Appendix A - EcoTrain Survey

Appendix Page 1 Mini stère des Transports du Québec, Ontario M inistry of Transportation, and Transport Canada – Updated Feasibility Study of a High Speed Rail Service in the Québec City – Windsor C orridor

145 File No. : 3301 -08-AH01 – N/Réf. : P020563 -0700 --100-EN-00 – November 2010

Appendix Page 2

Mini stère des Transports du Québec, Ontario M inistry of Transportation, and Transport Canada – Updated Feasibility Study of a High Speed Rail Service in the Québec City – Windsor C orridor

146 File No. : 3301 -08-AH01 – N/Réf. : P020563 -0700 --100-EN-00 – November 2010

Appendix Page 3

Appendix B - EcoTrain Nesting Structure

Appendix Page 4 Mini stère des T ransports du Québec, Ontario M inistry of Transportation, and Transport Canada – Updated Feasibility Study of a High Speed Rail Service in the Québec City – Windsor C orridor

65 File No. : 3301 -08-AH01 – N/Réf. : P020563 -0700 --100-EN-00 – November 2010

Appendix Page 5

rail] and New Existing Mode [Automobile, Bus, Air, VIA Mode [HSR] Air, HSR and Slow Mode [Automobile, Bus, VIA rail] ast Mode [Air, HSR], and Public Slow Automobile, Public F Mode [Bus, VIA rail]

tructures Tested Figure 14 : Nesting S

re tested including the following: timation, multiple nesting structures we For the NL model es

Fast Mode [Air, HSR] and Slow Mode [Automobile, Bus, VIA rail] hway [Air, HSR, Highway [Automobile, Bus] and Non-Hig VIA rail]

and Slow Mode [Bus, VIA rail] HSR, Automobile, Air,

Appendix C - SurveyMonkey Questions

Appendix Page 6 Quebec-Windsor Intercity Modal Choice Survey

Dear Survey Respondent,

You have been chosen to participate in a research study conducted by the Department of Civil Engineering at the University of Toronto. The aim of this study is to better understand a trip makers’ decision process when travelling between cities in Canada.

We are contacting a random sample of people in the Greater Toronto and Hamilton Area (GTHA) to gather information on their local and intercity trip mode choices. Collected information will be anonymous and stored on secured servers. This data will only be used for research at the University of Toronto.

The survey is designed to be as short as possible and will take approximately 15 minutes to complete. Your cooperation and time are highly appreciated.

Please note that at any point in the survey, it is possible to revise previously answered questions. You can also leave the session and complete the survey at a later time (if web cookies are enabled on your internet browser). If you exit the survey accidentally, you can restart using the same link that you used to start the survey; however, in this case it is possible that the entries you have already made may be lost.

If you lose your Internet connection for a short time, it is usually possible to continue normally once the connection is restored, as long as your browser is still open at the page you were on.

Yours sincerely,

Billy Wong M.ASc Candidate Department of Civil Engineering University of Toronto

Appendix Page 7 Quebec-Windsor Intercity Modal Choice Survey

Other What is intercity travel? Intercity travel are any trips made where the destination is not located in the same urban area boundaries as the origin location. For example, a trip from Toronto to Montreal would be considered an intercity trip.

How can I make an intercity trip? Intercity trips can be made through a variety of travel modes. The most popular modes of travel are personal vehicles, bus (such as Megabus and Greyhound), rail (such as Via Rail), or airplane. Non­motorized modes are not normally feasible for such trips.

What about trips within the GTA (ie between Mississauga and Markham)? For the scope of this research, trips between municipalities in the GTA are considered local trips. However, we are still interested in understanding the frequency of travel within the GTA compared to an individual's intercity trip frequency.

Other What about trips between the GTA and Hamilton? Under the scope of this project, trips between Hamilton and the GTA are considered intercity trips.

1. If you any any further questions about intercity trips, please feel free to ask in the box below. these suggestions will be helpful for future research purposes.

