1. Technical Advisory Committee
July 2, 2014

2. Today’s Agenda Preliminary Demo of GTAModel V4.0
GTAModel V4.0 Component Overview
File System Structure
GTAModel V4.0 Model System
Transit Assignment Calibration
Fare specification for FBTA (time permitting)

3. Component Overview

4. GTAModel V4.0 – Component Outline
Synthetic Population
Place of Residence Place of Work
Place of Residence Place of School
Activity Generation
Location Choice
Mode Choice
Station Access Choice

5. Synthetic Population Based on TTS Household records
Aggregated households on to the planning district level
Redistributed to each zone randomly closely matching population by zone
Requires to be run with access to the TTS Database / Computer
Will be replaced in the future with a superior model in the near future (V4.01)

6. Place of Residence Place of Work
Four occupations
Full-Time / Part-Time
3 worker categories
No Car or no License
License and less cars than drivers
License and at least as many cars as drivers

7. Place of Residence Place of School
Updates proportionally by zone the base year’s linkages.
School zones are assigned discreetly through a random process
If no linkages existed in the base year then another zone from the planning district is used instead.
To help increase the fit we assign school zones without replacement

8. Activity Generation
Activity generation times / rates are generated from TTS data
Activities are first broken down by frequency
Then they are broken down into start times
Finally for each start time it is broken down by duration

9. Location Choice Three location choice models
Market
Work Based Business
Other
The models look at the previous and next episodes and only include feasible trips into the choice set according to auto travel time

10. Mode Choice - Modes Auto Carpool (Interhousehold passenger / taxi)
Passenger (Intrahousehold passenger)
Drive Access Transit (DAT)
Walk Access Transit (WAT)
Bicycle
Walk (all way)
Rideshare (joint trip auto)
Schoolbus

11. Mode Choice - Algorithm
Generate a list of all feasible tours for each non shared mode
If a tour uses a tour based mode compute the tour level utility (needed for DAT)
Find the best tour using the restrictions of what vehicles they use
In our case it is car / no car
Assign available resources to people by tour
Given the resource assignments we then try to match drivers with passengers if it provides a better household level utility
Joint trips then share mode choices with other tour members

12. Station Access Choice
For DAT discrete choice needs to be made to see what station will be chosen to access the transit network
This station will be used for both access and egress
Utilities are calculated on a tour based level
A station is only chosen once the DAT mode has been selected as the wanted mode after vehicles have been assigned

13. File System Structure

14. Overview Activity Distributions BaseYearMatrix Documentation
Employment
HouseholdData
Transit Fares
ZoneData

15. Model System

16. Model System Template -Tasha Runtime

17. Main Components Household Loader Network Data Scheduler Modes
Location Choice Model
Modes
Auto Mode
Other Modes
Shared Modes
Mode Choice
Auto Type ( mainly used for future expansions )
Zone System

18. Expansion Components Pre Run Pre Iteration Post Scheduler
Post Household Iteration
Post Household
Post Iteration
Post Run

19. Used Expansions Pre Run Post Iteration Post Household
Computes Auto and Transit’s initial network data
Post Iteration
Computes next iteration’s Auto and Transit network data
Post Household
Builds then stores the demand matrices for Auto and Transit

20. Resources
Used for storing important pieces of data that are used between multiple modules
The EMME Modeller Controller
Jobs by occupation and employment status
Allow for dynamic loading

21. Traffic Assignment

22. Traffic Assignment Overview and Parameters
Standard Traffic Assignment
Use link-based generalized cost for tolls
Light and Regular Fare Zones for the 407ETR
Includes transit background traffic
Peak Hour Factor (AM/PM)
Representative Hour Factor (Midday, Evening)
Toll Percption

23. TTS Data Extraction
For each time period, extracted the matrix conditional on the following:
Trips started within the time period
Primary mode was D, M, T, or was a drive-access-transit trip
For drive-access-transit trips, only trips to valid network stations were included
No trips were extracted for overnight (assumed freeflow traffic conditions)

24. Cordon Count Data Used 2011 Cordon Count data
Toronto, Durham, York, Peel, and Halton data were included
For AM/PM periods, determined the peak hour of traffic volume
Compared count post data against the traffic volume for the peak hour

