scholarly journals The forecast and analysis of toll channel scale of expressway toll station

2021 ◽  
Vol 283 ◽  
pp. 02016
Author(s):  
Guanzheng Pang
Keyword(s):  
Traffic Flow ◽  
Rapid Growth ◽  
Traffic Congestion ◽  
Theory Model ◽  
Normal Operation ◽  
Common Phenomenon ◽  
Highway Traffic ◽  
Average Delay ◽  
Traffic Flow Theory ◽  
Toll Station

With the rapid growth of highway traffic, congestion of highway toll station has become a common phenomenon in peak hours. However, the congestion at the toll station has a serious impact on the normal operation of expressway. When the queuing vehicles overflow the toll station, the queue leader will lose control, and the operation of the main line vehicles will be seriously disturbed. This paper analyzes the causes of queuing phenomenon in toll stations, studies the characteristics of different toll channels and traffic flow, and then establishes the traffic flow theory model, and classifies the toll stations according to the average delay.

SHORT TERM TRAFFIC FLOW PREDICTION IN HETEROGENEOUS CONDITION USING ARTIFICIAL NEURAL NETWORK

Transport ◽  
2013 ◽  
Vol 30 (4) ◽  
pp. 397-405 ◽  
Author(s):  
Kranti Kumar ◽  
Manoranjan Parida ◽  
Vinod Kumar Katiyar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Volume ◽  
Flow Data ◽  
Highway Traffic ◽  
Short Term ◽  
Artificial Neural ◽  
Input Variables

Traffic congestion is one of the main problems related to transportation in developed as well as developing countries. Traffic control systems are based on the idea to avoid traffic instabilities and to homogenize traffic flow in such a way that risk of accidents is minimized and traffic flow is maximized. There is a need to predict traffic flow data for advanced traffic management and traffic information systems, which aim to influence traveller behaviour, reducing traffic congestion and improving mobility. This study applies Artificial Neural Network for short term prediction of traffic volume using past traffic data. Besides traffic volume, speed and density, the model incorporates both time and the day of the week as input variables. Model has been validated using actual rural highway traffic flow data collected through field studies. Artificial Neural Network has produced good results in this study even though speeds of each category of vehicles were considered separately as input variables.

Modeling traffic flow in work zone sections considering the effect of traffic lights

2021 ◽  
pp. 2150113
Author(s):  
Xingyu Lu ◽  
Li Fei ◽  
Huibing Zhu ◽  
Wangjun Cheng ◽  
Zijie Wang
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Traffic Accidents ◽  
Red Light ◽  
Work Zone ◽  
Traffic Model ◽  
Traffic Density ◽  
Highway Traffic ◽  
Traffic Lights

Based on the two-lane highway traffic model with a work zone presented previously, a new traffic model with a work zone under the control of traffic lights is proposed. The length of the waiting area for vehicles before traffic lights is recommended cautiously after numerical simulation. The relationship between the vehicles’ queuing time and the cycle of traffic lights is studied, and the cycle time of traffic lights is obtained also considering people’s endurance to the red light. It is found that the traffic lights are effective to ease the traffic congestion in the work zone when the density is medium, and help to eliminate the inducement of traffic accidents. On the other hand, the simulation results show that traffic lights are not needed in the work zone when the traffic density is small. It indicates that the traffic flow in the work zone area can be optimized by using appropriate traffic management when the traffic density varies.

scholarly journals How To Improve Urban Intelligent Traffic? A Case Study Using Traffic Signal Timing Optimization Model Based On Swarm Intelligence Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2631
Author(s):  
Xiancheng Fu ◽  
Hengqiang Gao ◽  
Hongjuan Cai ◽  
Zhihao Wang ◽  
Weiming Chen
Keyword(s):  
Traffic Flow ◽  
Optimization Model ◽  
Traffic Congestion ◽  
Delay Time ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Average Delay ◽  
Traffic Capacity ◽  
Traffic Signal Timing

Traffic congestion is a major problem in today’s society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion.

scholarly journals Visualization and Understanding of Traffic Flow and Congestion in India

