Generation time - Revision history

en>Bhny: /* top */

2014-12-26T22:21:08Z

top

← Older revision		Revision as of 00:21, 27 December 2014
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	Hi there. ~~good psychic ([http://myfusionprofits.com/groups/find-out-about-self-improvement-tips-here/ click the up coming document])~~ My name is Sophia Meagher ~~although~~ it is not the ~~title~~ on my beginning ~~certificate~~. ~~Office supervising is exactly where~~ her ~~primary income comes from~~. ~~For many years~~ she~~'s been residing in Kentucky but her spouse wants them to transfer~~. ~~What me~~ and ~~my family love is doing ballet but~~ I'~~ve been taking on new things lately~~.<br><br>my blog ~~online [http://ustanford~~.~~com/index~~.~~php?do=/profile-38218/info/ best psychics] [~~[http://~~c045~~.~~danah~~.~~co.kr/home~~/index.php?document_srl=~~1356970~~&mid=~~qna c045.danah.co.kr]~~]		Hi there. My name is Sophia Meagher even though it is not the name on my beginning certification. Some time in the past she selected to reside in Alaska and her mothers and fathers live nearby. Doing ballet is some thing she would by no means give up. I am an invoicing officer and I'll be promoted soon.<br><br>My web blog ... [http://www.010-5260-5333.com/index.php?document_srl=1880&mid=board_ALMP66 online psychic]

64.206.141.46: Corrected a spelling mistake

2014-03-01T00:08:22Z

Corrected a spelling mistake

← Older revision		Revision as of 02:08, 1 March 2014
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	~~{{Unreferenced\|date=February 2007}}~~		Hi there. good psychic ([http://myfusionprofits.com/groups/find-out-about-self-improvement-tips-here/ click the up coming document]) My name is Sophia Meagher although it is not the title on my beginning certificate. Office supervising is exactly where her primary income comes from. For many years she's been residing in Kentucky but her spouse wants them to transfer. What me and my family love is doing ballet but I've been taking on new things lately.<br><br>my blog online [http://ustanford.com/index.php?do=/profile-38218/info/ best psychics] [[http://c045.danah.co.kr/home/index.php?document_srl=1356970&mid=qna c045.danah.co.kr]]
	~~'''Microscopic traffic flow models''' are a class of [[Scientific modeling\|scientific models]] of [[traffic flow\|vehicular traffic dynamics]]~~.

	~~In contrast to~~ [[Macroscopic traffic flow model\|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 <math>x_\alpha</~~math> and velocities <math>v_\alpha<~~/~~math>~~. ~~It is assumed that the input stimuli of the drivers are restricted to their own velocity <math>v_\alpha<~~/~~math>, the net distance (bumper~~-to-~~bumper distance) <math>s_\alpha = x_{\alpha~~-1} - ~~x_\alpha~~ - ~~l_{\alpha~~-~~1}<~~/~~math> to~~ the ~~leading vehicle <math>\alpha-1</math> (where <math>l_{\alpha-1}</math> denotes the vehicle length~~)~~, and the velocity <math>v_{\alpha-1}</math> of the leading vehicle. The [[equation of motion]] of each vehicle~~ is ~~characterized by an acceleration function that depends on those input stimuli:~~

	~~:<math>\ddot{x}_\alpha(t) = \dot{v}_\alpha(t) = F(v_\alpha(t), s_\alpha(t), v_{\alpha-1}(t))</math>~~

	~~In general, the driving behavior of a single driver-vehicle unit <math>\alpha</math> might~~ not ~~merely depend on~~ the ~~immediate leader <math>\alpha-1</math> but~~ on ~~the <math>n_a</math> vehicles in front~~. ~~The equation of motion~~ in ~~this more generalized form reads:~~

	~~:<math>\dot{v}_\alpha(t) = f(x_\alpha(t), v_\alpha(t), x_{\alpha-1}(t), v_{\alpha-1}(t), \ldots, x_{\alpha-n_a}(t), v_{\alpha-n_a}(t))</math>~~