25. AM Peak Hour Factor
Use a PHF = 0.437

26. PM Peak Hour
Use a PHF = 0.385

27. Midday/Evening Representative Hour
The highest demand within the midday and evening period comes at the shoulders of the AM and PM periods, respectively
Therefore no need to represent the peak of demand, just divide by the number of hours in the time period
Midday Assignment Period =
Evening Assignment Period = 0.2

28. Toll Perception

29. Screenline Results
[Refer to handout]

30. Transit Assignment

31. Model Overview Congested Transit Assignment (captras.mac port)
Includes ALL transit modes in one assignment
GO Rail, GO Bus, and Subway lines all use segment speeds
All others use line speeds
Assumed no service in the Overnight time period
Walk-on-links permitted
Logit model on outgoing centroid connectors (Emme 4+ only)
Headways based on average arrival interval in the time period
Speeds based on scheduled time and route length

32. Transit Assignment Parameters
Congestion Perception
Fare Perception
Wait time Perception
Walk Perceptions
Toronto Access
Toronto Walk-on-network
Non-Toronto Egress
Non-Toronto Walk-on-network
IVTT Perception = 1.0

33. Transit Assignment Parameters (cont’d)
Boarding Penalties
Subway
Brampton
TTC Bus
Durham
YRT
GO Bus
VIVA
GO Train
Halton
Hamilton
Mississauga
Streetcar (Regular, XROW)

34. Transit Assignment Parameters (cont’d)
Boading Penalty Perception (not estimated)
GO Train Headway fraction
Consistently estimated to be 0.2 (compared to 0.5 for the rest of the network)
Walking speed
Assignment Period
The only parameter which is different for each time period

35. TTS Data Extraction - Demand
Extracting transit demand matrices
Assigned to the 4 time periods based on trip start time
5 classes of trip:
Walk-acces-egress transit (WAE)
Station-access, walk-egress GO (SAG)
Station-access, walk-egress Subway (SAS)
Walk-access, station-egress GO (SEG)
Walk-access, station-egress Subway (SES)
Exluded trips which used a transit route not in the network

36. TTS Data Extraction - Demand
For SAS/SES:
First/last route respectively must be TTC
Subway station of access/egress must have a TTC parking lot
For SAG/SEG, the first/last route respectively must be GO

37. TTS Data Extraction – Calibration Data
For each time period, for each trip class (WAE, SAG, SAS, SEG, SES):
Boardings for each transit route
Matrix of transfers, including initial boardings and final alightings, for the following groups of transit lines:
Brampton Transit
MiWay
Durham Region Transit
TTC Bus
GO Bus
TTC Streetcar
GO Train
TTC Subway
Oakville, Burlington, Milton Transit
York Region Transit
VIVA
Hamilton Street Railway

38. Estimation Procedure Extract TTS demand matrices
Determine time period assignment period
For each model:
Determine parameters
Code tool
Estimate parmeter values using XTMF
Calibrate parameter values by hand
Repeat 3 until satisfied with model results

39. Transit Assignment Period
Used to calculate transit line capacity
Represents the number of hours for which the demand matrix is applied
Inverse of the Peak Hour Factor (which gets applied to the demand matrix for Auto Assignment)
A different value is required for each time period

40. AM Assignment Period Start End Trip Starts 6:00 6:59 101,045 6:15 7:14
157,558
6:30
7:29
178,371
6:45
7:44
224,476
7:00
7:59
239,287
Peak Hour Factor
Assignment Period
7:15
8:14
273,443
0.49
2.04
7:30
8:29
268,165
7:45
8:44
242,053
8:00
8:59
218,245
Total
558,578

41. PM Assignment Period Start End Trip Starts 15:00 15:59 173,731 15:15
16:14
187,511
15:30
16:29
182,630
15:45
16:44
195,199
16:00
16:59
199,193
16:15
17:14
229,627
Peak Hour Factor
Assignment Period
16:30
17:29
232,947
0.33
3.03
16:45
17:44
218,193
17:00
17:59
212,767
17:15
18:14
159,503
17:30
18:29
150,470
17:45
18:44
123,037
18:00
18:59
119,225
704,915