2020 ◽  
Author(s):  
Tsutomu Tsuboi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Social Issues ◽  
Flow Characteristics ◽  
Flow Theory ◽  
Gujarat State ◽  
Traffic Condition ◽  
Traffic Flow Theory ◽  
Traffic Data Collection ◽  
Real Traffic

This study focuses on traffic condition analysis, especially in under developing country India and makes more visible of traffic flow by using traffic flow theory in order to understand real traffic condition. India is one of rapid economic growing countries and large market with second largest population 1.3 billion in 2018. On the other hand, there are social issues such as environment air pollution and global warming by traffic CO2 emission of transportation. This kind of condition is not only in India, but in other South East Asia and Africa in future. From recent more than one-month traffic observation in a typical major city Ahmedabad in Gujarat state, which has about 8 million population and industrialized city. In terms of traffic data collection, 14 CCTV cameras are used in the city. Based on multiple traffic cameras monitoring, author found the unique traffic flow characteristics and compares traffic flow theory. In this study, it is described what is key parameters to show real traffic congestion condition and how these congestion occurs.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

scholarly journals Formation of the Traffic Flow Rate under the Influence of Traffic Flow Concentration in Time at Controlled Intersections in Tyumen, Russian Federation

2021 ◽  
Vol 13 (15) ◽  
pp. 8324
Author(s):  
Viacheslav Morozov ◽  
Sergei Iarkov
Keyword(s):  
Traffic Flow ◽  
Flow Rate ◽  
Traffic Congestion ◽  
Traffic Management ◽  
System Analysis ◽  
Motor Vehicle ◽  
Experimental Studies ◽  
Transport Systems ◽  
Experiment Planning ◽  
Traffic Light

Present experience shows that it is impossible to solve the problem of traffic congestion without intelligent transport systems. Traffic management in many cities uses the data of detectors installed at controlled intersections. Further, to assess the traffic situation, the data on the traffic flow rate and its concentration are compared. Latest scientific studies propose a transition from spatial to temporal concentration. Therefore, the purpose of this work is to establish the regularities of the influence of traffic flow concentration in time on traffic flow rate at controlled city intersections. The methodological basis of this study was a systemic approach. Theoretical and experimental studies were based on the existing provisions of system analysis, traffic flow theory, experiment planning, impulses, probabilities, and mathematical statistics. Experimental data were obtained and processed using modern equipment and software: Traficam video detectors, SPECTR traffic light controller, Traficam Data Tool, SPECTR 2.0, AutoCad 2017, and STATISTICA 10. In the course of this study, the authors analyzed the dynamics of changes in the level of motorization, the structure of the motor vehicle fleet, and the dynamics of changes in the number of controlled intersections. As a result of theoretical studies, a hypothesis was put forward that the investigated process is described by a two-factor quadratic multiplicative model. Experimental studies determined the parameters of the developed model depending on the directions of traffic flow, and confirmed its adequacy according to Fisher’s criterion with a probability of at least 0.9. The results obtained can be used to control traffic flows at controlled city intersections.

Research of the Temporary Parking Acceleration-Spacing Model Based on Swarm Theory

2013 ◽  
Vol 361-363 ◽  
pp. 2113-2116
Author(s):  
Jin Xin Cao ◽  
Lei Wang ◽  
Wei Li Zhang ◽  
Jun Wu
Keyword(s):  
Traffic Flow ◽  
Potential Field ◽  
Traffic Congestion ◽  
Field Method ◽  
Lagrange Method ◽  
Traffic Jams ◽  
Model Based ◽  
Potential Field Method ◽  
Swarm Theory

The disturbance factors in the traffic flow may lead to traffic congestion. The agglomeration characteristics shown in traffic jams are similar to the biological swarm characteristics. In this paper, acceleration-spacing model is established based on the potential field method and the Lagrange method. The vehicle in front is viewed as the main force source. Then the data of the traffic congestion caused by the temporary parking in front of the school are used to calibrate the parameters of the model. It can be verified that the model is effective.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

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