	~~===Examples of car-following models===~~
	* [[Optimal velocity model]] (OVM)
	* [[Velocity difference model]] (VDIFF)
	* [[VISSIM\|Wiedemann model]] (1974)
	* [[Intelligent driver model]] (IDM, 1999)
	* [[Gipps' model]] (Gipps, 1981)

	~~==Cellular automaton models==~~
	~~[[Cellular automaton]] (CA) models use integer variables~~ to ~~describe the dynamical properties of the system~~. ~~The road is divided into sections of a certain length <math>\Delta x</math>~~ and ~~the time~~ is ~~discretized to steps of <math>\Delta t</math>~~. ~~Each road section can either be occupied by a vehicle or empty and the dynamics are given by update rules of the form:~~

	:<~~math~~>~~v_\alpha^{t+1} = f(s_\alpha^t, v_\alpha^t, v_{\alpha-1}^t, \ldots)~~<~~/math~~>
	:~~<math>x_\alpha^{t+1} = x_\alpha^t + v_\alpha^{t+1}</math>~~

	~~(the simulation time <math>t<~~/~~math> is measured in units of <math>\Delta t<~~/~~math> and the vehicle positions <math>x_\alpha<~~/~~math> in units of <math>\Delta x</math>)~~.

	~~The time scale is typically given by the reaction time of a human driver, <math>\Delta t~~ = ~~1 \text{s}<~~/~~math>. With <math>\Delta t<~~/math> fixed, the length of the road sections determines the granularity of the model. At a complete standstill, the average road length occupied by one vehicle is approximately 7.5 meters. Setting <math>\Delta x</~~math> to this value leads to a model where one vehicle always occupies exactly one section of the road and a velocity of 5 corresponds to <math>5 \Delta x~~/~~\Delta t = 135 \text{km~~/~~h}</math>, which is then set to be the maximum velocity a driver wants to drive at~~. ~~However, in such a model, the smallest possible acceleration would be <math>\Delta x/(\Delta t)^2 = 7~~.~~5 \text{m}~~/~~\text{s}^2<~~/~~math> which is unrealistic~~. ~~Therefore, many modern CA models use a finer spatial discretization, for example <math>\Delta x~~ = 1.~~5 \text{m}</math>, leading to a smallest possible acceleration of <math>1.5 \text{m}/\text{s}^2</math>.~~

	~~Although cellular automaton models lack the accuracy of the time-continuous car-following models, they still have the ability to reproduce a wide range of traffic phenomena~~. ~~Due to the simplicity of the models, they are numerically very efficient and can be used to simulate large road networks in realtime or even faster~~.

	~~===Examples of CA models===~~
	* [[Rule 184]]
	* [[Biham–Middleton–Levine traffic model]]
	* [[Nagel–Schreckenberg model]] (NaSch, 1992)

	~~{{DEFAULTSORT:Microscopic Traffic Flow Model}}~~
	~~[[Category:Road traffic management]]~~
	~~[[Category:Mathematical modeling~~]]

en>Cm421: fixed a recent link added

2014-01-16T09:52:36Z

fixed a recent link added

← Older revision		Revision as of 11:52, 16 January 2014
Line 1:		Line 1:
	~~Alyson is what my husband enjoys~~ to ~~call me but I don't~~ like ~~when people use my full title~~. ~~Her family life~~ in ~~Ohio~~. ~~Office supervising~~ is ~~exactly~~ where ~~her main earnings comes from~~. The ~~favorite hobby for him~~ and ~~his kids~~ is to ~~perform lacross~~ and ~~he would never give it up~~.<br><br>~~Here~~ is ~~my homepage~~ ... ~~free psychic; [http:~~//~~black7~~.~~mireene~~.~~com~~/~~aqw~~/~~5741 http:~~/~~/black7~~.~~mireene~~.~~com~~],		{{Unreferenced\|date=February 2007}}
			'''Microscopic traffic flow models''' are a class of [[Scientific modeling\|scientific models]] of [[traffic flow\|vehicular traffic dynamics]].

