1. Transportation demand analysis
MODE AND ROUTE CHOICE
Transportation demand analysis
Meeghat Habibian

2. Outlines
Mode and route choices are major steps in the trip-making decision process.
These choices occur in urban and in intercity transportation.
most mode and route choice analyses have evolved within the context of urban transportation.

3. Outlines
A major concern in urban travel analysis is the prediction of link flows, particularly during periods of potential congestion.
Why???

4. Outlines
This concern with peak flows has necessitated that mode and route choice analyses be quite microscopic in detail?!?
WHY…???

5. Because….
in order to permit demand models to address a variety of policy issues dealing with transportation systems management.

6. Outlines
In mode choice analysis, one is interested in assessing the impact of policies such as
exclusive lanes for high occupancy vehicles
increased parking taxes for private automobiles
And so on ...

7. Outlines
In route choice analysis one is often concerned with the impacts of such policies…
Tolls.
Metering on freeway ramps.
Joint fare systems on the routes

8. It is common in urban travel demand analysis to associate mode and route choice together, and, indeed, in many applications it is useful to consider a simultaneous mode and route choice as an appropriate model of the trip-making decision process.

9. The hierarchy, or sequence, of choices is generally agreed to be the mode followed by the route choice, although it is quite common to consider both simultaneously.

10. If the scope of the problem at hand is manageable, it may be desirable to model each mode and route combination as a separate alternative.

11. This can, however, lead to excessive numbers of alternatives in large scale networks. In such cases it is preferable to analyze mode choice first and then follow this by route choice .

12. MODE CHOICE

13. Mode choice layouts Trip-end models Trip interchange models Tim=Tif(m)
Restricted to the characteristics of origin (socio- economic variables)
Trip interchange models
Tijm=Tijf(m)
Restricted to the characteristics of origin-destination (socio-economic and LOS variables)

14. Mode choice Binary Diversion Analysis. Abstract Mode Model.
Behavioral (stochastic) model.
Binary model.
Trinomial model.
Multimodal model.

15. Binary Diversion Analysis
Early developments in urban travel mode choice analysis resulted in rather primitive aggregate diversion models where traffic flows were split between modes on the basis of some simple formulation of their relative attributes.

16. Binary Diversion Analysis
Most early applications were limited to the binary choice case because traffic was split between the automobile and the transit modes.

17. Binary Diversion Analysis
This approach is based on comparing the attributes of private and public transportation and then breaking the traffic down between them.

18. Binary Diversion Analysis
The diversion method has been used for modal split both for trip-end (before trip distribution analysis) and for trip interchanges (after trip distribution analysis).

19. Trip-end models Placed before trip distribution step
The accounted variables are socio economic variables and accessibility ratio to the modes
The model is independent of system variables
The model obsolete due to increasing car usage after the 2nd world war

20. The Accessibility Ratio
In the case of trip end diversion, an accessibility measure is usually computed for each of a number of zones in a study area, for each of the two modes, automobile and transit.
Accessibility= ∑ajfij, where,
aj=trip attraction of zone j and
fij=distance between i and j

21. Binary Diversion Curves
Figure 7.1 Auto-transit diversion curves.

22. Types of variables
Sometimes the diversion curves would be stratified on the basis of some socioeconomic characteristics such as average car ownership or household size.

23. Examples of Binary Diversion Curves (trip-end)
Wisconsin Model (1963):
Advanced trip-end model including trip type (purpose), individual characteristics (number of cars) and System characteristics limited to zone i (accessibility ratio in the form of private to public)
Chicago Model (CATS):
Including Landuse, Gender, Occupation, Driver, Age, Car ownership

24. Example of Binary Regression model (trip-end)
Pittsburg model (PATS):
Trip type (purpose), car ownership, residential density, distance from CBD
Ln(number of edu. trips by public/1000people)= Ln (residential density)
Ln(number of other trips by public/1000people with no car)= (residential density) Ln (residential density)2
Ln(number of other trips by public/1000people with 1 car)= (residential density) Ln (residential density)2
Ln(number of other trips by public/1000people with 2+ car)= (residential density) Ln (residential density)2
Generally, regression is avoided because of 0,1 boundaries of the probability

25. Trip Interchange models
In the case of trip interchange diversion, origin-destination flows measured in person-trips are distributed among the two modes using a similar diversion procedure.

