. on the other hand. The main aim of the current paper is to investigate the effect of different traffic conditions on urban road traffic noise. To do so, different traffic conditions have

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Wang, Liang, Yu Jie Wang, and Ling Yu Wang. "Simulation Research of Flow and Speed on Urban Expressway Interchange Entrance." Advanced Materials Research 779-780 (September 2013): 796–99. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.796.

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The urban expressway overpass entrance is an important node of the urban road system. Traffic jams often happen at entrance. The characteristics of urban expressway entrance and the advantages of the microscopic traffic simulation were combined to analyze capacity of entrance. VISSIM simulation software was used and the validity of the simulation model was verified. The influence of the main road traffic flow and the desired speed of entrance to flow and speed of the urban expressway entrance were analyzed through simulation experiment. On the whole, traffic capacity of urban expressway entran

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17

Zhao, P. X., and S. M. Zhao. "UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 263–68. http://dx.doi.org/10.5194/isprs-archives-xli-b2-263-2016.

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In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Exp

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Zhao, P. X., and S. M. Zhao. "UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 263–68. http://dx.doi.org/10.5194/isprsarchives-xli-b2-263-2016.

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In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Exp

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19

Lekariap, Edwin Mararo, Zachary C. Abiero Gariy, and Timothy Nyomboi. "The Influences of Macroscopic Modeling of Traffic Dynamics in Urban Road Segments Considering Side Friction Influences: A Case Study." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 4 (2024): 28–36. http://dx.doi.org/10.35940/ijrte.d8167.13041124.

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Urban road networks are essential arteries of modern cities, facilitating the movement of people and goods. However, efficient traffic flow management in urban areas remains a critical challenge, often compounded by factors like side friction. This research article presents a comprehensive macroscopic modeling approach to understand and incorporate the effects of side friction on traffic flow in the context of Nakuru City, Kenya. Nakuru City, like many growing urban centres, grapples with traffic congestion and associated issues, demanding innovative solutions for sustainable urban mobility. S

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Edwin, Mararo Lekariap. "The Influences of Macroscopic Modeling of Traffic Dynamics in Urban Road Segments Considering Side Friction Influences: A Case Study." International Journal of Recent Technology and Engineering (IJRTE) 13, no. 4 (2024): 28–36. https://doi.org/10.35940/ijrte.D8167.13041124.

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<strong>Abstract:</strong> Urban road networks are essential arteries of modern cities, facilitating the movement of people and goods. However, efficient traffic flow management in urban areas remains a critical challenge, often compounded by factors like side friction. This research article presents a comprehensive macroscopic modeling approach to understand and incorporate the effects of side friction on traffic flow in the context of Nakuru City, Kenya. Nakuru City, like many growing urban centres, grapples with traffic congestion and associated issues, demanding innovative solutions for su

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21

Ji, Yitong. "Short-Term Traffic Flow Prediction: Techniques and Methods." Applied and Computational Engineering 111, no. 1 (2024): 230–34. https://doi.org/10.54254/2755-2721/2024.ch18112.

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With the development of the economy, the expansion of the city scale, the increase in the population of urban residents and the increase in the per capita car ownership, the traffic congestion problem has an increasing impact on urban operation. Especially during the peak period of commuting traffic and holidays in large cities, most cities are facing the serious problem of road congestion. In order to facilitate the travel of residents and reduce air pollution, it is very important to keep the main road of the city smooth and rapid passage of vehicles. Therefore, cities should vigorously deve

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22

Li, Yue, and Hua Pu Lu. "Research on Traffic Flow Characteristics of Urban Expressway." Applied Mechanics and Materials 587-589 (July 2014): 2036–39. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2036.