42. Midday, Evening Assignment Periods
The highest demand within the midday and evening period comes at the shoulders of the AM and PM periods, respectively
Therefore no need to represent the peak of demand, just use the # of hours in the period
Midday Assignment Period = 6.0
Evening Assignment Period = 5.0

43. Model 1 Description Congested transit assignment (5 iterations)
Estimated on AM dataset
NOT using fare perception (i.e. not fare-based)

44. Model 1 Parameter Values
Congestion Penalty Perception
0.41
Wait Time Perception
2.23
Toronto Walk Perception
1.2
Non-Toronto Walk Perception
1.3
Boarding Penalty Perception
0.8
GO Train Headway Fraction
0.2
Walking speed (km/hr) 5
-Access and walk-on-network folded into a single parameter

45. Model 1 Parameter Values
Brampton Boarding Penalty 9
Durham Boarding Penalty
2.5
GO Bus Boarding Penalty 15
GO Train Boarding Penalty
7.5
Halton Boarding Penalty
2.75
Hamilton Boarding Penalty 6
MiWay Boarding Penalty 3
Streetcar Boarding Penalty
Streetcar XROW Boarding Penalty
Subway Boarding Penalty
2.25
TTC Bus Boarding Penalty 2
YRT Boarding Penalty 5
VIVA Boarding Penalty

46. Model 1 Results: AM Boardings
Group
TTS
Model
Error
% Error
Brampton
21,096
17,925
,170
-15%
Durham
10,055
9,172
-9%
GO Bus
10,330
16,269
5,939
57%
GO Train
98,304
101,130
2,826 3%
Halton
6,796
6,105
-10%
Hamilton
22,952
19,168
,784
-16%
Mississauga
36,760
41,457
4,697
13%
Streetcar
56,887
52,549
,337
-8%
Subway
361,520
356,509
,011
-1%
TTC Bus
318,859
315,096
,763
VIVA
6,986
7,916
930
YRT
16,119
13,628
,491
AM TOTAL
966,664
956,925
,739

47. Model 1 Results: AM Transfers
[Please refer to handouts]

48. Model 1 Results: PM Boardings
Group
TTS
Model
Error
% Error
Brampton
27,428
23,603
,824
-14%
Durham
10,522
11,043
520 5%
GO Bus
14,048
18,360
4,313
31%
GO Train
98,622
113,421
14,799
15%
Halton
8,882
8,003
-10%
Hamilton
30,131
25,375
,756
-16%
Mississauga
51,447
58,566
7,119
14%
Streetcar
77,676
63,440
,236
-18%
Subway
470,499
439,530
,969
-7%
TTC Bus
415,776
397,171
,605
-4%
VIVA
9,934
12,715
2,782
28%
YRT
20,190
17,046
,143
PM TOTAL
1,235,154
1,188,274
,880

49. Model 1 Results: PM Transfers
[Please refer to handouts]

50. Model 2 Description Congested transit assignment Using Fare Perception
Still work-in-progress for GTAModel 4.1
Could include transit speed updating

51. Fare Based Transit Assignment

52. Overview
To get around technical limitations of Emme’s transit assignment implementation, a “hyper-network” is required to model transit fares
A Python tool has been developed to generate this network
This process has been abstracted and generalized to be specified using a single XML file, interpreted by the tool
This way, arbitrary fare scenarios can be coded up and designed

53. Transit line groups
The XML file contains a list of transit line groups
Each group is defined by a selector expression (i.e. an expression which is true for all transit lines in the group)
Each group becomes a ‘layer’ in the hyper network
In other words, transfers between lines in the same group are free
In order for the algorithm to be work, each transit line must belong to a group
Each transit line belongs to one group only

54. Fare Rules Each rule consists of a cost (positive or negative)
Costs can be applied to either links or transit segments
LINK rule types: initial boarding, transfer
SEGMENT rule types: zone crossing, in-vehicle distance
Rules are additive
E.g. if a link qualifies for two rules, its assigned cost will be the sum of both rules
The procedure checks for negative costs once all rules have been applied

55. FBTA Miscellaneous
Example Fare Schema File (.xml) for 2012 is available from the TMG Website
A report has been written documenting usage and summarizing details (also available on the website)
New fare rules could possibly be implemented if requested, as long as they are representable in Emme (e.g. cannot do origin-destination-specific fares)

56. Thank you!
Any questions?