1. Trip Generation II Meeghat Habibian Transportation Demand Analysis

2. Content Linear regression Statistical tests
Aggregate vs. Disaggregate approach
The dummy variable
Transferability and temporal stability of model
Accessibility
Transportation Demand Analysis- Trip Generation 2

3. Introduction
Calibrating models to forecast trips produced from (attracted to) each zone in the future
Methods:
Graphs
Land use based factors (ITE)
Growth factor
Cross classification
Linear regression
Transportation Demand Analysis- Trip Generation 2

4. Transportation Demand Analysis
Trip Generation 2
Linear Regression

5. Linear Regression An approach for modeling a relationship between
Dependent variable (y) and
One (or more) independent variable(s) (xk)
y=β0+β1x1+β2x2 +β3x3 +β4x4 +…+βkxk +ε
Goal:
To calculate coefficients (βi), such to minimize sum of the squares of errors (differences between observations and estimation)
Transportation Demand Analysis- Trip Generation 2

6. Matrix Notation Y=X β+ ε n: number of observationβ0
Transportation Demand Analysis- Trip Generation 2
n: number of observation
k: number of independent variables

7. Assumptions for Error Term
1- εi has normal distribution: εi ~ Normal
2- Mean of εi is 0: E(εi)=0
3- Expectation of εi2 is finite: E(εi2)=σ2
Var(εi)=E[εi-E(εi)]2=E[εi-0]2=E(εi2)= σ2
Therefore: εi~ N(0 , σ2 )
Transportation Demand Analysis- Trip Generation 2

8. Assumptions for Error Term
4- Errors are independent: ∀i≠j: E(εi.εj)=0
E(εi.εj)= E(εi).E(εj)=0 (Non auto regression)
5- Xi s are deterministic such to
6-Number of observations is grater than number of coefficients: n > k+1
7- Xi s are independent from each other
Transportation Demand Analysis- Trip Generation 2

9. Society and Sample
Calculations are based on the sample but results should reflect the society
E(yi)=β0+β1xi
β 0~N(β0, σ2)
β 1~N(β1, σ2)
Transportation Demand Analysis- Trip Generation 2

10. Dependent Variable Distribution (y)
y=β0+β1x1+β2x2 +β3x3 +β4x4 +…+βKxK + ε
Therefore:
y~N(β0+β1x1+β2x2 +β3x3 +β4x4 +…+βKxK , σ2)
𝐸(𝑦)=𝐸( 𝛽 0 + 𝑖=1 𝐾 𝛽 𝑖 𝑥 𝑖 +𝜀)=𝐸( 𝛽 0 )+𝐸( 𝑖=1 𝐾 𝛽 𝑖 𝑥 𝑖 )+𝐸(𝜀) = 𝛽 0 + 𝑖=1 𝐾 𝐸( 𝛽 𝑖 𝑥 𝑖 ))+0= 𝛽 0 + 𝑖=1 𝐾 𝑥 𝑖 𝐸( 𝛽 𝑖 )= 𝛽 0 + 𝑖=1 𝐾 𝑥 𝑖 𝛽 𝑖
𝑉𝑎𝑟(𝑦)=𝐸 𝑦−𝐸(𝑦) 2 =𝐸 𝛽 0 + 𝑖=1 𝑘 𝛽 𝑖 𝑥 𝑖 +𝜀−( 𝛽 0 + 𝑖=1 𝑘 𝛽 𝑖 𝑥 𝑖 ) 2 =𝐸( 𝜀 2 )= 𝜎 2
Transportation Demand Analysis- Trip Generation 2

11. Question? Given (society): y~N(β0+β1xi, σ2)
What are the mean and standard error of the estimated coefficients based on the sample (i.e., )?
Given (society):
y~N(β0+β1xi, σ2)
β 0~N(β0, σ2)
β 1~N(β1, σ2)
Transportation Demand Analysis- Trip Generation 2

12. Remember from TP We were looking for estimation of β0 and β1:
Error sum of squares definition:
We calculated in order to minimize ESS:
Transportation Demand Analysis- Trip Generation 2
𝐸𝑆𝑆= 𝑖=1 𝑛 𝑦 𝑖 − 𝛽 ∧ 0 − 𝛽 ∧ 1 𝑥 𝑖 ) 2

