Four-step travel model - Revision history

Edasmalchi: add/cite image

2019-12-05T22:10:58Z

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			[[File:4stepmodel.png\|frame\|Illustration of the Four-step travel model <ref>Metropolitan Washington Council of Governments. Accessed December 2019. https://www.mwcog.org/transportation/data-and-tools/modeling/four-step-model/</ref>]]
	==Introduction==		==Introduction==
	The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950s. It was one of the first travel demand models that sought to link land use and behavior to inform transportation planning<ref name="mcnally"> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 4</ref>. Originally applied in the highway planning context, the model was expanded in the 1970s and 1980s to include multimodal trips and improved modelling techniques<ref name="mcnally" />.		The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950s. It was one of the first travel demand models that sought to link land use and behavior to inform transportation planning<ref name="mcnally">McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 4</ref>. Originally applied in the highway planning context, the model was expanded in the 1970s and 1980s to include multimodal trips and improved modelling techniques<ref name="mcnally" />.

	==The Four Steps==		==The Four Steps==
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	===Trip Distribution===		===Trip Distribution===
	Trip distribution matches origins with destinations, often using a gravity model– a calculation that takes into account the relative activity at the origin and destination as well as the travel cost to go between them<ref> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 13</ref>.		Trip distribution matches origins with destinations, often using a gravity model– a calculation that takes into account the relative activity at the origin and destination as well as the travel cost to go between them<ref>McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 13</ref>.

	===Mode Choice===		===Mode Choice===

Edasmalchi at 21:50, 5 December 2019

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	===Trip Distribution===		===Trip Distribution===
	Trip distribution matches origins with destinations, often using a gravity model– a calculation that ~~take~~ into account the relative activity at the origin and destination as well as the travel cost to go between them<ref> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 13</ref>.		Trip distribution matches origins with destinations, often using a gravity model– a calculation that takes into account the relative activity at the origin and destination as well as the travel cost to go between them<ref> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 13</ref>.

	===Mode Choice===		===Mode Choice===

Edasmalchi at 21:55, 4 December 2019

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	==Introduction==		==Introduction==
	The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950s. It was one of the first travel demand models that sought to link land use and behavior to inform transportation planning<ref name=mcnally> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf</ref>. Originally applied in the highway planning context, the model was expanded in the 1970s and 1980s to include multimodal trips and improved modelling techniques<ref name=mcnally />.		The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950s. It was one of the first travel demand models that sought to link land use and behavior to inform transportation planning<ref name="mcnally"> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 4</ref>. Originally applied in the highway planning context, the model was expanded in the 1970s and 1980s to include multimodal trips and improved modelling techniques<ref name="mcnally" />.

	==The Four Steps==		==The Four Steps==
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	===Trip Distribution===		===Trip Distribution===
	Trip distribution matches origins with destinations, often using a gravity model– a calculation that take into account the relative activity at the origin and destination as well as the travel cost to go between them<ref> ~~Wikipedia~~. "~~Trip Distribution~~" ~~Accessed November 2019~~. https://~~en.wikipedia~~.org/~~wiki~~/~~Trip_distribution#Gravity_model~~.</ref>.		Trip distribution matches origins with destinations, often using a gravity model– a calculation that take into account the relative activity at the origin and destination as well as the travel cost to go between them<ref> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf p. 13</ref>.

	===Mode Choice===		===Mode Choice===
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	Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.		Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.

			==Further Reading==
			Wikipedia. "Trip Distribution" https://en.wikipedia.org/wiki/Trip_distribution#Gravity_model
	==References==		==References==
	<references />		<references />

	[[Category:Ridership forecasting]]		[[Category:Ridership forecasting]]

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Trip Distribution

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	===Trip Distribution===		===Trip Distribution===
	Trip distribution matches origins with destinations, often using a gravity ~~model function, equivalent~~ to ~~an entropy maximizing model~~. ~~Older models include the fratar model~~.		Trip distribution matches origins with destinations, often using a gravity model– a calculation that take into account the relative activity at the origin and destination as well as the travel cost to go between them<ref> Wikipedia. "Trip Distribution" Accessed November 2019. https://en.wikipedia.org/wiki/Trip_distribution#Gravity_model.</ref>.

