1. A Meta-Analysis of Two Gauteng SP Data Sets
Presentation to Southern African Transport Conference
Gary Hayes
Prof. Christo Venter
11th July 2017

2. Presentation Overview
Choice Model Design Process
SP Experiments & Discrete Choice Models (DCM)
Background to Tshwane and Ekurhuleni SP Data Sets
Discrete Choice Model Results
What does this tell us?
Why is this important?
Transport models are important tools in the planning process, we make important infrastructure and operational decisions based on their outputs
The decisions have long-term economic, financial, environmental, social and development implications

3. 1. The Choice Model Design Process
What user choices are to be simulated?
Implications for model structure;
Utility function definition & attributes (focus groups);
Market segments & sample sizes. 1
Design of choice model
Design & Execution of
SP / RP Experiment 2
Capture device & type of survey (e.g. CAPI intercept)
Attribute levels, orthogonal designs, fractional factorial designs (i.e. no. of choice sets & blocks);
Pilot surveys, revisions & main surveys. 3
Very often find that choices to be modelled are not included in choice model structure and SP experiment design;
Stated preference experiments require careful design, both statistical and layout design;
The number of attributes and attribute levels can create a high number of combinations;
Statistical methods (orthogonal designs) are used to create a reduced number of combinations but retain statistical significance
Estimation of Discrete Choice Model (DCM)
Appropriate software (incl. freeware e.g. Biogeme, R);
Overall model and individual attribute statistical significance;
Interpretation & application of outputs.
7 December December 2019

4. 2. Stated Preference Experiments
Used to determine user preferences for products / services using choice models;
Require respondents to make a CHOICE between alternatives;
Respondents make their choice based on trip time, cost and transfer trade-offs.
Example of a choice set (labelled)
Current Mode Taxi
Walk 10 minutes
Wait 10 minutes
Travel time 35 minutes
Fare R per trip
Without any transfers OR
Alternative Mode BRT
Wait 5 minutes
Travel time 30 minutes
Fare R 9.00 per trip
With 1 transfer
The number of attributes and attribute values makes up the number of possible combinations L=^(MA) where L = no. of levels; M= no. of alternatives; A = no. of attributes
For example, 2 alternatives with 5 attributes each & 2 levels results in 2^(5x2) =2^10 = 1024 combinations for full factorial design
Fractional factorial designs allow for main effects to be included RP SP
7 December December 2019

5. 2. SP Surveys - Key Requirements
Integration of mode choice model and SP survey design (SP Outputs = Mode Choice model inputs).
Questionnaire completion time and type of survey. Use of CAPI must be standard. Pilot survey essential.
Inclusion of RP and pivoting SP off RP.
Mode Choice Model Design and SP Survey Design
SP Survey fieldwork training, management and constant monitoring.
Fractional factorial & orthogonal choice set designs.
7 December December 2019

6. 2. Discrete Choice Models (DCM’s)
Choice process of individual n:
DCM’s use utility theory to explain user preferences
Random utility maximisation is fundamental to DCM’s
Several types, e.g. multinomial logit (MNL), nested logit and mixed logit (ML) models.
7 December December 2019

7. 2. Definition of Trip Utility
Standard Form of Utility: Linear in Parameters form of weighted attributes
Utility of Alternative
Weighted Sum of
Observable Attributes
Unobservable
‘Random’ Component = +
Utility of Alternative i with n attributes = μi = Σ βinX εi n
n=1
Notes
Systematic / Deterministic
component common to all individuals.
Also known as Representative Utility
Stochastic Component
unique to individuals +
Important assumptions in regard the error term
Independently and Identically Distributed Error Terms (IID). If extreme Gumbel distributed, then can ignore. Test for IID;
Independence from Irrelevant Alternatives (IIA) (red bus – blue bus issue)
As we cannot ‘read the mind’ of the individual and exactly predict their choice,
we can only explain choice up to a probability of selection;
VTTS is the marginal rate of travel time divided by marginal rate of travel cost
i.e. Behavioural VTTS = ratio of travel time and cost β values
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8. 3. The Three SP Data Sets in Gauteng
SP Survey for Proposed BRT Mode:
Sample size: 400
Current Modes: Minibus taxi; rail; bus; car
Alternative mode: BRT
Rated responses (5-point Likert);
Conjoint analysis for MNL application
Tshwane (2012)
SP Survey for Proposed BRT Mode:
Sample size: 400
Current Modes: Minibus taxi; rail; car
Alternative mode: BRT
Rated responses (5-point Likert);
Conjoint analysis for MNL application
Ekurhuleni (2013)
SP/RP Survey for BRT Mode Evaluation:
Sample size: 1,200
Current Modes: Minibus taxi; rail; car; Gautrain; bus
CAPI survey with alternative attributes pivoted off current mode values
Alternative mode: BRT
Mixed logit application
Johannesburg (2014)
7 December December 2019