26. Binary Diversion Analysis
The difference here is that the attributes used for the diversion are specific to origin-destination pairs and can therefore include more than a single measure of accessibility.

27. Binary Diversion Analysis
Diversion curves can then be developed on the basis of travel time ratios and travel cost ratios, and so forth.

28. Examples of Binary Diversion Curves (trip-interchange)
Bay Area Rapid transit (BART)
Trip type (purpose), peak and off-peak period, CBD and non-CBD, Travel time ratio
Washington, Philadelphia and Toronto study:
Cost ratio, Travel time ratio, out of vehicle time ratio, Income level

29. Washington, Philadelphia and Toronto study
معرفی گراف:

30. Washington, Philadelphia and Toronto study
معرفی گراف:
CR: Ratio of transit to auto time
EC: Annual median income per worker
L: Ratio of transit to auto service
Rang for (L) service time ratio:
L1: 0.0 to 1.5
L2: 1.5 to 3.5
L3: 3.5 to 5.5
L4: 5.5 and over

31. Washington, Philadelphia and Toronto study
معرفی گراف:
CR: Ratio of transit to auto time
EC: Annual median income per worker
L: Ratio of transit to auto service
TTR: Ratio of transit to auto time

32. Binary Diversion Analysis Deficiencies
Diversion curves of mode choice are simple tools for analysis, but their limitations are too many and too serious to make them of much use in the types of policy analyses for which demand studies are done.

33. Binary Diversion Analysis Deficiencies1
The diversion curve method is too aggregate in nature and hence does not lend itself to prediction of the impacts of transportation policies that might affect different groups of urban travelers differently.

34. Binary Diversion Analysis Deficiencies
Households without automobiles, for example, are captive public transportation users and have little or no mode choice, as are households in areas that are not easily accessible to points of public transportation service , they are captive automobile users.

35. Binary Diversion Analysis Deficiencies
Another serious deficiency in diversion analysis is its limitation to binary choice situations.
This precludes its use in most applications of demand analysis, since it is generally agreed that multiple choices are available in most transportation systems. 2

36. Abstract Mode Model

37. Assumptions
Mode choice is based on the modes characteristics (not on their names)
A mode characteristics are sufficient to find its transportation share
A new mode usage can be determined by its characteristics
General form (Gravity structure):
T=αHβCγNθ
H:travel time, C:Cost, N:Nmumber of modes

38. 1Abstract Mode Example Mode 3 Mode 2 Mode 1 3 2 1 Travel Time (hr)
Ratio to best 5
Travel cost (100$)
1.5
2.5
Tk= Hb-60Cb-60Hrk-50Crk-50N
Tk: Number of trips by mode k
Hb: Best travel time Hrk: Travel time ratio of mode k to the best
Cb: Best cost Crk: Travel cost ratio of mode k to the best
N: Number of modes
Tmode1= *1-60*2-60*1-50*2.5-50*3
Tmode1=445, Tmode2=435, Tmode3=  TTotal=1280

39. Abstract Mode Example 1 (Introduction of mode 4)
1.5 3 2 1
Travel Time (hr)
Ratio to best 5
Travel cost (100$)
Tk= Hb-60Cb-60Hrk-50Crk-50N
Tk: Number of trips by mode k
Hb: Best travel time Hrk: Travel time ratio of mode k to the best
Cb: Best cost Crk: Travel cost ratio of mode k to the best
N: Number of modes
Tmode1= *1-60*1-60*1-50*5-50*4
Tmode1=330, Tmode2=370, Tmode3= Tmode4=500
 TTotal=1560