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Urban expressways play an important role in urban traffic networks. Different from the highway speed-flow-density relationship, the urban expressway has more complicate characteristics. With the traffic stream data collected from loop detectors on different sites of the expressway in Beijing, traffic parameters of traffic speed, density and volume are analyzed. As the relationship of speed flow and density changes remarkably during different period of time, this paper establish a speed-flow-density model during nighttime and daytime separately. The relationship among traffic density, speed and

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23

Liao, Zhuhua, Haokai Huang, Yijiang Zhao, Yizhi Liu, and Guoqiang Zhang. "Analysis and Forecast of Traffic Flow between Urban Functional Areas Based on Ride-Hailing Trajectories." ISPRS International Journal of Geo-Information 12, no. 4 (2023): 144. http://dx.doi.org/10.3390/ijgi12040144.

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Urban planning and function layout have important implications for the journeys of a large percentage of commuters, which often make up the majority of daily traffic in many cities. Therefore, the analysis and forecast of traffic flow among urban functional areas are of great significance for detecting urban traffic flow directions and traffic congestion causes, as well as helping commuters plan routes in advance. Existing methods based on ride-hailing trajectories are relatively effective solution schemes, but they often lack in-depth analyses on time and space. In the paper, to explore the r

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Zhang, Ya, Mingming Lu, and Haifeng Li. "Urban Traffic Flow Forecast Based on FastGCRNN." Journal of Advanced Transportation 2020 (September 27, 2020): 1–9. http://dx.doi.org/10.1155/2020/8859538.

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Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal depend

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Li, Dazhou, Chuan Lin, Wei Gao, Zeying Chen, Zeshen Wang, and Guangqi Liu. "Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction." Wireless Communications and Mobile Computing 2020 (June 4, 2020): 1–15. http://dx.doi.org/10.1155/2020/6896579.

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Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network a

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Zhang, Ting, and Zhan Wei Song. "Real-Time Monitoring Unit of Traffic Flow." Advanced Materials Research 588-589 (November 2012): 1058–61. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1058.

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With the sustained growth of vehicle ownerships, traffic congestion has become obstacle of urban development. In addition to developing public transport and accelerating the construction of rail transit, use scientific managing and controlling method in real-time monitoring traffic flow to divert the traffic stream is an effective way to solve urban traffic problems. In this paper, cross-correlation algorithm is used to obtain real-time traffic information, such as capacity and occupancy of a lane, so as to control traffic lights intelligently.

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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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Mahona, John N. P., Cuthbert F. Mhilu, Joseph Kihedu, and Hannibal Bwire. "Effects of static bottlenecks on traffic flow in urban road network." International Journal of Engineering, Science and Technology 12, no. 3 (2020): 1–15. http://dx.doi.org/10.4314/ijest.v12i3.1.

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Existing traffic flow models do not consider the effects of road static bottlenecks on traffic flow. In this paper, a modified macroscopic continuum model for traffic flow on urban road network with static bottlenecks is presented. The model takes into account the fluctuations of traffic flow considering static bottlenecks during the morning peak period. The model results show that existence of static road bottlenecks with various configurations cause traffic flow instabilities. This phenomenon lead into stop-and-go traffic flow conditions under the moderate density and reduction of the traffi

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Liu, Fengkai, Xingmin Ma, Xingshuo An, and Guangnan Liang. "Urban Traffic Flow Prediction Model with CPSO/SSVM Algorithm under the Edge Computing Framework." Wireless Communications and Mobile Computing 2020 (September 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/8871998.

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Urban traffic flow prediction has always been an important realm for smart city build-up. With the development of edge computing technology in recent years, the network edge nodes of smart cities are able to collect and process various types of urban traffic data in real time, which leads to the possibility of deploying intelligent traffic prediction technology with real-time analysis and timely feedback on the edge. In view of the strong nonlinear characteristics of urban traffic flow, multiple dynamic and static influencing factors involved, and increasing difficulty of short-term traffic fl

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Tan, Shuang, and Lu Zhang. "Analysis of Urban Road Network Robustness under Different Attack Conditions." Environmental Science & Sustainable Development 9, no. 4 (2024): 63–71. https://doi.org/10.21625/essd.v9i4.1131.