9. 3. Objectives of SP Meta-Analysis
Four main objectives of the investigation were:
Assess Suitability of Conjoint Models
Evaluate Extent of Traveller Heterogeneity
Estimate Value of Behavioural Travel Time Savings (VTTS)
Consolidate Tshwane and Ekurhuleni SP Data Sets

10. Tshwane (2012) and Ekurhuleni (2013)

11. 4. Conversion Assumption: Likert to Choice Responses
The key assumptions were that:
If a respondent prefers or strongly prefers their current mode, the discrete choice is 0;
If they prefer or strongly prefer the alternative (BRT) mode, then the discrete choice is 1;
If the response was neutral it was removed for the data set.
Respondent Preference Rating
Conjoint Likert Scale
Discrete Choice
Strongly Prefer Current Mode 5
Prefer Current Mode 4
No Preference 3
Data Removed
Prefer BRT Mode 2 1
Strongly Prefer BRT Mode
7 December December 2019

12. 4. City of Tshwane CM and MNL Results: All Income Groups
Public Modes (Excl. Bus)
Private Car
Attribute
Coefficient
t-ratio
t-value
Wait Time (min)
0.0129
6.77
0.0165
3.23
Travel Time (min)
0.0077
5.63
0.0120
9.79
Fare (R)
0.0331
7.74
0.0700
3.72
No. Transfers
0.5230
30.78
0.0961
2.13
BRT ASC
3.167
119.10
1.672
7.20
VTTS (Rand/hour)
14.00
10.29
Log-Likelihood -
Prob > |Chi2|
Bus mode excluded from public modes
Conjoint
Model
Original VTTS all modes all income groups: R5.31/hr
Tshwane ML
Public Modes (Excl. Bus)
Private Car
Attribute
Coefficient
t-value
Wait Time (min)
-5.22
-2.30
Travel Time (min)
-9.03
-9.79
Fare (R)
-12.05
-3.37
No. Transfers
-19.72
-2.00
BRT ASC -
1.8560
4.00
VTTS (Rand/hour)
6.44
18.32
Log-Likelihood
Prob > |Chi2|
0.000
Note: Behavioural VTTS is not the same as social or equity VTTS used in economic analyses.
MNL
Note that behavioural VTTS is not the social or equity VTTS
7 December December 2019

13. 4. Ekurhuleni CM and MNL Results (All Incomes)
Ekurhuleni Multinomial Logit
Public Modes (Rail + Taxi)
Private Car Mode
Coefficient
t-value
Wait Time (min)
0.0234
9.97
0.0199
2.87
Travel Time (min)
0.0258
12.45
0.0257
3.62
Fare (R)
0.0911
8.52
0.1099
1.99
No. Transfers
0.2340
12.12
0.1231
1.00
BRT ASC
2.6890
72.31
0.8534
1.02
VTTS (Rand/hour)
8.86
14.04
Log-Likelihood -
Prob > |Chi2|
Conjoint
Model
Original VTTS:
Rail: R8.89/hr
Taxi: R14.72/hr
Car: R83.36/hr
MNL
Ekurhuleni Mixed Logit
Public Modes (Rail + Taxi)
Private Car Mode
Coefficient
t-value
Wait Time (min)
-0.032
-8.98
-0.038
-3.77
Travel Time (min)
-0.031
-7.31
-0.040
-3.86
Fare (R)
-0.172
-8.16
-0.100
-1.29
No. Transfers
-0.279
-9.89
-0.291
-1.65
BRT ASC
-0.710 -
2.444
2.07
VTTS (Rand/hour)
10.81
24.00
Log-Likelihood
-178.2
-475.8
Prob > |Chi2|
0.000
7 December December 2019