Appendix Page 8 Quebec-Windsor Intercity Modal Choice Survey

Other 2. When was the last intercity trip that you made in the following categories? Definition of each category is available below. Within the past Within the past Within the past Within the past Within the past Beyond a year Not applicable 7 days 31 days 3 months half year year Inside Province (i.e. Toronto nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj to Waterloo)

National (i.e. Toronto to nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Vancouver)

International (i.e. Toronto to nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj nmlkj Shanghai)

Trips within the GTA are not considered intercity trips, i.e. Toronto to Mississauga

Appendix Page 9 Quebec-Windsor Intercity Modal Choice Survey

Other 3. In the last 12 months, have you made a recent intercity trip within the Quebec­Windsor corridor? Please refer to the map below for major urban areas located within the Quebec­ Windsor corridor.

nmlkj Yes

nmlkj No

Other

Other

source: wikipedia.com

Appendix Page 10 Quebec-Windsor Intercity Modal Choice Survey

You have indicated that you have made an intercity trip in the Quebec­Windsor corridor within the past year. The following section is about your most recent intercity trip made within the Quebec­Windsor corridor. Please refer to the following image of the Quebec­Windsor corridor region. If you have made an error, you can select the back button to correct the previous question.

This trip is a one­way trip originating from your home location within the GTA

Other

Other

Other

source: wikipedia.com

4. When was your most recent intercity trip? Please choose the closest option.

nmlkj Within the past 7 days

nmlkj Within the past 31 days

nmlkj Other Within the past 3 months

nmlkj Within the past 6 months

nmlkj Within the past year

nmlkj Beyond a year

Appendix Page 11 Quebec-Windsor Intercity Modal Choice Survey 5. In your most recent intercity trip, what was the trip destination?

nmlkj Montreal Other nmlkj Ottawa

nmlkj Mississauga

nmlkj Quebec City

nmlkj Hamilton

nmlkj London

nmlkj Kitchener­Waterloo

nmlkj St. Catharines

nmlkj Oshawa

nmlkj Windsor Other

nmlkj Sherbrooke

nmlkj Kingston

nmlkj Trois­Rivieres

nmlkj Other (please specify)

Other

6. In your most recent intercity trip, what was the trip purpose?

nmlkj Business

nmlkj Personal (non­business)

nmlkj Social (meeting family/friends)

nmlkj Recreational (activity such as camping or vacation)

nmlkj Other

7. Did you depart for this trip on a weekday or weekend?

nmlkj Weekday

nmlkj Weekend

8. How many other household members were present during this trip? 6 Other

Appendix Page 12 Quebec-Windsor Intercity Modal Choice Survey 9. In your most recent intercity trip, what was the main travel mode was used?

nmlkj Private Car

nmlkj Bus (GO Bus, Greyhound, Megabus)

nmlkj Rail (GO Train, Via Rail)

nmlkj Airplane Other

nmlkj Other (Please specify below)

Appendix Page 13 Quebec-Windsor Intercity Modal Choice Survey

10. Did you rent the vehicle used to make this intercity trip?

nmlkj Yes

nmlkj No

Appendix Page 14 Quebec-Windsor Intercity Modal Choice Survey

11. What was the approximate cost of renting a vehicle for this trip? 6

Appendix Page 15 Quebec-Windsor Intercity Modal Choice Survey Other 12. If applicable, how did you access the [Q9] station? Please check all that apply.

gfedc Park and Ride

gfedc Dropped Off

gfedc Transit (eg TTC, VIVA)

gfedc Taxi

gfedc Shuttle Service

gfedc Non Motorized

gfedc Other (please specify)

Other

13. If applicable, which travel mode(s) did you use when leaving the [Q9] station? Please check all that apply.

gfedc Park and Ride

gfedc Dropped Off

gfedc Transit

gfedc Taxi

gfedc Shuttle Service Other gfedc Non Motorized

gfedc Other (please specify)

Appendix Page 16 Quebec-Windsor Intercity Modal Choice Survey

The following section investigates how different factors influence your choice for how you make intercity trips. For each of the six situations presented, choose the transportation type that you are most likely to take given the information presented.

For bus, rail, and airplane travel types, there are a number of methods to get to the departure station. For example, an individual from downtown Toronto may use the TTC to get to Pearson International Airport to fly to Montreal.

