Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models
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Abstract Online traffic flow modeling is of increasing importance due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper, the dynamic flow-density relation is identified based on the classification of traffic states and is quantified employing fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.Keywords:
Three-phase traffic theory
We present results of numerical simulations of the effect of driver behavior on spatiotemporal congested traffic patterns that result from traffic breakdown at an on-ramp bottleneck. The simulations are made with the Kerner-Klenov stochastic traffic flow model in the framework of three-phase traffic theory. Different diagrams of congested patterns at the bottleneck associated with different driver behavioral characteristics are found and compared each other. An adaptive cruise control (ACC) in the framework of three-phase traffic theory introduced by the author (called a "driver alike ACC" (DA-ACC)) is discussed. The effect of DA-ACC-vehicles on traffic flow, in which without the DA-ACC-vehicles traffic congestion occurs at the bottleneck, is numerically studied. We show that DA-ACC-vehicles improve traffic flow considerably without any reduction in driving comfort. It is found that there is a critical percentage of DA-ACC-vehicles in traffic flow: If the percentage of the DA-ACC-vehicle exceeds the critical one no traffic breakdown occurs at the bottleneck. A criticism of a recent "criticism of three-phase traffic theory" is presented
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The traffic flow problem is spread analyzed by many authors like Liu et al., Nagatani and Kaur et al. This paper investigates the traffic flow problem that occurs due to the incorrect traffic lights control. Traffic lights should be match with the traffic flow and must to optimize it. The rational traffic lights period is set by the analysis of the situation on city’s street. A discrete model of traffic flow is used to obtain the results of traffic flow simulation. The results of modelling allow to analyze the dynamics of transport flows in the context of analyzed road and due to the studied boundary conditions. In this article the discrete model of traffic flow is complemented by new parameters (coefficients), which allows to fit the modelling results with real conditions of traffic flows. Authors argued that the last point of all traffic flows is the main aspect of the investigation of the effect of the changed concentration on the modelling street. Also an amplitude spectrum of traffic flow parameters has been created. This amplitude spectrum helps to determine the frequencies of the road network. DOI: http://dx.doi.org/10.5755/j01.mech.24.6.22477
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VEHICULAR TRAFFIC FLOW HAS BEEN INVESTIGATED THEORETICALLY AND EXPERIMENTALLY IN ORDER THAT PEAK HOUR COLLECTIVE TRAFFIC FLOW DYNAMICS CAN BE UNDERSTOOD AND THAT THE PEAK HOUR FLOW THROUGH THE CALLAHAN TUNNEL CAN BE IMPROVED BY MEANS OF TRAFFIC FLOW CONTROL AND MODIFICATION. TWO THEORETICAL MODELS ARE SUGGESTED, THE FINITE REACTION TIME MODEL AND THE ASYMMETRICAL RESPONSE MODEL, AS PREDICTIVE OF OBSERVED TRAFFIC DENSITY DYNAMICS, WAVE GROWTH, AND ASYMMETRY. EXPERIMENTALLY, A TRAFFIC FLOW PROFILE OF CAPACITIES, VEHICLE SPEEDS, AND TRAFFIC DENSITIES IN THE CALLAHAN TUNNEL HAS BEEN OBTAINED, AND RELATIONSHIPS BETWEEN SLOWDOWN WAVE PHENOMENA AND TRAFFIC FLOW, DETERMINED. BASED ON THESE, IT IS SUGGESTED THAT TRAFFIC FLOW MAY BE IMPROVED WITH TRAFFIC FLOW MODIFICATION PROCEDURES. /NTIS/
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Recently, the modelling of heterogeneous traffic flow has gained significant attention from traffic theorists. The influence of slow vehicles (e.g. trucks) on traffic operations has been studied both from a micro and macroscopic level. Though multiclass traffic models have been successfully developed in literature, few of them are adequately used to describe traffic network operations. To this end, this article aims to propose a model to study the (heterogeneous) traffic network operations based on the macroscopic modelling approach. More specifically, on the one hand, we introduce an extension of the classic Lighthill–Whitham–Richards model to describe multiclass traffic operations in the network. The proposed model is based on solving a system of hyperbolic partial differential equations describing multiclass traffic dynamics with discontinuous fluxes. On the other hand, a dynamic routing algorithm is applied to determine the turning flow at nodes based on the information provision of the current network situation. Numerical results have shown that the proposed model can capture some real traffic phenomena in multiclass traffic networks.