			In contrast to [[Macroscopic traffic flow model\|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 <math>x_\alpha</math> and velocities <math>v_\alpha</math>. It is assumed that the input stimuli of the drivers are restricted to their own velocity <math>v_\alpha</math>, the net distance (bumper-to-bumper distance) <math>s_\alpha = x_{\alpha-1} - x_\alpha - l_{\alpha-1}</math> to the leading vehicle <math>\alpha-1</math> (where <math>l_{\alpha-1}</math> denotes the vehicle length), and the velocity <math>v_{\alpha-1}</math> of the leading vehicle. The [[equation of motion]] of each vehicle is characterized by an acceleration function that depends on those input stimuli:

			:<math>\ddot{x}_\alpha(t) = \dot{v}_\alpha(t) = F(v_\alpha(t), s_\alpha(t), v_{\alpha-1}(t))</math>

			In general, the driving behavior of a single driver-vehicle unit <math>\alpha</math> might not merely depend on the immediate leader <math>\alpha-1</math> but on the <math>n_a</math> vehicles in front. The equation of motion in this more generalized form reads:

			:<math>\dot{v}_\alpha(t) = f(x_\alpha(t), v_\alpha(t), x_{\alpha-1}(t), v_{\alpha-1}(t), \ldots, x_{\alpha-n_a}(t), v_{\alpha-n_a}(t))</math>

			===Examples of car-following models===
			* [[Optimal velocity model]] (OVM)
			* [[Velocity difference model]] (VDIFF)
			* [[VISSIM\|Wiedemann model]] (1974)
			* [[Intelligent driver model]] (IDM, 1999)
			* [[Gipps' model]] (Gipps, 1981)

			==Cellular automaton models==
			[[Cellular automaton]] (CA) models use integer variables to describe the dynamical properties of the system. The road is divided into sections of a certain length <math>\Delta x</math> and the time is discretized to steps of <math>\Delta t</math>. Each road section can either be occupied by a vehicle or empty and the dynamics are given by update rules of the form:

			:<math>v_\alpha^{t+1} = f(s_\alpha^t, v_\alpha^t, v_{\alpha-1}^t, \ldots)</math>
			:<math>x_\alpha^{t+1} = x_\alpha^t + v_\alpha^{t+1}</math>

			(the simulation time <math>t</math> is measured in units of <math>\Delta t</math> and the vehicle positions <math>x_\alpha</math> in units of <math>\Delta x</math>).

			The time scale is typically given by the reaction time of a human driver, <math>\Delta t = 1 \text{s}</math>. With <math>\Delta t</math> fixed, the length of the road sections determines the granularity of the model. At a complete standstill, the average road length occupied by one vehicle is approximately 7.5 meters. Setting <math>\Delta x</math> to this value leads to a model where one vehicle always occupies exactly one section of the road and a velocity of 5 corresponds to <math>5 \Delta x/\Delta t = 135 \text{km/h}</math>, which is then set to be the maximum velocity a driver wants to drive at. However, in such a model, the smallest possible acceleration would be <math>\Delta x/(\Delta t)^2 = 7.5 \text{m}/\text{s}^2</math> which is unrealistic. Therefore, many modern CA models use a finer spatial discretization, for example <math>\Delta x = 1.5 \text{m}</math>, leading to a smallest possible acceleration of <math>1.5 \text{m}/\text{s}^2</math>.

			Although cellular automaton models lack the accuracy of the time-continuous car-following models, they still have the ability to reproduce a wide range of traffic phenomena. Due to the simplicity of the models, they are numerically very efficient and can be used to simulate large road networks in realtime or even faster.

			===Examples of CA models===
			* [[Rule 184]]
			* [[Biham–Middleton–Levine traffic model]]
			* [[Nagel–Schreckenberg model]] (NaSch, 1992)

			{{DEFAULTSORT:Microscopic Traffic Flow Model}}
			[[Category:Road traffic management]]
			[[Category:Mathematical modeling]]

en>Helpful Pixie Bot: ISBNs (Build KE)

2012-05-09T14:58:48Z

ISBNs (Build KE)

New page

Alyson is what my husband enjoys to call me but I don't like when people use my full title. Her family life in Ohio. Office supervising is exactly where her main earnings comes from. The favorite hobby for him and his kids is to perform lacross and he would never give it up.<br><br>Here is my homepage ... free psychic; [http://black7.mireene.com/aqw/5741 http://black7.mireene.com],