For intercity bus trips (Megabus or Greyhound), the departure station is the bus terminal at Toronto Coach Terminal located at Bay & Dundas, near Other the Eaton Centre in downtown Toronto. For intercity train trips (Via Rail or high speed rail), the departure station is at Union Station in downtown Toronto. For intercity airplane trips (Air Canada or Westjet), the departure station is at Pearson International Airport located in Mississauga.

This stated preference section is independent from any previous trips that may have been made. For each of the six situations, you are travelling to a chosen location in Montreal from your home location. It is assumed that you are headed directly to your destination using the shortest distance possible with no prolonged stops along the way.

Definitions Other

Attribute Definition Access Method The method used to get to the airport, train station, or bus station. Access Time The time it takes to get to the airport, train station, or bus station. Egress Method The method used to get to the final destination in Montreal from the airport, train station, or bus station. Egress Time The time it takes to get to the final destination in Montreal from the airport, train station, or bus station. Percentage On Time The probability that you will arrive on time at the destination. Departures Per Day The number of times that a given travel mode will depart from the GTA in a day. Number of Transfers Number of times that one has to switch travel modes during one leg of the intercity trip. Seat Choice The type of seating that is available on the chosen travel mode. Trip Information Type of information that is given to passengers. Main Travel Time The time it takes to get one­way between Toronto and Montreal. Travel Cost The cost of a one­way ticket between Toronto and Montreal. First Class/Business Class Cost The cost of a first class or business class one­way ticket between Toronto and Montreal.

Total Travel Time The door­to­door time for a one­way trip from Toronto to Montreal. Total Travel Cost The total cost of travelling door­to­door from your home to the intended destination in Montreal.

The following questions are related to a hypothetical trip from the GTA to Montreal

14. For this hypothetical stated preference trip to Montreal, what is your trip purpose?

nmlkj Business

nmlkj Personal (non­business)

nmlkj Social (meeting family/friends)

nmlkj Recreational

Appendix Page 17 Quebec-Windsor Intercity Modal Choice Survey 15. Do you want to know more about high speed rail in Canada?

nmlkj Yes

nmlkj No

Appendix Page 18 Quebec-Windsor Intercity Modal Choice Survey

Basic Facts About High Speed Rail

­ High speed rail (HSR) are passenger trains that go over 200 km/h ­ Current VIA rail trains have a max speed of 160 km/h ­ Canada is the only G8 country without HSR service ­ HSR service would have less stops along the route compared to current rail options

Please press "Next" to continue the survey

Appendix Page 19 Quebec-Windsor Intercity Modal Choice Survey

36. What is the first three (3) digits of your postal code in Toronto?

nmlkj M1B nmlkj M4A nmlkj M5V

nmlkj M1C nmlkj M4B nmlkj M5W

nmlkj M1E nmlkj M4C nmlkj M5X

nmlkj M1G nmlkj M4E nmlkj M6A

nmlkj M1H nmlkj M4G nmlkj M6B

nmlkj M1J nmlkj M4H nmlkj M6C

nmlkj M1K nmlkj M4J nmlkj M6E

nmlkj M1L nmlkj M4K nmlkj M6G

nmlkj M1M nmlkj M4L nmlkj M6H

nmlkj M1N nmlkj M4M nmlkj M6J

nmlkj M1P nmlkj M4N nmlkj M6K

nmlkj M1R nmlkj M4P nmlkj M6L

nmlkj M1S nmlkj M4R nmlkj M6M

nmlkj M1T nmlkj M4S nmlkj M6N

nmlkj M1V nmlkj M4T nmlkj M6P

nmlkj M1W nmlkj M4V nmlkj M6R

nmlkj M1X nmlkj M4W nmlkj M6S

nmlkj M2H nmlkj M4X nmlkj M7A

nmlkj M2J nmlkj M4Y nmlkj M7Y

nmlkj M2K nmlkj M5A nmlkj M8V

nmlkj M2L nmlkj M5B nmlkj M8W

nmlkj M2M nmlkj M5C nmlkj M8X

nmlkj M2N nmlkj M5E nmlkj M8Y

nmlkj M2P nmlkj M5G nmlkj M8Z

nmlkj M2R nmlkj M5H nmlkj M9A

nmlkj M3A nmlkj M5J nmlkj M9B

nmlkj M3B nmlkj M5K nmlkj M9C

nmlkj M3C nmlkj M5L nmlkj M9L

nmlkj M3H nmlkj M5M nmlkj M9M

nmlkj M3J nmlkj M5N nmlkj M9N

nmlkj M3K nmlkj M5P nmlkj M9P

Appendix Page 20 Quebec-Windsor Intercity Modal Choice Survey

41. In a hypothetical future trip to Montreal, please choose a location that best suits a future trip destination within the city. 6

