Loading in 5 sec....

Trip Distribution Lecture 9 Norman W. GarrickPowerPoint Presentation

Trip Distribution Lecture 9 Norman W. Garrick

- 186 Views
- Uploaded on
- Presentation posted in: General

Trip Distribution Lecture 9 Norman W. Garrick

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Trip Distribution

Lecture 9

Norman W. Garrick

1

3

2

5

4

8

7

6

Socioeconomic Data

Land Use Data

Input:

Output:

Trip Ends by trip purpose

The question is… how do we allocate all the trips among all the potential destinations?

Trip MatrixorTrip Table

Zone 1

- We link production or origin zones to attraction or destination zones
- A trip matrix is produced
- The cells within the trip matrix are the “trip interchanges” between zones

- Number of trips decrease with COST between zones
- Number of trips increase with zone “attractiveness”

- Growth Factor Models
- Gravity Model

- Growth Factor Models assume that we already have a basic trip matrix
- Usually obtained from a previous study or recent survey data

- The goal is then to estimate the matrix at some point in the future
- For example, what would the trip matrix look like in 2 years time?

- Trip Matrix, T (2018)

- Trip Matrix, t(2008)

- Uniform Growth Factor
- Singly-Constrained Growth Factor
- Average Factor
- Detroit Factor
- Fratar Method

i= I = Production Zone

j= J = Attraction Zone

Tij = τ tij for each pair i and j

Tij =Future Trip Matrix

tij = Base-year Trip Matrix

τ = General Growth Rate

If we assume τ = 1.2 (growth rate), then…

- Trip Matrix, t(2013)

Tij = τ tij

= (1.2)(5)

= 6

- Trip Matrix, T (2018)

The Uniform Growth Factor is typically used for over a 1 or 2 year horizon

However, assuming that trips growat a standard uniform rate is a fundamentally flawed concept

The big idea behind the gravity model is Newton’s law of gravitation…

The force of attraction between 2 bodies is directly proportional to the product of masses between the two bodies and inversely proportional to the square of the distance

M1 M2

F = k

r2

Tij = Qij= Trips Volume between i & j

Fij=1/Wcij = Friction Factor

Wij= Generalized Cost (including travel time, cost)

c= Calibration Constant

pij= Probability that trip i will be attracted to zone j

kij= Socioeconomic Adjustment Factor

Tij = Trips between i & j

i = production zone

j = attraction zone

Tij = f (Pi, Aj, Cij)

Cij = Generalized cost of trip from i to j

The bigger the friction factor, the more trips that are encouraged

Pi Aj FijKij

Tij =Qij =

= Pipij

ΣAjFijKij

(Productions)(Attractions)(Friction Factor)

=

Sum of the (Attractions x Friction Factors) of the Zones

Fij = 1 / Wcij

or ln F = - c ln W

What we need…

- Productions, {Pi}
- Attractions, {Aj}
- Skim Tables {Wij)
- Target-Year Interzonal Impedances

- Given:
- Target-year Productions, {Pi}
- Relative Attractiveness of Zones, {Aj}
- Skim Table, {Wij}
- Calibration Factor, c = 2.0
- Socioeconomic Adjustment Factor, K = 1.0

- Find:
- Trip Interchanges, {Qij}

Find Trip Interchanges, {Qij}

Find Denominator of Gravity Model Equation {AjFijKij}

Calculate Friction Factors, {Fij}

Find Probability that Trip i will be attracted to Zone j, {pij}

Given…

1

F11=

=

0.04

52

A2F12K12

Calibration Factorc = 2.0 Socioeconomic Adj. FactorK = 1.0

Target-Year Inter-zonal Impedances, {Wij}

Calculate Friction Factors, {Fij}

1

Fij =

=

Wcij

Find AjFijKij

AjFijKij=A2F12K12

(3)(0.01)(1.0) = 0.03

Find Probability that Trip from i will be attracted to Zone j, {pij}

0.03

p12 =

= 0.5838

=

Σ(AjFijKij)

0.0514

Find Trip Interchanges, {Qij}

Q12 = P1p12 = (1500)(0.5838) = 876

Find Trip Interchanges, {Qij}

Find Denominator of Gravity Model Equation {AjFijKij}

Calculate Friction Factors, {Fij}

Find Probability that Trip i will be attracted to Zone j, {pij}

Given…

1

F23=

=

0.01

102

A3F23K23

Calibration Factorc = 2.0 Socioeconomic Adj. FactorK = 1.0

Target-Year Inter-zonal Impedances, {Wij}

Calculate Friction Factors, {Fij}

1

Fij =

=

Wcij

Find AjFijKij

AjFijKij=A3F23K23

(2)(0.01)(1.0) = 0.02

Find Probability that Trip from i will be attracted to Zone j, {pij}

0.02

p23 =

= 0.1233

=

Σ(AjFijKij)

0.1622

Find Trip Interchanges, {Qij}

Q12 = P2p23 = (0)(0.1233) = 2

Find Trip Interchanges, {Qij}

Find Denominator of Gravity Model Equation {AjFijKij}

Calculate Friction Factors, {Fij}

Find Probability that Trip i will be attracted to Zone j, {pij}

Given…

1

F23=

=

0.01

102

A3F23K23

Calibration Factorc = 2.0 Socioeconomic Adj. FactorK = 1.0

Target-Year Inter-zonal Impedances, {Wij}

Calculate Friction Factors, {Fij}

1

Fij =

=

Wcij

Find AjFijKij

AjFijKij=A3F23K23

(2)(0.01)(1.0) = 0.02

Find Probability that Trip from i will be attracted to Zone j, {pij}

0.02

p23 =

= 0.1233

=

Σ(AjFijKij)

0.1622

Find Trip Interchanges, {Qij}

Q12 = P2p23 = (0)(0.1233) = 2

Keep in mind that the socioeconomic factor, K, can be a matrix of values rather than just one value

- Although K-Factors may improve the model in the base year, they assume that these special conditions will carry over to future years and scenarios
- This limits model sensitivity and undermines the model’s ability to predict future travel behavior

- The need for K-factors often is a symptom of other model problems.
- Additionally, the use of K-factors makes it more difficult to figure out the real problems

- Too much of a reliance on K-Factors in calibration
- External trips and intrazonal trips cause difficulties
- The skim table impedance factors are often too simplistic to be realistic
- Typically based solely upon vehicle travel times
- At most, this might include tolls and parking costs

- Almost always fails to take into account how things such as good transit and walkable neighborhoods affect trip distribution
- No obvious connection to behavioral decision-making

- Typically based solely upon vehicle travel times