40. Abstract Mode Example 1 (Introduction of mode 5)
0.5
1.5 3 2 1
Travel Time (hr) 6 4
Ratio to best 5
Travel cost (100$)
Introduction of Mode 5:
Tmode1=385, Tmode2=315, Tmode3= Tmode5=520
 TTotal=1440
Introduction of Mode 4 and Mode 5:
Tmode1=270, Tmode2=250, Tmode3= Tmode4=410, Tmode5=480
 TTotal=1590

41. Abstract Mode Example 2
Tijk= α0(Pi)α1(Pj)α2(Yi)α3(Yj)α4(Mi)α5(Mj)α6 (Nij)α7 f1(H)f2(C)f3(D)
f1(H)= (Hijb)β0 (Hijrk)β1
f2(C)= (Cijb)γ0 (Cijrk)γ1
f3(D)= (Dijb)δ0 (Dijrk)δ1
Hb: Best travel time Hrk: Travel time ratio of mode k to the best
Cb: Best cost Crk: Travel cost ratio of mode k to the best
Db: Best convenience Drk: Convenience ratio of mode k to the best
N: Number of modes between i and j
Y: Income M: Land-use index (Labor source)
P:population

42. Abstract Mode deficiencies
The total number of trips depends on the number of modes (This may not be the case in work trips)
Sometimes, it is difficult to find a corridor with the mode variation similar to the model
Ratios are more important than real values

43. Behavioral Models of Mode Choice

44. Variables That Describe Mode Choice
The variables used to explain mode choice belong to two categories:
socioeconomic demand variables
And …
level of service or supply variables

45. The challenge in good mode choice analysis is not only to select the variables that are significant but those that can be used to reflect the type of policy analysis or planning for which mode choice modeling is intended in the first place.

46. Socioeconomic variables
The following are some of the socioeconomic demand variables used to explain mode choice behavior:
Income.
Age and role in household.
Car ownership.
Household size.
Residential location.
Profession.

47. Supply variables
The following are some of the more important supply variables used to explain mode choice behavior:
In-vehicle travel time.
Access, waiting, and transfer times.
Travel cost.
Qualitative and attitudinal variables.

48. Labeled and Unlabeled Choice Models
In constructing behavioral models of mode choice it is possible to adopt either of two postulates regarding the way by which the alternatives are perceived.

49. Unlabeled choice (Mode-abstract)
The person making the choice perceives the attributes themselves rather than the mode being considered and that two distinct modes which have the same level of service attributes would be treated as one.

50. Unlabeled choice (Mode-abstract)
This postulation is derived from Lancaster's (1966) theory of abstract commodities which proposes that consumers decide on the basis of characteristics of goods and services rather than the goods themselves.

51. Labeled choice (Mode-specific)
The assumption is that choices are influenced by the level of service attributes, but these influences will vary from one alternative to another.

52. Mode-abstract and mode-specific comparison
In quantitative terms the difference between a mode-abstract and a mode- specific model is in the specification of the choice function used.

53. Mode-abstract and mode-specific comparison
In unlabelled choice the choice function parameters will not have any relation to specific modes and only the values of the variables would vary according to the alternative in question.

54. Mode-abstract and mode-specific comparison
On the other hand, in the labeled choice case the parameters would also vary depending on the alternative in question and would thus represent the mode-specific influences of the variables they relate to.

55. Mode-abstract choice function:
where α and β are constant parameters,
and i = A or B.
[7.1]

56. Mode-specific choice function
[7.2]

57. in addition, the mode-specific choice function may include dummy variables or constant terms that are specific to each alternative:
where γi is a mode-specific constant term that will have different values for A and B
[7.3]

58. The mode-abstract postulate has an intellectual appeal in mode choice modeling, because it attempts to quantify mode characteristics that affect their likelihood of being chosen and, consistent with other transportation demand analyses, build on the framework of modern microeconomic theory.