If you are not sure about the specific borough in Montreal, you can refer to the map below and choose the blue location flag closest to your destination. The green flags indicate the airport, rail, and bus arrival locations in Montreal from the typical intercity travel modes.

Loading...

Appendix Page 21 Quebec-Windsor Intercity Modal Choice Survey

Other Scenario 1 of 6 From your home location in Toronto to Ville­Marie (click for map)

Driving / Carpooling / Bus Rail Airplane Passenger Access Method Transit (Cost = $3­$6) Drop Off (Cost = $0) Transit (Cost = $3­ Access Time 0h 12m 0h 12m 0h 36m Walk/Bike (Cost = $0­ Egress Method Taxi (Cost = $10­$30) Pick Up (Cost = $0) $5) Egress Time 0h 18m 0h 0m 0h 18m Other Percentage On Time 70% 80% 90% 70% Departures Per Day 11 8 14 Number of Transfers 2 Transfers Direct Direct Seat Choice First Come First Serve First Come First Serve Assigned Seating Trip Information Real­Time Schedule Pre­Posted Schedule Real­Time Schedule Main Travel Time 4h 24m 9h 42m 5h 48m 1h 30m Travel Cost $42 $36 $94 $338 First Class/Business Class Cost $118 $592

Click this link for definition of terms. Travel cost for driving/carpooling/passenger does not include parking fees or costs.

54. Please select your preferred mode from Scenario 1

nmlkj Automobile nmlkj Bus nmlkj Rail nmlkj Airplane nmlkj High Speed Rail

55.Other How confident are you in selecting the above travel mode? If you have no doubts about your choice then you are very confident. If there were two or three choices that you could have picked, then you would be not confident.

nmlkj Very Not Confident nmlkj Not Confident nmlkj Neither nmlkj Confident nmlkj Very Confident

Appendix Page 22 Quebec-Windsor Intercity Modal Choice Survey

Scenario 2 of 6 From your home location in Toronto to Ville­Marie (click for map)

Driving / Carpooling / Bus Rail Airplane Passenger Access Method Drop Off (Cost = $0) Taxi (Cost = $10­$30) Drop Off (Cost = $0) Access Time 0h 0m 0h 6m 0h 36m

Egress Method Transit (Cost = $3­$6) Pick Up (Cost = $0) Taxi (Cost = $10­$30)

Egress Time 0h 6m 0h 6m 0h 12m Percentage On Time 70% 70% 80% 90% Departures Per Day 16 8 7 Number of Transfers Direct 2 Transfers Direct Seat Choice Pre­booked Seating First Come First Serve Pre­booked Seating Trip Information Pre­Posted Schedule Mobile Schedule Mobile Schedule Main Travel Time 8h 12m 5h 12m 4h 6m 1h 30m Travel Cost $84 $27 $71 $169 First Class/Business Class Cost $142 $254

Click this link for definition of terms. Travel cost for driving/carpooling/passenger does not include parking fees or costs.

56. Please select your preferred mode from Scenario 2

nmlkj Automobile nmlkj Bus nmlkj Rail nmlkj Airplane nmlkj High Speed Rail

57. How confident are you in selecting the above travel mode? If you have no doubts about your choice then you are very confident. If there were two or three choices that you could have picked, then you would be not confident.

nmlkj Very Not Confident nmlkj Not Confident nmlkj Neither nmlkj Confident nmlkj Very Confident

Appendix Page 23 Quebec-Windsor Intercity Modal Choice Survey

Scenario 3 of 6 From your home location in Toronto to Ville­Marie (click for map)

