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Car Ownership Forecasting: Routledge Library Edtions

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Car Ownership Forecasting (Routledge Library Edtions: Global Transport Planning 2)

Car Ownership Forecasting (Routledge Library Edtions: Global Transport Planning Book 2)

by E. W. Allanson

4.6 out of 5

Language	:	English
File size	:	4495 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	195 pages

Car ownership forecasting is a critical input to transportation planning. It is used to estimate the number of cars that will be on the road in the future, which is essential for planning new roads, public transit systems, and other transportation infrastructure. Car ownership forecasting is also used to evaluate the impacts of transportation policies, such as congestion pricing and parking restrictions.

There are a number of different methods that can be used to forecast car ownership. The most common method is the household travel survey. Household travel surveys collect data on the travel behavior of households, including their car ownership and usage. This data can be used to develop models that can forecast car ownership in the future.

Other methods that can be used to forecast car ownership include:

Time series analysis

Economic models

Land use models

Microsimulation models

The choice of which method to use depends on the availability of data and the specific needs of the forecasting study.

Methods for Car Ownership Forecasting

Household Travel Surveys

Household travel surveys are the most common method used to forecast car ownership. These surveys collect data on the travel behavior of households, including their car ownership and usage. This data can be used to develop models that can forecast car ownership in the future.

Household travel surveys typically include a variety of questions about car ownership and usage, such as:

How many cars does your household own?

What type of cars do you own?

How often do you use your cars?

Where do you usually drive your cars?

The data collected from household travel surveys can be used to develop a variety of models that can forecast car ownership. These models typically use statistical techniques to identify the factors that influence car ownership, such as household income, household size, and residential location.

Time Series Analysis

Time series analysis is another method that can be used to forecast car ownership. Time series analysis involves analyzing historical data on car ownership to identify trends and patterns. This information can then be used to forecast future car ownership.

Time series analysis is a relatively simple and straightforward method to use. However, it is important to note that this method can only be used to forecast future car ownership if the historical trends and patterns are expected to continue. If there are any major changes in the factors that influence car ownership, such as a change in the economy or a change in transportation policy, then time series analysis may not be an accurate method to use.

Economic Models

Economic models can also be used to forecast car ownership. Economic models typically use economic theory to predict how car ownership will change in response to changes in the economy, such as changes in income, interest rates, and fuel prices.

Economic models can be a useful tool for forecasting car ownership, but it is important to note that these models are only as accurate as the assumptions that they are based on. If the assumptions of the model are not accurate, then the forecast will not be accurate.

Land Use Models

Land use models can also be used to forecast car ownership. Land use models typically use data on land use and zoning to predict how car ownership will change in response to changes in the built environment, such as the construction of new roads or the development of new housing.

Land use models can be a useful tool for forecasting car ownership, but it is important to note that these models are only as accurate as the data that they are based on. If the data on land use and zoning is not accurate, then the forecast will not be accurate.

Microsimulation Models

Microsimulation models are another method that can be used to forecast car ownership. Microsimulation models typically use data on individual households and vehicles to simulate how car ownership will change in response to changes in the factors that influence car ownership, such as household income, household size, and residential location.

Microsimulation models can be a useful tool for forecasting car ownership, but it is important to note that these models are only as accurate as the data that they are based on. If the data on individual households and vehicles is not accurate, then the forecast will not be accurate.

Applications of Car Ownership Forecasting

Car ownership forecasting is used for a variety of applications in transportation planning. Some of the most common applications include:

Planning new roads and public transit systems

Evaluating the impacts of transportation policies

Managing traffic congestion

Reducing air pollution

Improving public health

Car ownership forecasting is an important tool for transportation planners. It can help them to make informed decisions about how to improve the transportation system and make it more sustainable.

Car ownership forecasting is a complex and challenging task. However, it is an essential input to transportation planning. By understanding the methods and applications of car ownership forecasting, transportation planners can make informed decisions about how to improve the transportation system and make it more sustainable.

Car Ownership Forecasting (Routledge Library Edtions: Global Transport Planning Book 2)

by E. W. Allanson

4.6 out of 5