14. Tshwane and Ekurhuleni MNL Tshwane and Ekurhuleni ML
4. Tshwane and Ekurhuleni Consolidated MNL & ML Results (All incomes): Mode VTTS Heterogeneity
Tshwane and Ekurhuleni MNL
Taxi
Rail
Private Car
Coefficient
t-value
Waiting Time
-0.032
-9.58
-0.028
-5.08
-0.027
-4.03
Travel Time
-0.022
-9.56
-0.023
-6.65
-0.034
-9.76
Fare/Cost
-0.209
-11.42
-0.193
-5.48
-0.081
-2.76
No. Transfers
-0.588
-19.62
-0.941
-9.31
-0.146
-2.20
BRT ASC
-0.076
-1.81
-0.211
-3.59
1.589
4.06
VTTS (Rand/Hr)
R6.32
R7.15
R25.18
Log-Likelihood
Prob > |Chi2|
0.000
MNL
Tshwane and Ekurhuleni ML
Taxi
Rail
Private Car
Coefficient
t-value
Waiting Time
-0.055
-8.05
-0.028
-4.06
-0.036
-5.52
Travel Time
-0.041
-7.02
-0.021
-1.52
-0.040
-2.10
Fare/Cost
-0.391
-13.95
-0.269
-9.61
-0.082
-4.01
No. Transfers
-1.125
-9.68
-0.540
-6.24
-0.274
-2.80
BRT ASC
-0.069
-1.07
0.198
1.19
1.866
3.85
VTTS (Rand/Hr)
R6.36
R4.65
R29.65
Log-Likelihood
-955.3
Prob > |Chi2|
0.000
Mixed
Logit
Heterogeneity also identified by income, gender, trip duration, trip purpose.
Notice that the attribute coefficients are indicating a need for operational focus (waiting time, fares, transfers), not infrastructure (CAPEX) focus, i.e. travel time
7 December December 2019

15. 4. Tshwane + Ekurhuleni Mode VTTS Heterogeneity
7 December December 2019

16. 4. Tshwane + Ekurhuleni: Car 95% VTTS Confidence Intervals
Mean VTTS R29.65/hr
Mean VTTS R25.18/hr
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17. 4. Jo’burg MNL & ML VTTS 95% Confidence Intervals by Mode & Income
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18. 4. Summary of VTTS 1990 to 2014 (2017 Rands)
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19. Ex post B/C evaluation of Rea Vaya Ph 1A
4. What if We get the Choice Model and VTTS Wrong?
Ex post B/C evaluation of Rea Vaya Ph 1A
Costs ( )
2012 Rm, i=12%
Capital expenditure
Bus Operating Contract
Other Infrastruct. O&M
Project Planning
Project Staff Labour
Taxi Ind. Negotiations
Total Costs
R3,881
R2,033
R127
R39
R30
R6,149
Benefits ( )
2012 Rm, i=12%
Travel time savings
Road Fatalities avoided
Increased physical actvy.
Vehicle op cost savings
Travel time lost (constr.)
Road accidents avoided
CO2 emissions avoided
Total Benefits
R2,719
R2,046
R1,161
R1,399
R-313
R159
R149
R7,320
TTS Share of total benefits = 37%
VTTS used = R56/hr
BCR = 1.19
At VTTS of R34/hr (-40%), estim. pass = -5%  BCR=1.0 19 19
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Source: EMBARQ: Social, Environmental and Economic Impacts of BRT systems: Bus Rapid Transit Case Studies from Around the World, 2014

20. 5. Conclusions
We still don’t have sufficient insight into travel behaviour and willingness to pay – there are large gaps;
VTTS is lower than estimated in the past;
We must be aware of pitfalls of ‘one size fits all’ transport systems and operations;
In some cases we may have made the wrong feasibility & infrastructure design and operations decisions;
We have to invest in behavioural research when making investment decisions worth R(billions);
We might make the same decisions, but be more aware of the downside risks, and mitigation strategies.
We normally get the forecasts wrong.
When we get it right it’s for the wrong reasons
7 December December 2019

21. Thank you (part 1)

22. Presentation to Southern African Transport Conference
Transit Assignment: Why Logit-Based Methods are Required for South African Transit Networks
Presentation to Southern African Transport Conference
Gary Hayes
Prof. Christo Venter
11th July 2017

23. Presentation Overview
Where does transit assignment fit in?
What is transit assignment so difficult to calibrate?
Optimal Strategy and the Common Line Problem
Logit-Based Transit Assignment
What does this tell us?