Driving / Carpooling / Bus Rail Airplane Passenger Walk/Bike (Cost = $0­ Access Method Transit (Cost = $3­$6) Drop Off (Cost = $0) $5) Access Time 0h 6m 0h 18m 0h 24m Egress Method Pick Up (Cost = $0) Rental (Cost = $50) Rental (Cost = $50) Egress Time 0h 6m 0h 6m 0h 30m Percentage On Time 90% 80% 70% 70% Departures Per Day 6 4 21 Number of Transfers 2 Transfers Direct 1 Transfer Seat Choice Pre­booked Seating Assigned Seating Pre­booked Seating Trip Information Pre­Posted Schedule Real­Time Schedule Mobile Schedule Main Travel Time 4h 24m 9h 42m 7h 36m 1h 12m Travel Cost $56 $27 $71 $225 First Class/Business Class Cost $89 $282

Click this link for definition of terms. Travel cost for driving/carpooling/passenger does not include parking fees or costs.

58. Please select your preferred mode from Scenario 3

nmlkj Automobile nmlkj Bus nmlkj Rail nmlkj Airplane nmlkj High Speed Rail

59. How confident are you in selecting the above travel mode? If you have no doubts about your choice then you are very confident. If there were two or three choices that you could have picked, then you would be not confident.

nmlkj Very Not Confident nmlkj Not Confident nmlkj Neither nmlkj Confident nmlkj Very Confident

Appendix Page 24 Quebec-Windsor Intercity Modal Choice Survey

Scenario 4 of 6 From your home location in Toronto to Ville­Marie (click for map)

Driving / Carpooling / Bus Rail Airplane Passenger Walk/Bike (Cost = $0­ Access Method Transit (Cost = $3­$6) Transit (Cost = $3 $5) Access Time 0h 24m 0h 6m 1h 48m Walk/Bike (Cost = $0­ Egress Method Pick Up (Cost = $0) Transit (Cost = $3 $5) Egress Time 0h 12m 0h 6m 0h 24m Percentage On Time 80% 90% 80% 80% Departures Per Day 16 12 7 Number of Transfers Direct 2 Transfers 1 Transfer Seat Choice Assigned Seating Pre­booked Seating Assigned Seating Trip Information Mobile Schedule Real­Time Schedule Pre­Posted Schedule Main Travel Time 6h 18m 7h 30m 5h 48m 0h 48m Travel Cost $70 $36 $94 $282 First Class/Business Class Cost $165 $423

Click this link for definition of terms. Travel cost for driving/carpooling/passenger does not include parking fees or costs.

60. Please select your preferred mode from Scenario 4

nmlkj Automobile nmlkj Bus nmlkj Rail nmlkj Airplane nmlkj High Speed Rail

61. How confident are you in selecting the above travel mode? If you have no doubts about your choice then you are very confident. If there were two or three choices that you could have picked, then you would be not confident.

nmlkj Very Not Confident nmlkj Not Confident nmlkj Neither nmlkj Confident nmlkj Very Confident

Appendix Page 25 Quebec-Windsor Intercity Modal Choice Survey

Scenario 5 of 6 From your home location in Toronto to Ville­Marie (click for map)

Driving / Carpooling / Bus Rail Airplane Passenger Access Method Taxi (Cost = $10­$30) Transit (Cost = $3­$6) Transit (Cost = $3­ Access Time 0h 6m 0h 12m 0h 36m Egress Method Taxi (Cost = $10­$30) Pick Up (Cost = $0) Rental (Cost = $50) Egress Time 0h 6m 0h 6m 0h 30m Percentage On Time 80% 70% 90% 90% Departures Per Day 6 12 14 Number of Transfers 1 Transfer 1 Transfer Direct Seat Choice Assigned Seating Pre­booked Seating First Come First Serve Trip Information Real­Time Schedule Pre­Posted Schedule Real­Time Schedule Main Travel Time 6h 18m 5h 12m 7h 36m 0h 48m Travel Cost $42 $54 $141 $225 First Class/Business Class Cost $212 $450

Total Travel Time 6h 18m 5h 18m 7h 54m 1h 54m

Click this link for definition of terms. Travel cost for driving/carpooling/passenger does not include parking fees or costs.

