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Intelligent Driver Model
In traffic flow modeling, the intelligent driver model (IDM) is a time-continuous car-following model for the simulation of freeway and urban traffic. It was developed by Treiber, Hennecke and Helbing in 2000 to improve upon results provided with other "intelligent" driver models such as Gipps' model, which loses realistic properties in the deterministic limit. Model definition As a car-following model, the IDM describes the dynamics of the positions and velocities of single vehicles. For vehicle \alpha, x_\alpha denotes its position at time t, and v_\alpha its velocity. Furthermore, l_\alpha gives the length of the vehicle. To simplify notation, we define the ''net distance'' s_\alpha := x_ - x_\alpha - l_, where \alpha - 1 refers to the vehicle directly in front of vehicle \alpha, and the velocity difference, or ''approaching rate'', \Delta v_\alpha := v_\alpha - v_. For a simplified version of the model, the dynamics of vehicle \alpha are then described by the following two o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Traffic Flow
In mathematics and transportation engineering, traffic flow is the study of interactions between travellers (including pedestrians, cyclists, drivers, and their vehicles) and infrastructure (including highways, signage, and traffic control devices), with the aim of understanding and developing an optimal transport network with efficient movement of traffic and minimal traffic congestion problems. History Attempts to produce a mathematical theory of traffic flow date back to the 1920s, when Frank Knight first produced an analysis of traffic equilibrium, which was refined into John Glen Wardrop, Wardrop's first and second principles of equilibrium in 1952. Nonetheless, even with the advent of significant computer processing power, to date there has been no satisfactory general theory that can be consistently applied to real flow conditions. Current traffic models use a mixture of empirical and Deductive reasoning, theoretical techniques. These models are then developed into Trans ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Discrete Time And Continuous Time
In mathematical dynamics, discrete time and continuous time are two alternative frameworks within which variables that evolve over time are modeled. Discrete time Discrete time views values of variables as occurring at distinct, separate "points in time", or equivalently as being unchanged throughout each non-zero region of time ("time period")—that is, time is viewed as a discrete variable. Thus a non-time variable jumps from one value to another as time moves from one time period to the next. This view of time corresponds to a digital clock that gives a fixed reading of 10:37 for a while, and then jumps to a new fixed reading of 10:38, etc. In this framework, each variable of interest is measured once at each time period. The number of measurements between any two time periods is finite. Measurements are typically made at sequential integer values of the variable "time". A discrete signal or discrete-time signal is a time series consisting of a sequence of quantities. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Car-following Model
Microscopic traffic flow models are a class of scientific models of vehicular traffic dynamics. In contrast, to macroscopic models, microscopic traffic flow models simulate single vehicle-driver units, so the dynamic variables of the models represent microscopic properties like the position and velocity of single vehicles. Car-following models Also known as ''time-continuous models'', all car-following models have in common that they are defined by ordinary differential equations describing the complete dynamics of the vehicles' positions x_\alpha and velocities v_\alpha. It is assumed that the input stimuli of the drivers are restricted to their own velocity v_\alpha, the net distance (bumper-to-bumper distance) s_\alpha = x_ - x_\alpha - \ell_ to the leading vehicle \alpha-1 (where \ell_ denotes the vehicle length), and the velocity v_ of the leading vehicle. The equation of motion of each vehicle is characterized by an acceleration function that depends on those input stimuli ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Gipps' Model
Gipps' model is a mathematical model for describing car-following behaviour by motorists in the United Kingdom. The model is named after Peter G. Gipps who developed it in the late-1970s under S.R.C. grants at the Transport Operations Research Group at the University of Newcastle-Upon-Tyne and the Transport Studies Group at the University College London. Gipps' model is based directly on driver behavior and expectancy for vehicles in a stream of traffic. Limitations on driver and vehicle parameters for safety purposes mimic the traits of vehicles following vehicles in the front of the traffic stream. Gipps' model is differentiated by other models in that Gipps uses a timestep within the function equal to \tau to reduce the computation required for numerical analysis. Introduction The method of modeling individual cars along a continuous space originates with Chandler et al. (1958), Gazis et al. (1961), Lee (1966) and Bender and Fenton (1972),Gipps, P. G. 1981 A behavioural car ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ordinary Differential Equation
In mathematics, an ordinary differential equation (ODE) is a differential equation whose unknown(s) consists of one (or more) function(s) of one variable and involves the derivatives of those functions. The term ''ordinary'' is used in contrast with the term partial differential equation which may be with respect to ''more than'' one independent variable. Differential equations A linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form :a_0(x)y +a_1(x)y' + a_2(x)y'' +\cdots +a_n(x)y^+b(x)=0, where , ..., and are arbitrary differentiable functions that do not need to be linear, and are the successive derivatives of the unknown function of the variable . Among ordinary differential equations, linear differential equations play a prominent role for several reasons. Most elementary and special functions that are encountered in physics and applied mathematics are ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Ordinary Differential Equations