	===Mode Choice===		===Mode Choice===
	Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)		Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)

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Trip Generation

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	The four steps are described as follows:<ref>Wikipedia: Four-step models https://en.wikipedia.org/wiki/Transportation_forecasting#Four-step_models</ref>		The four steps are described as follows:<ref>Wikipedia: Four-step models https://en.wikipedia.org/wiki/Transportation_forecasting#Four-step_models</ref>
	===Trip Generation===		===Trip Generation===
	Trip generation determines the frequency of origins or destinations of trips in each zone by trip purpose, as a function of land ~~uses and~~ household demographics, and other ~~socio-economic~~ factors.		Trip generation determines the frequency of origins or destinations of trips in each zone by trip purpose, as a function of land use, household demographics, and other socioeconomic factors.

	===Trip Distribution===		===Trip Distribution===
	Trip distribution matches origins with destinations, often using a gravity model function, equivalent to an entropy maximizing model. Older models include the fratar model.		Trip distribution matches origins with destinations, often using a gravity model function, equivalent to an entropy maximizing model. Older models include the fratar model.

Edasmalchi: expanded intro

2019-12-04T21:40:09Z

expanded intro

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	==Introduction==		==Introduction==
	The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the ~~1950~~.		The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950s. It was one of the first travel demand models that sought to link land use and behavior to inform transportation planning<ref name=mcnally> McNally, Michael G. "The four step model." (2000). https://escholarship.org/content/qt7j0003j0/qt7j0003j0.pdf</ref>. Originally applied in the highway planning context, the model was expanded in the 1970s and 1980s to include multimodal trips and improved modelling techniques<ref name=mcnally />.

	==The Four Steps==		==The Four Steps==

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2019-12-04T21:27:13Z

reformat, in-progress

← Older revision		Revision as of 21:27, 4 December 2019
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			==Introduction==
	The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950.		The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950.

			==The Four Steps==
	The four steps are described as follows:<ref>Wikipedia: Four-step models https://en.wikipedia.org/wiki/Transportation_forecasting#Four-step_models</ref>		The four steps are described as follows:<ref>Wikipedia: Four-step models https://en.wikipedia.org/wiki/Transportation_forecasting#Four-step_models</ref>
	*Trip generation determines the frequency of origins or destinations of trips in each zone by trip purpose, as a function of land uses and household demographics, and other socio-economic factors.		===Trip Generation===
	*Trip distribution matches origins with destinations, often using a gravity model function, equivalent to an entropy maximizing model. Older models include the fratar model.		Trip generation determines the frequency of origins or destinations of trips in each zone by trip purpose, as a function of land uses and household demographics, and other socio-economic factors.
	*Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)		===Trip Distribution===
	*Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.		Trip distribution matches origins with destinations, often using a gravity model function, equivalent to an entropy maximizing model. Older models include the fratar model.
			===Mode Choice===
			Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)
			===Route Assignment===
			Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.

	==References==		==References==

Leeor at 22:42, 11 February 2017

2017-02-11T22:42:30Z

← Older revision		Revision as of 22:42, 11 February 2017
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	*Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)		*Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)
	*Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.		*Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.

			==References==
			<references />

			[[Category:Ridership forecasting]]

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The four-step travel model is a ubiquitous framework for determining transportation forecasts that goes back to the 1950.

The four steps are described as follows:<ref>Wikipedia: Four-step models https://en.wikipedia.org/wiki/Transportation_forecasting#Four-step_models</ref>
*Trip generation determines the frequency of origins or destinations of trips in each zone by trip purpose, as a function of land uses and household demographics, and other socio-economic factors.
*Trip distribution matches origins with destinations, often using a gravity model function, equivalent to an entropy maximizing model. Older models include the fratar model.
*Mode choice computes the proportion of trips between each origin and destination that use a particular transportation mode. (This modal model may be of the logit form, developed by Nobel Prize winner Daniel McFadden.)
*Route assignment allocates trips between an origin and destination by a particular mode to a route. Often (for highway route assignment) Wardrop's principle of user equilibrium is applied (equivalent to a Nash equilibrium), wherein each driver (or group) chooses the shortest (travel time) path, subject to every other driver doing the same. The difficulty is that travel times are a function of demand, while demand is a function of travel time, the so-called bi-level problem. Another approach is to use the Stackelberg competition model, where users ("followers") respond to the actions of a "leader", in this case for example a traffic manager. This leader anticipates on the response of the followers.