62. Please select your preferred mode from Scenario 5

nmlkj Automobile nmlkj Bus nmlkj Rail nmlkj Airplane nmlkj High Speed Rail

63. How confident are you in selecting the above travel mode? If you have no doubts about your choice then you are very confident. If there were two or three choices that you could have picked, then you would be not confident.

nmlkj Very Not Confident nmlkj Not Confident nmlkj Neither nmlkj Confident nmlkj Very Confident

Appendix Page 26 Quebec-Windsor Intercity Modal Choice Survey

Scenario 6 of 6 From your home location in Toronto to Ville­Marie (click for map)

Driving / Carpooling / Bus Rail Airplane Passenger

Access Method Drop Off (Cost = $0) Drop Off (Cost = $0) Taxi (Cost = $10­$30)

Access Time 0h 0m 0h 12m 0h 24m Egress Method Rental (Cost = $50) Transit (Cost = $3­$6) Pick Up (Cost = $0) Egress Time 0h 6m 0h 18m 0h 18m Percentage On Time 90% 90% 70% 80% Departures Per Day 11 4 21 Number of Transfers 1 Transfer 1 Transfer 1 Transfer Seat Choice First Come First Serve Assigned Seating First Come First Serve Trip Information Mobile Schedule Mobile Schedule Pre­Posted Schedule Main Travel Time 8h 12m 7h 30m 4h 6m 1h 12m Travel Cost $56 $45 $118 $169 First Class/Business Class Cost $177 $212

Click this link for definition of terms. Travel cost for driving/carpooling/passenger does not include parking fees or costs.

64. Please select your preferred mode from Scenario 6

nmlkj Automobile nmlkj Bus nmlkj Rail nmlkj Airplane nmlkj High Speed Rail

65. How confident are you in selecting the above travel mode? If you have no doubts about your choice then you are very confident. If there were two or three choices that you could have picked, then you would be not confident.

nmlkj Very Not Confident nmlkj Not Confident nmlkj Neither nmlkj Confident nmlkj Very Confident

Appendix Page 27 Quebec-Windsor Intercity Modal Choice Survey

The following sections inquires about your daily travel and activity patterns.

66. What is your typical weekday travel purpose?

nmlkj Work

nmlkj School

nmlkj Other (Please specify below)

Appendix Page 28 Quebec-Windsor Intercity Modal Choice Survey

67. What is the first three digits your work address' postal code? (for example: M5T) If you cannot remember, where is your typical work location (closest intersection or landmark) 5

6

68. What transportation mode do you typically use during the weekday? Check all that apply.

gfedc Car driver

gfedc Car passenger

gfedc Carpool

gfedc Transit

gfedc Park and Ride

gfedc Kiss and Ride

gfedc Bicycle

gfedc Walk

gfedc Other (Please specify below)

Appendix Page 29 Quebec-Windsor Intercity Modal Choice Survey

69. What transportation mode do you typically use during the weekday? Check all that apply.

gfedc Car driver

gfedc Car passenger

gfedc Carpool

gfedc Transit

gfedc Park and Ride

gfedc Kiss and Ride

gfedc Bicycle

gfedc Walk

gfedc Other (Please specify below)

Appendix Page 30 Quebec-Windsor Intercity Modal Choice Survey

70. What is your most regular type of weekday trip? Please choose one from the list below.

nmlkj Shopping

nmlkj Recreational

nmlkj Household Maintenance

nmlkj Other (Please specify below)

71. What transportation mode do you typically use during the weekday? Check all that apply.

gfedc Car driver

gfedc Car passenger

gfedc Carpool

gfedc Transit

gfedc Park and Ride

gfedc Kiss and Ride

gfedc Bicycle

gfedc Walk

gfedc Other (Please specify below)