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In preventing large-scale congestion caused by urban road traffic attacks, both random and deliberate attack methods were employed, with the robustness of the traffic system analyzed using a cascading failure model. The details of the proposed methods are, firstly, a road network model is constructed based on a Geographic Information System (GIS), and the traffic flow data of a provincial city during peak traffic hours is obtained by Python. Then, the flow data of Origin-Destination (OD) pairs for the road network under normal operation is obtained using the OD estimation module in TransCAD, t

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Zhao, Qing Yu, Li Hua Chen, Zhi Jun Gao, and Jin Xin Cao. "Research on the Urban Traffic Flow and Traffic Problems in Hohhot." Applied Mechanics and Materials 587-589 (July 2014): 2053–56. http://dx.doi.org/10.4028/www.scientific.net/amm.587-589.2053.

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With a sustained growth of cars, many urban traffic problems caused by cars should be solved as soon as possible. However, basic urban traffic investigation and analysis of current situation is the the basis and the key to solve the problems. In this paper, investigation of traffic volume and pot speed is done at DongYing Road in Hohhot, and basic data base is established. Some significant parameters of urban traffic are calculated, and current traffic situation of the investigated road is detailedly analysed and studied. What is more, some problems found during the investigation are proposed

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Qu, Rui. "Research on Optimization Algorithm for Urban Traffic Flow Based on Computer Simulation." Academic Journal of Science and Technology 7, no. 3 (2023): 65–68. http://dx.doi.org/10.54097/ajst.v7i3.12719.

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With the acceleration of urbanization, the problem of urban traffic congestion is becoming increasingly prominent. In order to address this issue, this paper proposes an optimization algorithm for urban traffic flow based on computer simulation. The algorithm simulates urban traffic flow, analyzes traffic bottlenecks and congestion points, and proposes corresponding optimization strategies. Through experiments and data analysis, the effectiveness and feasibility of the algorithm are verified. The achievements of this research have important implications for urban traffic management and optimiz

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Cao, Xiangyang, Bingzhong Zhou, Qiang Tang, Jiaqi Li, and Donghui Shi. "Urban Wasteful Transport and Its Estimation Methods." Sustainability 10, no. 12 (2018): 4562. http://dx.doi.org/10.3390/su10124562.

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The paper studies urban road traffic problems from the perspective of resource science. The resource composition of urban road traffic system is analysed, and the road network is proved as a scarce resource in the system resource combination. According to the role of scarce resources, the decisive role of road capacity in urban traffic is inferred. Then the new academic viewpoint of “wasteful transport” was proposed. Through in-depth research, the paper defines the definition of wasteful transport and expounds its connotation. Through the flow-density relationship analysis of urban road traffi

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Bomadandi, Om. "Traffic Flow Prediction in Smart City." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 2256–59. https://doi.org/10.22214/ijraset.2024.65617.

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Traffic flow prediction in smart cities plays a crucial role in enhancing urban mobility, reducing congestion, and optimizing transportation systems. In this study, we leverage deep learning techniques to develop accurate and reliable traffic flow prediction models. We collect and preprocess real-time and historical traffic data from various sources, including traffic sensors, GPS devices, and traffic management systems. Through feature engineering, we extract relevant spatiotemporal features such as time of day, day of week, weather conditions, and historical traffic patterns. We then design

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Li, Y., Q. Zhao, and M. Wang. "ANALYSIS THE INFLUENCING FACTORS OF URBAN TRAFFIC FLOWS BY USING NEW AND EMERGING URBAN BIG DATA AND DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (June 2, 2022): 537–43. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-537-2022.

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Abstract. Urban traffic analysis has acted an important role in the process of urban development, which can provide insights for urban planning, traffic management and resource allocation. Meanwhile, the advancement of Intelligent Transportation Systems has produced a variety of traffic-related data from sensors and cameras to monitor urban traffic conditions in high spatio-temporal resolution. This research applies spatial regression models combined with computer vision and deep learning to analyse traffic flow distributions via various factors in the urban areas and traffic flow data. We inc

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Zhang, Kai, Zixuan Chu, Jiping Xing, Honggang Zhang, and Qixiu Cheng. "Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model." Mathematics 11, no. 19 (2023): 4075. http://dx.doi.org/10.3390/math11194075.

