8.3.2 - Example
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Understanding Trip Data
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Today, we are looking at an example of trip distribution using the growth factor model. Can anyone tell me what trip data we have for zones 1, 2, and 3?
The trips originating from zone 1 are 78, from zone 2 are 92, and from zone 3 are 82.
Exactly! And what about the trips terminating at those zones?
They are 88 for zone 1, 96 for zone 2, and 78 for zone 3.
Great! Just remember, understanding this initial data is crucial for applying the growth factor correctly.
Applying the Growth Factor Model
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Now that we have our trip data, how do we calculate the expanded origin-constrained trip table?
We need to use the growth factor, which in this case is 1.3, to adjust the trip values.
Correct! Can anyone share how we apply this growth factor to calculate the new trip values?
We multiply each trip value by 1.3!
Exactly! This uniform factor helps us expand our trip numbers proportionally across all zones.
Calculating Expanded Trip Table
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Let's do the calculations together. What should the new trip values look like for zone 1?
For zone 1, we take 78 trips and multiply by 1.3 to get 101.4.
Excellent! What about the trips to other zones?
For zone 2, 92 times 1.3 equals 119.6, and for zone 3, it's 106.2.
Let's summarize this. What we found were the new trip totals for each zone!
Finalizing the Trip Table
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Now that we have calculated the new trip values, how do we summarize this into a table?
We can organize it visually in a matrix format similar to the one we started with!
Exactly! And remember, each value in the new matrix must reflect the expanded trips for each zone accurately.
Introduction & Overview
Read summaries of the section's main ideas at different levels of detail.
Quick Overview
Standard
The section illustrates how to expand trip information using a growth factor model through a detailed example. Specific origin and destination trip data are provided, alongside calculations to demonstrate the application of a uniform growth factor in trip distribution.
Detailed
In this section, we delve into a specific example of the growth factor model in trip distribution. The given trip data shows trips originating from zones 1, 2, and 3 as 78, 92, and 82, respectively, with corresponding trips terminating at those zones as 88, 96, and 78. By applying a growth factor of 1.3, we aim to find the expanded origin-constrained trip table. The provided trip matrix indicates the distribution of trips across zones, illustrating how each zone's trip volume is proportionally increased to accommodate expected growth. The calculations apply the growth factor uniformly, ensuring that the total trips from origins and the attracting trips to destinations reflect the original data adjusted for the expected growth.
Audio Book
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Data on Trips from Zones
Chapter 1 of 4
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Chapter Content
Trips originating from zone 1,2,3 of a study area are 78,92 and 82 respectively and those terminating at zones 1,2,3 are given as 88,96 and 78 respectively.
Detailed Explanation
In this example, we start with data showing the number of trips originating from three different zones in a study area. The trips from each zone are quantified as follows: Zone 1 has 78 trips, Zone 2 has 92 trips, and Zone 3 has 82 trips. Additionally, we observe the trips terminating at the same zones: Zone 1 receives 88 trips, Zone 2 receives 96 trips, and Zone 3 receives 78 trips. This data is essential for understanding travel patterns and distribution in the specified area.
Examples & Analogies
Imagine a small city divided into three neighborhoods. In neighborhood 1, 78 people leave their homes to visit other neighborhoods, while neighborhood 2 sees 92 people venturing out, and neighborhood 3 sends 82 people on their way. Meanwhile, these neighborhoods also attract visitors: neighborhood 1 welcomes 88 newcomers, neighborhood 2 attracts 96, and neighborhood 3 draws 78. This scenario helps city planners understand the flow of traffic between these neighborhoods.
Calculating Expanded Trip Table
Chapter 2 of 4
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Chapter Content
If the growth factor is 1.3 and the cost matrix is as shown below, find the expanded origin-constrained growth trip table.
Detailed Explanation
The growth factor used in this example is 1.3, indicating an increase in trip numbers by 30%. To find the expanded trip table, each cell in the trip matrix must be multiplied by this growth factor. This means taking each of the previously calculated trips for every zone pair and increasing them by 30%. This process allows us to estimate how many trips would be generated under the new growth conditions.
Examples & Analogies
Suppose there’s a bakery that sells 100 loaves of bread each day. If the demand suddenly increases, and the owner predicts that they can sell 30% more bread due to popularity, they can calculate that they now expect to sell 130 loaves daily. Just as the bakery multiplies its daily sales by a growth factor, the trip distribution process uses a growth factor to project how many trips will occur in an expanding city.
Solution to Trip Table Expansion
Chapter 3 of 4
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Chapter Content
Given growth factor = 1.3, Therefore, multiplying the growth factor with each of the cells in the matrix gives the solution as shown below.
Detailed Explanation
In this section, we apply the growth factor of 1.3 to each of the cells of the initial trip matrix, producing new values for the trips between the zones. For example, if the original number of trips from zone 1 to zone 2 is 30, the new number becomes 30 multiplied by 1.3, resulting in 39 trips. This process is repeated for each cell of the trip matrix, helping create an updated representation of the expected journey patterns.
Examples & Analogies
Consider a growing school where the number of students increases by a certain percentage each year. If there are initially 100 students in a class, applying a growth factor of 1.3 means the class will eventually have about 130 students. By calculating how each class size will grow, the school can prepare for additional resources and staff!
Final Expanded Trip Table
Chapter 4 of 4
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Chapter Content
1 2 3 O
1 26 39 36.4 101.4
2 46.8 41.6 31.2 119.6
3 28.6 44.2 33.8 106.2
D 101.4 124.8 101.4 327.6
Detailed Explanation
This section illustrates the completed expanded trip table after applying the growth factor. The new table shows the number of trips originating from each zone and how many trips are expected to be attracted to each zone. The totals in the last column ('O') represent the expanded origin trips for each respective zone, while the totals in the last row ('D') signify the expected attractants to each zone.
Examples & Analogies
Think of this final expanded table like a busy seating chart in a restaurant. If the restaurant sees an uptick in customers (by applying our growth factor), all sections in the seating plan adjust accordingly. The restaurant manager can then anticipate the increases in certain areas, guaranteeing they have enough staff and food to meet the new demand!
Key Concepts
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Growth Factor: A multiplier used to adjust existing trip data to project future use.
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Trip Table: A matrix displaying trips between origins and destinations.
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Origin-Constrained Growth: Refers to adjusting trip values based on the origin zone's trip generation.
Examples & Applications
Using a growth factor of 1.3 on zone 1 trips of 78 results in updated trips of 101.4.
Applying the same factor to zone 2 trips of 92 results in updated trips of 119.6.
Memory Aids
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Rhymes
With trips to grow, we use a factor to show, '1.3' makes numbers flow!
Stories
Imagine a city where every trip counts, and somebody discovers a magic number, '1.3'. Whenever they apply this magic, each city's journey measures how much it can grow!
Memory Tools
F-A-T for remembering trip steps: Factor, Adjust, Total. Calculate each step to see where trips go!
Acronyms
G-F-M for Growth Factor Model
for Growth
for Factor
and M for Model!
Flash Cards
Glossary
- Trip Generation
The process of determining the number of trips originating from a particular zone.
- Trip Distribution
The method of allocating generated trips to various destinations within a study area.
- Growth Factor Model
A method for projecting future travel demand based on a growth factor applied to existing trip data.
- Trip Matrix
An array representing trips between various origin and destination zones.
Reference links
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