Appendix Page 31 Quebec-Windsor Intercity Modal Choice Survey

72. What is your typical weekday travel time (door to door)? 6

73. What is your typical weekday travel cost? 6

Appendix Page 32 Quebec-Windsor Intercity Modal Choice Survey

74. What is your age?

nmlkj Between 18 and 24

nmlkj Between 25 and 30

nmlkj Between 31 and 40

nmlkj Between 41 and 50

nmlkj Between 51 and 65

nmlkj Over 65

nmlkj Undisclosed

75. What is your gender?

nmlkj Male

nmlkj Female

nmlkj Other

nmlkj Undisclosed

76. What is your marital status?

nmlkj Single

nmlkj Married

nmlkj Divorced

nmlkj Widowed

nmlkj Undisclosed

77. Including yourself, how many people live in your household? 6

78. How many persons above 18 years of age reside in your household? 6

79. How many automobiles does your household own? 6

Appendix Page 33 Quebec-Windsor Intercity Modal Choice Survey

80. What types of vehicles do you own? 0 1 2 3 or more Sedan nmlkj nmlkj nmlkj nmlkj

SUV nmlkj nmlkj nmlkj nmlkj

Van nmlkj nmlkj nmlkj nmlkj

Pick­up Truck nmlkj nmlkj nmlkj nmlkj

Other nmlkj nmlkj nmlkj nmlkj

Appendix Page 34 Quebec-Windsor Intercity Modal Choice Survey

81. What is the household's annual income? 6

Appendix Page 35 Quebec-Windsor Intercity Modal Choice Survey

82. How did you hear about this survey?

nmlkj Facebook

nmlkj Twitter

nmlkj Email Referral

nmlkj Forum Link

nmlkj Poster

nmlkj Other

Other (please specify)

Appendix Page 36 Quebec-Windsor Intercity Modal Choice Survey

Thank you for taking the time to complete the survey.

83. If you have any comments regarding the survey, please enter them in the space below. Feedback is very much appreciated. 5

6

Data collected from your participation in this survey will only be used for research purposes at the University of Toronto.

If you have further questions about the survey, please contact [email protected]. Thank you for your time and have a great day.

Appendix Page 37

Appendix D - Ngene Input Code

Appendix Page 38 Design ?Orthogonal ;alts = Auto,Bus,Rail,Air,HSR ;rows = 6 ;eff = (mnl,d) ;model:

U(Auto)= + b5*ONTIME[0.7,0.8,0.9] + b11*TT[0.70,1.00,1.30] + b13*COST[0.75,1.00,1.25,1.50] /

U(Bus) = b1*ACCESSmode[1,2,3,4] + b2*ACCESStime[0.5,1.0,1.5] + b3*EGRESSmode[1,2,3,4,5] + b4*EGRESStime[0.5,1.0,1.5] + b5*ONTIME[0.7,0.8,0.9] + b6*FREQ[0.5,1.0,1.5] + b7*TRANSFER1[0,1,2] + b9*CROWD[1,2,3] + b10*TRIPinfo[1,2,3] + b11*TT[0.70,1.00,1.30] + b13*COST[0.75,1.00,1.25,1.50] /

U(Rail)= b1*ACCESSmode[1,2,3,4] + b2*ACCESStime[0.5,1.0,1.5] + b3*EGRESSmode[1,2,3,4,5] + b4*EGRESStime[0.5,1.0,1.5] + b5*ONTIME[0.7,0.8,0.9] + b6*FREQ[0.5,1.0,1.5] + b7*TRANSFER1[0,1,2] + b9*CROWD[1,2,3] + b10*TRIPinfo[1,2,3] + b11*TT[0.70,1.00,1.30] + b13*COST[0.75,1.00,1.25,1.50] + b14*PREMcost[1.25,1.50,1.75,2.00]/

U(Air) = b1*ACCESSmode[1,2,3,4] + b2*ACCESStime[0.5,1.0,1.5] + b3*EGRESSmode[1,2,3,4,5] + b4*EGRESStime[0.5,1.0,1.5] + b5*ONTIME[0.7,0.8,0.9] + b6*FREQ[0.5,1.0,1.5] + b8*TRANSFER2[0,1] + b9*CROWD[1,2,3] + b10*TRIPinfo[1,2,3] + b11*TT[0.70,1.00,1.30] + b13*COST[0.75,1.00,1.25,1.50] + b14*PREMcost[1.25,1.50,1.75,2.00]/

U(HSR) = b1*ACCESSmode[1,2,3,4] + b2*ACCESStime[0.5,1.0,1.5] + b3*EGRESSmode[1,2,3,4,5] + b4*EGRESStime[0.5,1.0,1.5] + b5*ONTIME[0.7,0.8,0.9] + b6*FREQ[0.5,1.0,1.5] + b8*TRANSFER2[0,1] + b9*CROWD[1,2,3] + b10*TRIPinfo[1,2,3] + b11*TT[0.70,1.00,1.30] + b13*COST[0.75,1.00,1.25,1.50] + b14*PREMcost[1.25,1.50,1.75,2.00] $