13. Remember from TP 𝐸𝑆𝑆= 𝑖=1 𝑛 𝑦 𝑖 − 𝛽 ∧ 0 − 𝛽 ∧ 1 𝑥 𝑖 ) 2 ∧ ∧
𝐸𝑆𝑆= 𝑖=1 𝑛 𝑦 𝑖 − 𝛽 ∧ 0 − 𝛽 ∧ 1 𝑥 𝑖 ) 2 ∧
Transportation Demand Analysis- Trip Generation 2 ∧

14. Remember from TP By solving the recent equations:
Transportation Demand Analysis- Trip Generation 2

15. Definitions Therefore: 𝑆 𝑥𝑦 = 𝑖=1 𝑛 𝑥 𝑖 − 𝑥 )( 𝑦 𝑖 − 𝑦 )
𝑆 𝑥𝑦 = 𝑖=1 𝑛 𝑥 𝑖 − 𝑥 )( 𝑦 𝑖 − 𝑦 )
Transportation Demand Analysis- Trip Generation 2

16. Transportation Demand Analysis- Trip Generation 2

17. Demonstration:
Transportation Demand Analysis- Trip Generation 2

18. Transportation Demand Analysis- Trip Generation 2

19. We can rewrite 𝛽 1 in form of a linear combination of 𝑦 𝑖 as follows :
Transportation Demand Analysis- Trip Generation 2

20. Transportation Demand Analysis- Trip Generation 2

21. =E [Σci2εi2+2ΣiΣjcicjεiεj])
Transportation Demand Analysis- Trip Generation 2
=E [Σci2εi2+2ΣiΣjcicjεiεj]

22. Transportation Demand Analysis- Trip Generation 2

23. Transportation Demand Analysis- Trip Generation 2
Page 43; Slide 166:

24. Demonstrate 𝛽 𝑜 is a linear combination of 𝑦 𝑖 :
Transportation Demand Analysis- Trip Generation 2

25. Transportation Demand Analysis- Trip Generation 2

26. Transportation Demand Analysis- Trip Generation 2

27. 2
Transportation Demand Analysis- Trip Generation 2

28. Conclusion 𝜷
Least square estimators have normal Distribution as follows:
Transportation Demand Analysis- Trip Generation 2
Best Linear Unbiased Estimator = BLUE

29. Sample standard error σ is a measure of variability of y
lower values for σ2 show that observations are closer to regression line
For 1 independent variable:
For k independent variables:
Transportation Demand Analysis- Trip Generation 2

30. Transportation Demand Analysis
Trip Generation 2
Statistical Tests

31. Correlation Coefficient
Square of correlation coefficient is called Coefficient of Determination, R2
-1≤R≤1
Transportation Demand Analysis- Trip Generation 2

32. Goodness of Fit Measure
Define:
TSS=RSS+ESS
Transportation Demand Analysis- Trip Generation 2

33. Coefficient Significance
T-student test (T-Test)
T-Table  Tcritical (α,n-k-1)
Transportation Demand Analysis- Trip Generation 2
T0<Tcritical  H0 can not be rejected at α level
T0>Tcritical  H0 is rejected at α level

34. Statistical Hypothesis
F test:
For checking whole model:
H0:R2= (RSS=0)
k: Number of independent variables
Transportation Demand Analysis- Trip Generation 2
F0<Fcritical  H0 can not be rejected at α level
F0>Fcritical  H0 is rejected at α level

35. Choosing Best Model 1- Low correlation of independent variables (IVs)
2- Intuitive sign of coefficients
3- T-Student test
4- Smaller value for β0
5- Goodness of fit value
6- F test
7- Lower number of variables
Transportation Demand Analysis- Trip Generation 2

36. Aggregate vs. Disaggregate Approaches
Transportation Demand Analysis
Trip Generation 2
Aggregate vs. Disaggregate Approaches

37. Modeling Approaches Disaggregate models Lower sample size
Aggregate total (zonal average level)
Aggregate rate (household-based average level)
Higher variation
Lower goodness of fit
Makes more sense
Transportation Demand Analysis- Trip Generation 2

