scholarly journals Cellular Automata Intersection Model

2020 ◽  
Vol 5 ◽  
Author(s):  
Tim Peter Erich Vranken ◽  
Michael Schreckenberg
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Variable Number ◽  
Urban Traffic ◽  
Traffic Data ◽  
Traffic Lights ◽  
Car Following ◽  
Time Value ◽  
Intersection Model ◽  
Flow Through

This paper introduces a cellular automaton design of intersections and defines rules to model traffic flow through them, so that urban traffic can be simulated. The model is able to simulate an intersection of up to four streets crossing. Each street can have a variable number of lanes. Furthermore, each lane can serve multiple purposes at the same time, like allowing vehicles to keep going straight or turn left and/or right. The model also allows the simulation of intersections with or without traffic lights and slip lanes. A comparison to multiple empirical intersection traffic data shows that the model is able to realistically reproduce traffic flow through an intersection. In particular, car following times in free flow and the required time value for drivers that turn within the intersection or go straight through it are reproduced. At the same time, important empirical jam characteristics are retained.

Development of Multiregime Speed–Density Relationships by Cluster Analysis

2005 ◽  
Vol 1934 (1) ◽  
pp. 64-71 ◽  
Author(s):  
Lu Sun ◽  
Jie Zhou
Keyword(s):  
Cluster Analysis ◽  
Traffic Flow ◽  
Ad Hoc ◽  
Data Sets ◽  
Traffic Data ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
Data Segmentation ◽  
Essential Components ◽  
Real Traffic

Empirical speed–density relationships are important not only because of the central role that they play in macroscopic traffic flow theory but also because of their connection to car-following models, which are essential components of microscopic traffic simulation. Multiregime traffic speed– density relationships are more plausible than single-regime models for representing traffic flow over the entire range of density. However, a major difficulty associated with multiregime models is that the breakpoints of regimes are determined in an ad hoc and subjective manner. This paper proposes the use of cluster analysis as a natural tool for the segmentation of speed–density data. After data segmentation, regression analysis can be used to fit each data subset individually. Numerical examples with three real traffic data sets are presented to illustrate such an approach. Using cluster analysis, modelers have the flexibility to specify the number of regimes. It is shown that the K-means algorithm (where K represents the number of clusters) with original (nonstandardized) data works well for this purpose and can be conveniently used in practice.

scholarly journals CELLULAR AUTOMATA SIMULATION OF TRAFFIC LIGHT STRATEGIES IN OPTIMIZING THE TRAFFIC FLOW

2005 ◽  
Vol 4 (2) ◽  
pp. 13
Author(s):  
E. H. NUGRAHANI ◽  
R. RAMDHANI
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Control Strategies ◽  
Velocity Model ◽  
Flow Density ◽  
Traffic Light ◽  
Cellular Automata Model ◽  
Optimal Velocity ◽  
Traffic Lights ◽  
Traffic System

The behavior of traffic system controlled by traffic lights is presented under assumptions of city traffic on a single lane based on the optimal velocity model.  The effect of differrent traffic light control strategies on the traffic flow is discussed using three different strategies, i.e. the synchronized, green wave, and random offset strategies. The flow-density diagrams are analyzed using these strategies.   The numerical investigation is carried out using cellular automata model to get a better understanding of the microscopic behavior of the system under the three different traffic light strategies.

Two Dimensional Cellular Automaton Model of the Mixed Traffic Flow for Urban Traffic

2012 ◽  
Vol 241-244 ◽  
pp. 2082-2087
Author(s):  
Li Yang ◽  
Jun Hui Hu ◽  
Ling Jiang Kong
Keyword(s):  
Boundary Conditions ◽  
Cellular Automaton ◽  
Traffic Flow ◽  
Flow Model ◽  
Urban Traffic ◽  
Mixed Traffic ◽  
Traffic Flow Model ◽  
Traffic Lights ◽  
Mixed Traffic Flow ◽  
Transit Traffic

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 network and boundary conditions. Under certain traffic light cycle time, the traffic flow has a periodical oscillation change. The comparison to practical measured data shows that our stimulation results are accordant with the changes of real traffic flow, which confirms the accuracy and rationality of our model.

scholarly journals Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

2014 ◽  
Vol 26 (5) ◽  
pp. 393-403 ◽  
Author(s):  
Seyed Hadi Hosseini ◽  
Behzad Moshiri ◽  
Ashkan Rahimi-Kian ◽  
Babak Nadjar Araabi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Weather Conditions ◽  
Traffic Data ◽  
Data Loss ◽  
Traffic Lights ◽  
Traffic Flow Prediction ◽  
Traffic Flow Forecasting ◽  
Flow Prediction ◽  
Forecasting Method

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 were highly acceptable for all cases and showed the robustness of the proposed flow forecasting method.