In mathematics, an ordinary differential equation (ODE) is a differential equation whose unknown(s) consists of one (or more) function(s) of one variable and involves the derivatives of those functions. The term ''ordinary'' is used in contrast with the term partial differential equation which may be with respect to ''more than'' one independent variable. Differential equations A linear differential equation is a differential equation that is defined by a linear polynomial in the unknown function and its derivatives, that is an equation of the form :a_0(x)y +a_1(x)y' + a_2(x)y'' +\cdots +a_n(x)y^+b(x)=0, where , ..., and are arbitrary differentiable functions that do not need to be linear, and are the successive derivatives of the unknown function of the variable . Among ordinary differential equations, linear differential equations play a prominent role for several reasons. Most elementary and special functions that are encountered in physics and applied mathematics are ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Runge–Kutta Methods
In numerical analysis, the Runge–Kutta methods ( ) are a family of implicit and explicit iterative methods, which include the Euler method, used in temporal discretization for the approximate solutions of simultaneous nonlinear equations. These methods were developed around 1900 by the German mathematicians Carl Runge and Wilhelm Kutta. The Runge–Kutta method The most widely known member of the Runge–Kutta family is generally referred to as "RK4", the "classic Runge–Kutta method" or simply as "the Runge–Kutta method". Let an initial value problem be specified as follows: : \frac = f(t, y), \quad y(t_0) = y_0. Here y is an unknown function (scalar or vector) of time t, which we would like to approximate; we are told that \frac, the rate at which y changes, is a function of t and of y itself. At the initial time t_0 the corresponding y value is y_0. The function f and the initial conditions t_0, y_0 are given. Now we pick a step-size ''h'' > 0 and define: ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Idm Rungekutta
IDM may refer to: Science and information * Identity management, the management of the identity life cycle of an entity ** Novell Identity Manager software, now called NetIQ Identity Manager * Integrated data management * Integrated document management * Integrated device manufacturer, a type of semiconductor company which designs, manufactures, and sells integrated circuit products * Integrated Direct Metering, a real-time TTL metering method for ambient and flashlight employed by the Pentax LX * Intelligent device management, a type of enterprise software applications * Intelligent driver model, a microscopic traffic flow model * Internet Download Manager, a closed source software download manager Organizations * IDM (ISP), also known as IncoNet-Data Management S.A.L., an internet service provider * IDM Computer Solutions, creators of the UltraEdit text editor * Impact Direct Ministries, a non-profit organization * Institute for Disease Modeling, epidemiological research organi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Euler's Method
In mathematics and computational science, the Euler method (also called forward Euler method) is a first-order numerical procedure for solving ordinary differential equations (ODEs) with a given initial value. It is the most basic explicit method for numerical integration of ordinary differential equations and is the simplest Runge–Kutta method. The Euler method is named after Leonhard Euler, who treated it in his book ''Institutionum calculi integralis'' (published 1768–1870). The Euler method is a first-order method, which means that the local error (error per step) is proportional to the square of the step size, and the global error (error at a given time) is proportional to the step size. The Euler method often serves as the basis to construct more complex methods, e.g., predictor–corrector method. Informal geometrical description Consider the problem of calculating the shape of an unknown curve which starts at a given point and satisfies a given differential equ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Traffic Wave
Traffic waves, also called stop waves, ghost jams, traffic snakes or traffic shocks, are traveling disturbances in the distribution of cars on a highway. Traffic waves travel backwards relative to the cars themselves. Relative to a fixed spot on the road the wave can move with, or against the traffic, or even be stationary (when the wave moves away from the traffic with exactly the same speed as the traffic). Traffic waves are a type of traffic jam. A deeper understanding of traffic waves is a goal of the physical study of traffic flow, in which traffic itself can often be seen using techniques similar to those used in fluid dynamics. It is related to the accordion effect. Mitigation It has been saidTraffic Wave Experiments William J. Beaty, 1998 that by knowing how traffic waves are created, drivers can sometimes reduce their effects ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Newell's Car-following Model
In traffic flow theory, Newell’s car-following model is a method used to determine how vehicles follow one another on a roadway. The main idea of this model is that a vehicle will maintain a minimum space and time gap between it and the vehicle that precedes it. Thus, under congested conditions, if the leading car changes its speed, the following vehicle will also change speed at a point in time-space along the traffic wave speed, ''-w''.Newell G.F. (2002) A simplified car-following theory: a lower order model. Institute of Transportation Studies, University of California, Berkeley. Overview Assuming the fundamental diagram (flow-density) is a triangular function, a traffic state ''A'' with speed ''vA'' and density ''kA'' can be assumed in the congestion region. The density on the roadway can be determined using the spacing between vehicles and is computed simply the equation: ''kA = 1/sA'' Geometric relations from the fundamental diagram can be used to calculate the density ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Microscopic Traffic Flow Model
Microscopic traffic flow models are a class of scientific models of vehicular traffic dynamics. In contrast, to macroscopic models, microscopic traffic flow models simulate single vehicle-driver units, so the dynamic variables of the models represent microscopic properties like the position and velocity of single vehicles. Car-following models Also known as ''time-continuous models'', all car-following models have in common that they are defined by ordinary differential equations describing the complete dynamics of the vehicles' positions x_\alpha and velocities v_\alpha. It is assumed that the input stimuli of the drivers are restricted to their own velocity v_\alpha, the net distance (bumper-to-bumper distance) s_\alpha = x_ - x_\alpha - \ell_ to the leading vehicle \alpha-1 (where \ell_ denotes the vehicle length), and the velocity v_ of the leading vehicle. The equation of motion of each vehicle is characterized by an acceleration function that depends on those input stimul ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