38. Problem definition 𝑦 𝑗 : 𝑥 𝑗 : 𝑌 𝐽 : 𝑋 𝐽 : Household j daily trips
𝑦 𝑗 : 𝑥 𝑗 : 𝑌 𝐽 : 𝑋 𝐽 :
Household j daily trips
Household j car ownership
Transportation Demand Analysis- Trip Generation 2
Household based zonal daily trips (zone J)
Household based zonal car ownership (zone J)

39. General Relationships (I)
Transportation Demand Analysis- Trip Generation 2

40. Errors distributions
Transportation Demand Analysis- Trip Generation 2

41. General Relationships (II)
𝑋 = 1 𝑇 𝐽=1 𝑇 𝑋 𝐽
Transportation Demand Analysis- Trip Generation 2

42. General Relationships (III)
Transportation Demand Analysis- Trip Generation 2

43. Variation of β 1
Transportation Demand Analysis- Trip Generation 2

44. Variation of β 1
Transportation Demand Analysis- Trip Generation 2

45. General Comparison
Values of R2 for aggregate models are much higher than disaggregate models
Coefficient of aggregate models have grater variance than disaggregate models
Transportation Demand Analysis- Trip Generation 2

46. Transportation Demand Analysis
Trip Generation 2
The Dummy Variable

47. Why We Need Dummy Variables?
Non quantifiable variables
e.g., Gender, Marital status, …
2. Non uniform effect (in different intervals) variables
e.g., Age, distance, …
Transportation Demand Analysis- Trip Generation 2
Uniform effect
Non-uniform effect

48. Structure Dummy variable = Example: D1= D2=
1, if a condition is satisfied
0, otherwise
Dummy variable =
Example:
D1=
D2=
1, if a household has 1 car
0, otherwise
Transportation Demand Analysis- Trip Generation 2
1, if a household has 2 or more car
0, otherwise

49. Example (Trip Generation)
An ordinary regression model:
Trip number = 𝛽 0+ 𝛽 1 (Car ownership)+ 𝛽 2 (HHSZ)
Trips
Cars
HHSZ=2
HHSZ=1
HHSZ=0 β1
Transportation Demand Analysis- Trip Generation 2
Transportation Demand Analysis- Trip Generation 2
𝛽 0+2 𝛽 2
𝛽 0+ 𝛽 2
𝛽 0

50. Example (Trip Generation)
An ordinary regression model:
Trip number = 𝛽 0+ 𝛽 1 (Car ownership)+ 𝛽 2 (HHSZ)
A dummy enhanced model:
Trip number = 𝛽 0+ 𝛽 1X2i+…+ 𝛽 5X6i+ 𝛽 6Z2i+ 𝛽 7Z3i+εi
X1i= Z1i=
X2i= Z2i= …
X6i= Z3i=
Therefore: Household i:
1, household i has 1 member
0, otherwise
1, household i has not car
0, otherwise
Transportation Demand Analysis- Trip Generation 2
1, household i has 2 members
0, otherwise
1, household i has 1 car
0, otherwise
1, household i has 6 members
0, otherwise
1, household i has 2+ cars
0, otherwise
X1i+X2i+X3i+X4i+X5i+X6i=1 Z1i+Z2i+Z3i=1

51. Example (Trip Generation)
Dummy enhanced model:
Trip number = 𝛽 0+ 𝛽 1X2i+…+ 𝛽 5X6i+ 𝛽 6Z2i+ 𝛽 7Z3i+εi
X1i= Z1i=
X2i= Z2i= …
X6i= Z3i=
Examples:
Household containing 1 member and no car: 𝑌 = 𝛽 0
Household containing 1 member and 1 car: 𝑌 = 𝛽 0+ 𝛽 6
Household containing 3 members and 3 cars: 𝑌 = 𝛽 0+ 𝛽 3+ 𝛽 7
1, household i has 1 member
0, otherwise
1, household i has not car
0, otherwise
1, household i has 2 members
0, otherwise
1, household i has 1 car
0, otherwise
Transportation Demand Analysis- Trip Generation 2
1, household i has 6 members
0, otherwise
1, household i has 2+ cars
0, otherwise