Real-Time Traffic Signal Control Based on Kalman Filtering Theory

2015 ◽  
Vol 713-715 ◽  
pp. 915-918
Author(s):  
Yuan Xin Xu ◽  
Wan Ying Yang ◽  
Wen Shi
Keyword(s):  
Kalman Filter ◽  
Traffic Flow ◽  
Control Method ◽  
Urban Traffic ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Traffic Flows ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Stable Signal

Aiming at the problem that individual control of urban traffic lights and stable signal timing. This paper proposed a real timing control method of traffic lights which based on Kalman filter. This method use Kalman filter to predict the next time traffic flows and then update the signal timing. By field researching the traffic flow of intersection in peak hour and predicting the traffic flow. Then update the signal timing. Meanwhile using the VISSIM to simulate the intersection. The result of the simulation shows that the length of vehicle queue decreased significantly and the number of stops dropped. The efficiency of access has been greatly improved.

Real-Time Monitoring Unit of Traffic Flow

2012 ◽  
Vol 588-589 ◽  
pp. 1058-1061
Author(s):  
Ting Zhang ◽  
Zhan Wei Song
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Traffic ◽  
Traffic Information ◽  
Real Time Monitoring ◽  
Traffic Lights ◽  
Real Time Traffic ◽  
Transit Use ◽  
Control Traffic

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.

scholarly journals Research on the Impacts of Generalized Preceding Vehicle Information on Traffic Flow in V2X Environment

2021 ◽  
Vol 13 (4) ◽  
pp. 88
Author(s):  
Xiaoyuan Wang ◽  
Junyan Han ◽  
Chenglin Bai ◽  
Huili Shi ◽  
Jinglei Zhang ◽  
...  
Keyword(s):  
Cellular Automata ◽  
Traffic Flow ◽  
Free Flow ◽  
Traffic Information ◽  
Flow State ◽  
Simulation Framework ◽  
Car Following ◽  
Preceding Vehicle ◽  
Car Following Model ◽  
Vehicle Information

With the application of vehicles to everything (V2X) technologies, drivers can obtain massive traffic information and adjust their car-following behavior according to the information. The macro-characteristics of traffic flow are essentially the overall expression of the micro-behavior of drivers. There are some shortcomings in the previous researches on traffic flow in the V2X environment, which result in difficulties to employ the related models or methods in exploring the characteristics of traffic flow affected by the information of generalized preceding vehicles (GPV). Aiming at this, a simulation framework based on the car-following model and the cellular automata (CA) is proposed in this work, then the traffic flow affected by the information of GPV is simulated and analyzed utilizing this framework. The research results suggest that the traffic flow, which is affected by the information of GPV in the V2X environment, would operate with a higher value of velocity, volume as well as jamming density and can maintain the free flow state with a much higher density of vehicles. The simulation framework constructed in this work can provide a reference for further research on the characteristics of traffic flow affected by various information in the V2X environment.

scholarly journals Exploiting witness for traffic simulation

2014 ◽  
Vol 1 (1) ◽  
pp. 12-24
Author(s):  
Azhar Ismail ◽  
Muhammad Latif ◽  
Mian Awai
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Waiting Times ◽  
Simulation Software ◽  
Peak Time ◽  
Traffic Lights ◽  
Traffic Intersection ◽  
Flow Through ◽  
Alternative Scenarios

Traffic congestion in urban cities is an increasing problem. Not only does it lead to an increase in pollution, but the time spent waiting in traffic queues wastes valuable time in addition to causing frustration. A system that can control and manage traffic efficiently is one way that this issue can be reduced.A specific road traffic intersection in South Manchester, UK, was selected for investigation as it experiences high levels of traffic flow through it during the evening peak time. This has led to large queues and long waiting times due to the fixed timings of the traffic lights. This paper explores strategies to better control the traffic flow through it. A model of the selected traffic junction has been built using Witness simulation software. Data for this junction has been obtained partially from observations and mostly from traffic surveys enabling a simulation of the traffic flow. Analysing the results allowed two alternative scenarios to be developed and simulated. Results from one of the scenarios showed noticeable reductions in the average queue waiting times at the traffic junction.

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