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Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend

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Berlotti, Mariaelena, Sarah Di Grande, and Salvatore Cavalieri. "Proposal of a Machine Learning Approach for Traffic Flow Prediction." Sensors 24, no. 7 (2024): 2348. http://dx.doi.org/10.3390/s24072348.

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Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised cluster

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Ju, Yong Feng, and Xiao Wei Wei. "Study on Urban Intersection Traffic Flow Forecasting and Traffic Signal Control." Applied Mechanics and Materials 135-136 (October 2011): 969–74. http://dx.doi.org/10.4028/www.scientific.net/amm.135-136.969.

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Short-traffic flow forecasting is an important part of ITS, and its accuracy and real-time is directly related to the effect of traffic control and traffic induce. Gathering and analyzing the real-time data of urban road network ,short-time traffic flow forecasting could estimate the state of traffic flow for a few minutes in future and provide support to intelligent transportation control, so it is one of the important premise for ITS.

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Chen, Jing, Guowei Yang, Zhaochong Zhang, and Wei Wang. "ST-D3DDARN: Urban traffic flow prediction based on spatio-temporal decoupled 3D DenseNet with attention ResNet." PLOS ONE 19, no. 6 (2024): e0305424. http://dx.doi.org/10.1371/journal.pone.0305424.

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Urban traffic flow prediction plays a crucial role in intelligent transportation systems (ITS), which can enhance traffic efficiency and ensure public safety. However, predicting urban traffic flow faces numerous challenges, such as intricate temporal dependencies, spatial correlations, and the influence of external factors. Existing research methods cannot fully capture the complex spatio-temporal dependence of traffic flow. Inspired by video analysis in computer vision, we represent traffic flow as traffic frames and propose an end-to-end urban traffic flow prediction model named Spatio-temp

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Yang, Qing-fang, Ru-ru Xing, Li-li Zheng, and Shu-xing Wang. "Quantitative Analysis of Urban Regional Traffic Status." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/2184397.

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In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this pr

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Wang, Wenyi. "Attention-LSTM Based Modelling for Hangzhou City Metro Passenger Flow Forecasting Study." Applied and Computational Engineering 117, no. 1 (2024): 31–41. https://doi.org/10.54254/2755-2721/2024.18304.

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With the expanding scale of urbanisation, urban traffic problems are increasing, and traffic flow prediction is often of great significance as the core of intelligent transport system (ITS) to solve traffic problems. This paper firstly analyses the temporal and spatial characteristics of the urban traffic flow prediction problem, and for the temporal and spatial characteristics, respectively, establishes a long and short-term memory network LSTM urban traffic flow prediction model based on the attention mechanism; finally, based on the kaggle dataset, the Hangzhou metro passenger flow dataset

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Adewumi, Aderemi, Jimmy Kagamba, and Alex Alochukwu. "Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/5656734.

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In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with

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Wang, Fu Jian, Wei Wei, Dong Fang Ma, and Dian Hai Wang. "Flow-Density Relationship for Discontinuous Flow on Urban Street Network." Applied Mechanics and Materials 505-506 (January 2014): 999–1004. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.999.

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The flow-density model for discontinuous flow on urban street network under under-saturated condition was studied in this paper. According to the concept of two-fluid theory, vehicles in the traffic stream were divided into two classes: stopped (for traffic signal) and moving. The amount of stopped and moving vehicles was estimated by applying fixed number theory and traffic flow theory. Then the model described flow-density relationship was derived on the basis of the concept of space-mean density. After that, impacts of the parameters in the model on the flow-density relationship are analyze

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Fu, Fengjie, Dianhai Wang, Meng Sun, Rui Xie, and Zhengyi Cai. "Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval." Sustainability 16, no. 5 (2024): 1818. http://dx.doi.org/10.3390/su16051818.