52. Notes Each pair of dummy variables must not overlapped
Union of all levels must be the Universal set
At least, one level (known as base level) should excluded from the modeling
A multiplicative dummy may also use (e.g., XZ) which would be a more complicated case
Transportation Demand Analysis- Trip Generation 2

53. Transferability and Temporal Stability of a Model
Transportation Demand Analysis
Trip Generation 2

54. Model Transferability
Aggregate model
Aggregate Total
Different zone size, topography, …
Aggregate rate
Households behavior can be similar
Disaggregate model
Individuals may behave more similar
E.g., New mass transit choice for new towns
Transportation Demand Analysis- Trip Generation 2

55. Temporal Stability Use a model after a long period in the future
Shall a new dataset for new time (t2) be collected?
Two issues should be checked (small dataset)
Macro
Observation – estimation graph
Micro
Statistical assessment of coefficients
Transportation Demand Analysis- Trip Generation 2

56. Macro (Observation – Estimation Graph)
Use an observation-estimation graph
Yt2=α+βY^t1 α
Statistically, α should be 0 and β should be 1
Yt2 β
Transportation Demand Analysis- Trip Generation 2
Y^t1

57. Micro (Statistical Assessment of Coefficients)
Differences of respective βs should be statistically 0
As βs have normal distributions, their respective difference has also normal distribution:
βt1-βt2 ~ N( 0, SE(βt1-βt2) )
Transportation Demand Analysis- Trip Generation 2

58. Micro (Example)
Trip generation data for 357 household is available for times t1 and t2
Trip number = β0+β1 (Car ownership)+ β2 (HHSZ)
Two models have been calibrated as follows:
Transportation Demand Analysis- Trip Generation 2 t1 t2 β0
-0.45
-0.19 β1
1.40 (0.13)
1.46 (0.15) β2
1.92 (0.31)
1.52 (0.27) R2
0.34
0.36

59. Micro (Example) Assessing β1
Trip number = β0+β1 (Car ownership)+ β2 (HHSZ)
H0: βt1-βt2 =0
T=(βt1-βt2 )/SE(βt1-βt2 )
Remember: Var(X-Y)=Var(X)+Var(Y)-2Cov(X,Y)
Independent samples: Cov(βt1,βt2 )=0
SE2=(0.13)2+(0.15)2-2*0=  SE=0.198
T=( )/0.198=  H0 can not be rejected t1 t2 β0
-0.45
-0.19
Β1 (SE)
1.40 (0.13)
1.46 (0.15)
Β2 (SE)
1.92 (0.31)
1.52 (0.27) R2
0.34
0.36
Transportation Demand Analysis- Trip Generation 2

60. Micro (Example) Assessing β2
Trip number = β0+β1 (Car ownership)+ β2 (HHSZ)
H0: βt1-βt2 =0
T=(βt1-βt2 )/SE(βt1-βt2 )
Remember: Var(X-Y)=Var(X)+Var(Y)-2Cov(X,Y)
Independent samples: Cov(βt1,βt2 )=0
SE2=(0.31)2+(0.27)2-2*0=0.169  SE=0.411
T=( )/0.411=0.97  H0 can not be rejected t1 t2 β0
-0.45
-0.19
Β1 (SE)
1.40 (0.13)
1.46 (0.15)
Β2 (SE)
1.92 (0.31)
1.52 (0.27) R2
0.34
0.36
Transportation Demand Analysis- Trip Generation 2

61. Notes No change in the role of variables during the studied period
E.g., Car as a proxy of income vs. Car as an essential instrument in individual’s lifestyle
Pooling the data together and use a respective dummy is also recommended to calibrate a model for a long period with two datasets
Transportation Demand Analysis- Trip Generation 2

62. Transportation Demand Analysis
Trip Generation 2
Accessibility

63. Necessity Trip generation models are not sensitive to policy making
Because they are not sensitive to attributes of the transportation system (e.g., time and cost)
Note:
Time and cost depend on both origin and destination which is not known in trip generation stage
Transportation Demand Analysis- Trip Generation 2

64. Definition Acci=ΣjcijAj Acci: Accessibility index for zone i
Cij: Cost of travel between I and j
Aj: Opportunities at zone j (e.g., Population, Student number, School number)
Transportation Demand Analysis- Trip Generation 2

65. Transportation Demand Analysis- Trip Generation 2
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