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Traffic flow is a kind of many-body system of strongly interacting vehicles. Traffic jams are a typical signature of the complex behavior of vehicular traffic. Various mathematical models are presented to understand the rich variety of physical phenomena exhibited by traffic. This paper provides an overview of what is currently the state-of-the-art with respect to traffic flow theory. Starting with a brief history about vehicular traffic flows, it discusses the Greenshields, Greenberg’s, Gurein’s, etc., models. This paper also discusses some basic relations between traffic flow characteristics, i.e., the fundamental diagrams; speed, volume, and density relationships; hydrodynamic analogies; and traffic hump formation (shock wave), and sheds some light on the different points of view adopted by the traffic engineering community. Moving on, it reviews some performance indicators that allow one to assess the quality of traffic operations. A final part of this paper gives the probabilistic description of traffic flow, distribution of vehicles on a road.
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Traffic flow is a kind of many-body system of strongly interacting vehicles. Traffic jams are a typical signature of the complex behavior of vehicular traffic. Various mathematical models are presented to understand the rich variety of physical phenomena exhibited by traffic. This paper provides an overview of what is currently the state of the art with respect to traffic flow theory. Starting with a brief history about vehicular traffic flows, this paper discusses the Greenshields, Greenberg, and Gurein, etc., models. This paper also discusses some basic relations between traffic flow characteristics, i.e., the fundamental diagrams; speed, volume, and density relationships; hydrodynamic analogies; and traffic hump formation (shock wave). It also sheds some light on the different points of view adopted by the traffic engineering community. Some performance indicators are reviewed that allow assessment of the quality of traffic operations. A final part of this paper gives the probabilistic description of traffic flow, distribution of vehicles on a road.
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With the development of near term automatic vehicles following concepts such as intelligent cruise control (ICC) and cooperative driving, vehicles will be able to automatically follow each other in the longitudinal direction. The modeling of traffic flow consisting of such vehicles is important for analyzing the effects of vehicle automation on the characteristics of traffic flow and for suggesting macroscopic control strategies to improve efficiency. Such analysis may also suggest ways for modifying the vehicle control characteristics in order to improve the macroscopic behavior of traffic. In this paper, we developed a mesoscopic and macroscopic model that describes the automated traffic-flow dynamics in a single highway lane. The mesoscopic model describes the speed and density continuously in time and space and at the same time retains the microscopic characteristics of traffic flow. The macroscopic model describes the average speed and density at each section of the lane and at each point in time. Even though the macroscopic model does not retain the microscopic characteristics of the vehicular traffic, computationally it is much simpler than the mesoscopic one. Simulations are used to demonstrate the effectiveness of these models in describing traffic-flow characteristics. The developed models indicate some similarities, but also some fundamental differences with existing traffic-flow models for manually driven vehicles.
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The purpose of this paper is to discover how Cellular Automata (CA) can be applied to traffic flow simulations. First, we introduce the three types of traffic model: microscopic traffic model, macroscopic traffic model and mesoscopic traffic model. Second, to evaluate dynamic traffic flow, we developed a traffic flow simulator that uses cellular automata model. We extend the existing CA models to describe the influence of a car accident in single-lane and double-lane traffic flow model. We also add the lane changing rules to simulate the reality traffic condition. By simulation, we analyze all possible situations. The simulation was implemented in Matlab programming language. 1. Main Features of Traffic Stream Traffic phenomena are an important question in modern society. Investigating on regular pattern of traffic flow has significant meaning. Analysis and simulation of traffic flow can be wildly applied in Transportation Planning, Traffic Control and Traffic Engineering. In the early 90S, New York City government decided to construct the tunnel to New Jersey. After analyzed and modeled the traffic flow, they adjusted traffic management strategy which increased the capacity of current existing facilities. So the tunnel construction was avoided. Traffic stream is complex and nonlinear and defined as multi-dimensional traffic lanes with flow of vehicles over time. Traffic phenomena are complex and nonlinear. Vehicles followed each other on each lane and they can choose different lane when the former position is empty. There are three main characteristics to visualize a traffic stream: speed, density, and flow. 1.1. Speed, Density and Flow Speed (V) is defined as travel distance per unit time in traffic flow. The precise speed of each car is difficult to measure. In practice, we calculate the average speed of the sample vehicles. In a time space diagram, time is measured along the horizontal axis and distance is measured along the vertical axis. The velocity of the traffic stream equals to the slope of the traffic trajectory (v=dx/dt).The figure below shows the nonlinear traffic stream. The common method speed is to calculate the time mean speed. Time mean speed is measured by the average speed of a traffic stream passing a fixed point along a roadway over a fixed period of time. Time mean speed can be sampled by loop detectors and other fixed-location speed detection equipment. The time-mean speed can be calculated as:
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