2. Trip Generation 1 2 3 4 5 6 8 7 Input: Output: Socioeconomic Data Land Use Data Trip Ends by trip purpose

3. Trip Generation Trip Distribution The question is… how do we allocate all the trips among all the potential destinations? Zone 1 Trip Matrix or Trip Table

4. Trip Distribution

5. 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

6. Basic Assumptions of Trip Distribution Number of trips decrease with COST between zones Number of trips increase with zone “attractiveness”

7. Methods of Trip Distribution I.Growth Factor Models II.Gravity Model

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

9. Growth Factor Models 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 (2008) Trip Matrix, T (2018)

10. Some of the More Popular Growth Factor Models Uniform Growth Factor Singly-Constrained Growth Factor Average Factor Detroit Factor Fratar Method

11. Uniform Growth Factor Model

12. Uniform Growth Factor T ij = τ t ij for each pair i and j T ij = Future Trip Matrix t ij = Base-year Trip Matrix τ = General Growth Rate i= I = Production Zone j= J = Attraction Zone

13. Uniform Growth Factor Trip Matrix, t (2013) Trip Matrix, T (2018) If we assume τ = 1.2 (growth rate), then… T ij = τ t ij = (1.2)(5) = 6

14. Uniform Growth Factor The Uniform Growth Factor is typically used for over a 1 or 2 year horizon However, assuming that trips grow at a standard uniform rate is a fundamentally flawed concept

15. The Gravity Model

16. 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 The Inspiration for the Gravity Model F = k M 1 M 2 r2r2

17. Some of the Variables T ij = Q ij = Trips Volume between i & j F ij =1/W c ij = Friction Factor W ij = Generalized Cost (including travel time, cost) c= Calibration Constant p ij = Probability that trip i will be attracted to zone j k ij = Socioeconomic Adjustment Factor T ij = Trips between i & j i = production zone j = attraction zone T ij = f (P i, A j, C ij ) C ij = Generalized cost of trip from i to j

18. The Gravity Model The bigger the friction factor, the more trips that are encouraged T ij = Q ij = P i A j F ij K ij ΣA j F ij K ij F ij = 1 / W c ij (Productions)(Attractions)(Friction Factor) Sum of the (Attractions x Friction Factors) of the Zones = = P i p ij or ln F = - c ln W

19. To Apply the Gravity Model What we need… 1.Productions, {P i } 2.Attractions, {A j } 3.Skim Tables {W ij )  Target-Year Interzonal Impedances

20. Gravity Model Example 8.2 Given: – Target-year Productions, {P i } – Relative Attractiveness of Zones, {A j } – Skim Table, {W ij } – Calibration Factor, c = 2.0 – Socioeconomic Adjustment Factor, K = 1.0 Find: – Trip Interchanges, {Q ij }

21. Σ(A j F ij K ij ) Calculate Friction Factors, {F ij } Given… Calibration Factor c = 2.0 Socioeconomic Adj. Factor K = 1.0 Calculate Friction Factors, {F ij } Find Denominator of Gravity Model Equation {A j F ij K ij } Find Probability that Trip i will be attracted to Zone j, {p ij } Find Trip Interchanges, {Q ij } Target-Year Inter-zonal Impedances, {W ij } F 11 = 1 5252 =0.04 A j F ij K ij =A 2 F 12 K 12 (3)(0.01)(1.0) = 0.03 Given… Find A j F ij K ij Find Probability that Trip from i will be attracted to Zone j, {p ij } Find Trip Interchanges, {Q ij } p 12 = A 2 F 12 K 12 0.0514 = 0.03 = 0.5838 Q 12 = P 1 p 12 = (1500)(0.5838) = 876 F ij = 1 W c ij =

22. Σ(A j F ij K ij ) Calculate Friction Factors, {F ij } Given… Calibration Factor c = 2.0 Socioeconomic Adj. Factor K = 1.0 Calculate Friction Factors, {F ij } Find Denominator of Gravity Model Equation {A j F ij K ij } Find Probability that Trip i will be attracted to Zone j, {p ij } Find Trip Interchanges, {Q ij } Target-Year Inter-zonal Impedances, {W ij } F 23 = 1 10 2 =0.01 A j F ij K ij =A 3 F 23 K 23 (2)(0.01)(1.0) = 0.02 Given… Find A j F ij K ij Find Probability that Trip from i will be attracted to Zone j, {p ij } Find Trip Interchanges, {Q ij } p 23 = A 3 F 23 K 23 0.1622 = 0.02 = 0.1233 Q 12 = P 2 p 23 = (0)(0.1233) = 2 F ij = 1 W c ij =

23. Σ(A j F ij K ij ) Calculate Friction Factors, {F ij } Given… Calibration Factor c = 2.0 Socioeconomic Adj. Factor K = 1.0 Calculate Friction Factors, {F ij } Find Denominator of Gravity Model Equation {A j F ij K ij } Find Probability that Trip i will be attracted to Zone j, {p ij } Find Trip Interchanges, {Q ij } Target-Year Inter-zonal Impedances, {W ij } F 23 = 1 10 2 =0.01 A j F ij K ij =A 3 F 23 K 23 (2)(0.01)(1.0) = 0.02 Given… Find A j F ij K ij Find Probability that Trip from i will be attracted to Zone j, {p ij } Find Trip Interchanges, {Q ij } p 23 = A 3 F 23 K 23 0.1622 = 0.02 = 0.1233 Q 12 = P 2 p 23 = (0)(0.1233) = 2 F ij = 1 W c ij =

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

25. The Problem with K-Factors 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

26. Limitations of the Gravity Model 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