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Predicting short-term urban traffic flow is a fundamental and cost-effective strategy in traffic signal control systems. However, due to the interrupted, periodic, and stochastic characteristics of urban traffic flow influenced by signal control, there are still unresolved issues related to the selection of the optimal aggregation time interval and the quantifiable uncertainties in prediction. To tackle these challenges, this research introduces a method for predicting urban interrupted traffic flow, which is based on Bayesian deep learning and considers the optimal aggregation time interval.

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Yu, Chen, Jiajie Zhang, Dezhong Yao, Ruiguo Zhang, and Hai Jin. "Speed-Density Model of Interrupted Traffic Flow Based on Coil Data." Mobile Information Systems 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7968108.

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As a fundamental traffic diagram, the speed-density relationship can provide a solid foundation for traffic flow analysis and efficient traffic management. Because of the change in modern travel modes, the dramatic increase in the number of vehicles and traffic density, and the impact of traffic signals and other factors, vehicles change velocity frequently, which means that a speed-density model based on uninterrupted traffic flow is not suitable for interrupted traffic flow. Based on the coil data of urban roads in Wuhan, China, a new method which can accurately describe the speed-density re

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Ballari, Syed Omar. "Area Occupancy Characteristics in Traffic Flow on Urban Highway: A Case Study." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (2019): 18–26. http://dx.doi.org/10.5373/jardcs/v11i10/20193001.

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Qin, Zhen Hai. "The Urban Road Short-Term Traffic Flow Prediction Research." Applied Mechanics and Materials 423-426 (September 2013): 2954–56. http://dx.doi.org/10.4028/www.scientific.net/amm.423-426.2954.

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To predict the future traffic flow status more accurately is of great significance to alleviate urban traffic congestion for a short period of time and avoid the waste of social resources. At first, this paper summarizes the characteristics of urban expressway traffic flow. Then establishes BP neural network short-term traffic flow evaluation model based on MATLAB, and finally through the instance, verify the validity of the model.

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Al-Bahr, Tareq M., Sitti Asmah Hassan, Othman Che Puan, Nordiana Mashros, and Nur Sabahiah Abdul Sukor. "Speed-Flow-Geometric Relationship for Urban Roads Network." Applied Sciences 12, no. 9 (2022): 4231. http://dx.doi.org/10.3390/app12094231.

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Speed on the urban roads is significantly affected by the surrounding geometric and traffic parameters. Based on this fact, we ascertained the impact of the geometric and traffic parameters on the average travel speed of the urban roads network. Herein, 197 urban road segments in Johor (Malaysia) with diverse features were randomly selected. The average travel speed and volume of the traffic on these road segments were measured using the moving observer method (MOM). Meanwhile, these roads’ features were recorded via the direct visual inspection. Various geometric (density of the traffic calmi

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Yang, Li, Jun Hui Hu, and Ling Jiang Kong. "Two Dimensional Cellular Automaton Model of the Mixed Traffic Flow for Urban Traffic." Applied Mechanics and Materials 241-244 (December 2012): 2082–87. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2082.

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Based on the two-dimension cellular automaton traffic flow model (BML model), a mixed traffic flow model for urban traffic considering the transit traffic is established in this paper. Under the don't block the box rules and the opening boundary conditions, the impacts of transit traffic, the central station, traffic lights cycle, the vehicles length on the mixed traffic flow is studied by computer simulation. Some important characters appearing in the new model are also elucidated. It shows that traffic flow is closely related to traffic lights cycle, the geometric structure of transport netw

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Hua Zhang, Xiaoye Wang, and Yingyuan Xiao. "Similarity Research for Urban Intersection Traffic Flow." International Journal of Digital Content Technology and its Applications 6, no. 1 (2012): 439–45. http://dx.doi.org/10.4156/jdcta.vol6.issue1.53.

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