59. It also has the pragmatic advantage of requiring a lower number of model parameters than the alternative approach.

60. Examples of Urban Mode Choice Models
The following is a selection of examples of mode choice models intended to illustrate the variety of situations in which they have been applied.

61. A stochastic binary model
Most of the earlier applications of stochastic choice models were in cases of binary choice .
The reason for this was simply pragmatic and due to the absence of efficient, computer-based estimation techniques for multinomial models.

62. In one of the earlier applications De Donnea (1970) constructed a model to describe the choice between auto and bus transit in Rotterdam, the Netherlands.
De Donnea calibrated both a logit and probit model to the same data with a rather insignificant difference in the results.

63. De Donnea's model is a mode-specific model with choice functions of the following form:
[7.4]
[7.5]
and

64. [7.4]
[7.5]
and
where V(1) is the choice function for the auto mode, and V(2) the choice function for the transit mode. Y is the individual's income, t the travel time, and H a dummy variable with H = 1, if the individual is the head of a household, and H = 0 if not, and a1, a2, a 3 are parameters.

65. De Donnea estimated the parameters of the two models:
and the probit model:

66. and obtained the following results:

67. A trinomial model
There have been a number of applications in the trinomial case, the case where there are three alternatives in the choice set.
An example of the trinomial mode choice analysis is a model calibrated on data for the Pittsburgh, Pennsylvania, region by Ganek and Saulino (1976).

68. A trinomial model
In addition to automobile and transit modes the carpool alternative was identified as the third choice.
The carpool in first case represents a mode that is intermediate between the alternative to drive alone and the public transportation mode.

69. Variables (Ganek and Saulino):
In-vehicle time
Access time and egress times
Total cost
Relative comfort and convenience
Flexibility
Mode reliability
Car availability
Location of work place
Household income
Sex
Mode constants

70. Multimodal model for auto and transit choice
It is possible perhaps to obtain a better resolution on mode choice behavior if the automobile and transit modes are differentiated into more alternatives depending on the possible combinations that can be made for particular journeys.

71. Train (1976)
In a study of San Francisco Bay Area households Train (1976) estimated the parameters of a multinomial logit choice mode for four alternatives consisting of auto alone, bus with walking access, bus with automobile access and car pool.

72. Variables (Train, 1976)
Cost divided by post tax wage, in cents per cent per minute
On-vehicle time in minutes
Walk time in minutes
Transfer time in minutes

73. Variables (Train, 1976) Number of transfers
Bus headways in minutes stratified below and above 8 minutes
Family income stratified in income classes Length of residence in the community Number of persons in the household who can drive Mode dummy variables.

74. Ben Akiva and Richards (1976)
A multimodal choice model Another example that illustrates the variety in scope of mode choice modeling is a multimodal model calibrated by Ben Akiva and Richards (1976) on data obtained for some Dutch cities.

75. Variables (Ben Akiva and Richards, 1976)
In-vehicle time
Out-of-vehicle time
Out-of-pocket cost
Household income
Car availability
Occupation
Mode-specific dummy variables

76. Some Limitations of Urban Mode Choice Models
These models can be used to evaluate the effects of minor and gradual changes in the choice environment on mode choices, though only in the area where they have been calibrated.

77. Some Limitations of Urban Mode Choice Models
Another source of weakness in mode choice modeling is the fact that most models that can be considered successful in predicting behavior are mode-specific rather than mode-abstract models.

78. Indeed, in most models available in the literature one can find that the mode-specific dummy variables together with some socioeconomic variables (particularly income car ownership) explain most of the variations of mode choice behavior among individuals or households

79. New developments:
Mode specific models containing a number of alternatives
Relaxing the assumption of independency in IID and developing the nested structure
Relaxing the assumption of independency and identically in IID and developing the mixed structure

80. Finish