24. 1. Mode Choice – Assignment Process
Note that the mode choice model and transit assignment can become more complicated if nested models are needed for transit access mode simulation
Note the need for overall network / system equilibrium
Note that transit lines = services
Note: Behavioural attributes and weightings must be used in mode choice and assignment models.
7 December December 2019

25. 2. Why is Transit Assignment Difficult to Calibrate?
Common line problem
Transit Assignment
Decision Rules
Limited Time Period Models (e.g. 1 hour)
Transit Operations Coding: Routes, Headways, Speeds, Fares, Stops.
Transit Assignment Limitations
Effect of technology on choice?
Large traffic zones don’t help
Capacity Constraints not Modelled
Static, Average Headway based Models
A key issue for transit assignment is how is the mode choice model structured, i.e. how are trip matrices by mode ‘passed’ into the assignment model
Modelling multi-mode trips is also difficult, e.g. bus-taxi or BRT-rail;
Not surprising that transit assignment cannot be calibrated;
High risk when forecasting
Behavioural data: time & cost weightings
7 December December 2019

26. 2. Why is Transit Assignment to Difficult to Calibrate?
Transit assignment calibration is notoriously difficult - goodness of fit criteria are hard to achieve;
Rail = Easiest
Taxi = Hardest
Rail is easiest because the network is limited and has fewer services
When we get down to passenger demand on individual lines, the results are even worse!
However we often base our decisions on the demand of individual lines
Bus = Harder
7 December December 2019

27. 3. Optimal Strategy & The Common Line Problem
A strategy is a set of attractive lines at each boarding decision point encountered by the traveller
The optimal strategy is the minimum generalized cost path;
Common lines can distort the assignment: will all the trips on Route 1 (with end station destination) interchange at Station 2 and use Route 2 if there are marginal travel time savings on Route 2?
If we don’t have an appropriate transfer penalty at Station 2, too many trips will transfer at Station 2 from Route 1 to Route 2
7 December December 2019

28. 3. Optimal Strategy & The Common Line Problem
Total Person trips i to j by taxi from MNL = 100 per hour A
Optimal taxi strategy is Taxi Line 3
Taxi
Line 1
Taxi
Line 3
Transit assignment result:
Taxi
Line 2
Comments:
Arrangement of centroid connectors is critical;
Unrealistic transit assignment result, trips should be spread between three lines;
Difficult to calibrate model link volumes based on transit assignment result;
Using trip matrix estimation approach to estimate base matrices will give incorrect trip ends and incorrect final matrix.
7 December December 2019

29. 4. Variant Transit Assignment (Florian & Constantin)
𝑃 𝑙𝑖𝑛𝑒 𝑖 = 𝑒𝑥𝑝 − 𝜃 𝑥 (𝑈𝑡𝑖𝑙 𝑖) 𝑛=1 𝑛 𝑒𝑥𝑝 − 𝜃 𝑥 (𝑈𝑡𝑖𝑙 𝑛)
P(line i) is the likelihood of a traveller choosing line i;
Θx is the unique scale factor for zone x (value between 0 and 1) that limits the number of competing strategies included in the solution. A value of 0 includes all strategies & value of 1 includes only the cheapest strategy;
n is the number of lines between O-D included in the strategy;
(Util i) is the trip utility of line i between O-D.
7 December December 2019

30. 4. Variant Transit Assignment (Florian & Constantin)
𝑃 𝑙𝑖𝑛𝑒 𝑖 = 𝑒𝑥𝑝 − 𝜃 𝑥 (𝑈𝑡𝑖𝑙 𝑖) 𝑛=1 𝑛 𝑒𝑥𝑝 − 𝜃 𝑥 (𝑈𝑡𝑖𝑙 𝑛)
7 December December 2019

31. 4. Zenith Transit Assignment (Brands, de Romph, Veitch)
Features:
Centroid connectors automatically generated within centroid radius;
Type of transit stop / station can be defined, e.g. park and ride;
Minimum number of stops within radius can be defined (i.e. define more stops or increase radius);
Transit systems that must be reached.
𝑃 𝑙𝑖𝑛𝑒 𝑖 = 𝐹 𝑖 𝑒𝑥𝑝 −𝜃𝑥(𝐶𝑜𝑠𝑡 𝑖) 𝑖=1 𝑛 𝐹 𝑛 𝑒𝑥𝑝 −𝜃𝑥(𝐶𝑜𝑠𝑡 𝑛)
P(line i) is the likelihood of a traveller choosing line i;
Fi is the frequency of line i;
Θx is the zonal service choice parameter (i.e. scale factor) for zone x;
n is the number of lines between O-D;
(Cost i) is the total trip generalised cost of line i between O-D.
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32. 4. Zenith Transit Assignment (Brands, de Romph, Veitch)
Taxi line demand with variation in Line 3 frequency and θ = 0.2.
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33. Conclusions Considered mode choice / assignment models are necessary;
Alternative approaches to standard transit assignment should be investigated;
DCM behavioural data must be used in mode choice and traffic & transit assignment models;
Variant transit assignment approach is relatively simple to implement and automate for zonal scale factor estimation.
7 December December 2019

34. Thank you (part 2)