The Traffic Flow Model of Intelligent Transportation System

2015 ◽  
Vol 713-715 ◽  
pp. 2000-2003 ◽  
Author(s):  
Hong Ying Jiao ◽  
Fang Chi Liang ◽  
Yi Rao
Keyword(s):  
Traffic Flow ◽  
Flow Model ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
The Other ◽  
Traffic Flow Model ◽  
Traffic State ◽  
Two Systems

In this paper, we develop models to analyze traffic flow of intelligent transportation system (ITS).The investigation into ITS is carried out in two aspects: one is the partly ITS, the other is the completely ITS. Comparisons between two systems show: with the increasing of intelligence degree, the superiority of each rule becomes more and more obvious. As is mentioned above, each rule is the most ideal for certain traffic state. While the detailed forms of different rules are not the same, the purpose of all rules is to promote the traffic flow. The phenomenon reveals the consistency of the ITS. In another word, the higher the intelligence degree of a system is, the larger its contributions to the traffic flow are.

scholarly journals The Application of Virtual Reality Technology on Intelligent Traffic Construction and Decision Support in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Gongxing Yan ◽  
Yanping Chen
Keyword(s):  
Big Data ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
The Other ◽  
The City

The core of smart city is to build intelligent transportation system.. An intelligent transportation system can analyze the traffic data with time and space characteristics in the city and acquire rich and valuable knowledge, and it is of great significance to realize intelligent traffic scheduling and urban planning. This article specifically introduces the extensive application of urban transportation infrastructure data in the construction and development of smart cities. This article first explains the related concepts of big data and intelligent transportation systems and uses big data to illustrate the operation of intelligent transportation systems in the construction of smart cities. Based on the machine learning and deep learning method, this paper is aimed at the passenger flow and traffic flow in the smart city transportation system. This paper deeply excavates the time, space, and other hidden features. In this paper, the traffic volume of the random sections in the city is predicted by using the graph convolutional neural network (GCNN) model, and the data are compared with the other five models (VAR, FNN, GCGRU, STGCN, and DGCNN). The experimental results show that compared with the other 4 models, the GCNN model has an increase of 8% to 10% accuracy and 15% fault tolerance. In forecasting morning and evening peak traffic flow, the accuracy of the GCNN model is higher than that of other models, and its trend is basically consistent with the actual traffic volume, the predicted results can reflect the actual traffic flow data well. Aimed at the application of intelligent transportation in an intelligent city, this paper proposes a machine learning prediction model based on big data, and this is of great significance for studying the mechanical learning of such problems. Therefore, the research of this paper has a good implementation prospect and academic value.

State-Space Model for Traffic State Estimation of a Two-Dimensional Network

2018 ◽  
Vol 13 (2) ◽  
pp. 326-337
Author(s):  
Yosuke Kawasaki ◽  
Yusuke Hara ◽  
Masao Kuwahara ◽  
◽  
◽  
...  
Keyword(s):  
State Space ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Model ◽  
State Space Model ◽  
Two Dimensional ◽  
Traffic Flow Model ◽  
Space Model ◽  
Traffic State ◽  
2D Network

This study proposes a real-time monitoring method for two-dimensional (2D) networks via the fusion of probe data and a traffic flow model. In the Great East Japan Earthquake occurring on March 11, 2011, there was major traffic congestion as evacuees concentrated in cities on the Sanriku Coast. A tragedy occurred when a tsunami overtook the stuck vehicles. To evacuate safely and efficiently, the state of traffic must be monitored in real time on a 2D network, where all networks are linked. Generally, the traffic state is monitored only at observation points. However, observation data presents the risk of errors. Additionally, in the estimated traffic state of the 2D network, unlike non-intersecting road sections (i.e., one-dimensional), it is necessary to model user route choice behavior and origin/destination (OD) demand to input in the model. Therefore, in this study, we develop a state-space model that assimilates vehicle density and divergence ratio data obtained from probe vehicles in a traffic flow model that considers route choice. Our state-space model considers observational errors in the probe data and can simultaneously estimate traffic state and destination component ratio of OD demand. The result of simulated traffic model verification shows that the proposed model has good congestion estimation precision in a small-scale test network.

Study on the integration effect of multiple vehicles’ delayed velocities on traffic stability in intelligent transportation system environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Geng Zhang ◽  
Qinglu Ma ◽  
Dongbo Pan ◽  
Yu Zhang ◽  
Qiaoli Huang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation System ◽  
Research Result ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Content Type ◽  
Car Following ◽  
Car Following Model ◽  
Multiple Vehicles ◽  
Integration Effect

Purpose In an intelligent transportation system (for short, ITS) environment, a vehicle’s motion is affected by the information in a large scale. The purpose of this paper is to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. Design/methodology/approach This paper constructed a new car-following model to study the integration effect of multiple vehicles’ delayed velocities on traffic flow. The new model is analyzed by linear and nonlinear perturbation method theoretically and also verified by simulation. Findings It is found out that the integration of preceding vehicles’ delayed velocities affect the stability of traffic flow importantly, and three preceding vehicles’ delayed velocities information should be considered in real traffic. Originality/value The new car-following model by considering the integration effect of multiple vehicles’ delayed velocities is firstly proposed in this paper. The research result shows that three preceding vehicles’ delayed velocities information is the best choice to stabilizing traffic flow.

Mixing Microscopic and Macroscopic Representations of Traffic Flow: Hybrid Model Based on Lighthill–Whitham–Richards Theory

2003 ◽  
Vol 1852 (1) ◽  
pp. 193-200 ◽  
Author(s):  
Emmanuel Bourrel ◽  
Jean-Baptiste Lesort
Keyword(s):  
Traffic Flow ◽  
Hybrid Model ◽  
Flow Model ◽  
The Other ◽  
Model Coupling ◽  
Traffic Flow Model ◽  
Richards Model ◽  
Transmission Of Information ◽  
Unique Model ◽  
Made In

The hybrid traffic flow model, coupling a microscopic (vehicle-based) and a macroscopic (flow-based) representation of traffic flow, may be a useful tool to better understand the relationships between the various types of representation. It can also be a basis for implementing various model extensions, which may be easier with using one type of representation or the other. The hybrid model presented here combines a flow and a vehicular representation of the same model, which is the classical Lighthill–Whitham–Richards model. The use of a simple and unique model makes it possible to focus on the specific problems raised by translating boundary conditions from vehicular to flow formulation and conversely. This translation is made in order to ensure the conservation of flow and proper transmission of information both downstream and upstream and to minimize the perturbations induced by transitions between a continuous and a discrete representation of flow. The resulting model is shown to have good properties, particularly concerning congestion propagation and flow smoothing at the interfaces between the two models.

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