Appendix Page 39

Appendix E - Postal Code Map

Appendix Page 40 Appendix Page 41

Appendix F - GeoMidpoint Calculation Methods

Appendix Page 42 Appendix Page 43 Appendix Page 44 Appendix Page 45 Appendix Page 46

Appendix G - Average Longitude & Latitude Calculation Steps

Appendix Page 47 1. A new column was created to display only the FSA designation for each postal code. The Excel function left was used to return the three (3) left characters from the six-character postal code. The designation of the Excel function is: =left(text,[num of characters]) 2. A column was created to designate a change in FSA. This is indicated by a binary value where 1 designated the last postal code record of one FSA and 0 designated a continuation of the current FSA. This binary value is used as a filtering tool as well as a stop function for the next step. An if-statement was used and the designation of the Excel function is: =if(FSA in current row = FSA in next row, 0, 1) 3. This created column functions to keep count of the number of successive postal code records under the same FSA designation. The count would start at 1 and increase by a value of one until the corresponding binary indicator, from the previous step, changes from 0 to 1. This function is used to keep count of the number of entries to know the multiple to average out the coordinates. The designation of the Excel function is: =if(corresponding binary value = 1, 1, previous count value + 1) 4. The next two columns are the cumulative latitude and longitude coordinates for each FSA. The coordinates for each postal code record are cumulatively added until a new FSA designation is encountered. The designation of the Excel function is: =if(value of count = 1, corresponding latitude/longitude, corresponding latitude/longitude + previous cumulative latitude/longitude value) 5. The final two columns are the average latitude and average longitude. A value is present in these two columns only when the binary indicator value is one, which stipulates that the next postal code record would be under a new FSA designation. The averaged latitude and longitude is calculated by dividing the cumulative latitude/longitude coordinates in that last postal code record by the total count of postal code records in that FSA – indicated by the count function. The designation of the Excel function is: =cumulative latitude/longitude value divided by corresponding count.

Appendix Page 48

Appendix H - Excel Worksheet Examples

Appendix Page 49 Variable

Car Bus Rail Air HSR Method of accessing station or airport 1 Drop Off (Cost = $0) Drop Off (Cost = $0) Drop Off (Cost = $0) Drop Off (Cost = $0) 2 Transit (Cost = $3-$6) Transit (Cost = $3-$6) Transit (Cost = $3-$6) Transit (Cost = $3-$6) 3 Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) 4 Walk/Bike (Cost = $0-$5) Walk/Bike (Cost = $0-$5) Walk/Bike (Cost = $0-$5) Walk/Bike (Cost = $0-$5) Time to get to station or airport 1 Existing * 0.75 Existing * 0.75 Existing * 0.75 Existing * 0.75 2 Existing * 1.00 Existing * 1.00 Existing * 1.00 Existing * 1.00 3 Existing * 1.25 Existing * 1.25 Existing * 1.25 Existing * 1.25 Method of egressing station or airport 1 Pick Up (Cost = $0) Pick Up (Cost = $0) Pick Up (Cost = $0) Pick Up (Cost = $0) 2 Transit (Cost = $3-$6) Transit (Cost = $3-$6) Transit (Cost = $3-$6) Transit (Cost = $3-$6) 3 Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) 4 Rental (Cost = $50) Rental (Cost = $50) Rental (Cost = $50) Rental (Cost = $50) 5 Walk/Bike (Cost = $0-$5) Walk/Bike (Cost = $0-$5) Walk/Bike (Cost = $0-$5) Walk/Bike (Cost = $0-$5) Time to get from station or airport 1 Existing * 0.75 Existing * 0.75 Existing * 0.75 Existing * 0.75 2 Existing * 1.00 Existing * 1.00 Existing * 1.00 Existing * 1.00 3 Existing * 1.25 Existing * 1.25 Existing * 1.25 Existing * 1.25 Percentage of trips on time 1 70% 70% 70% 70% 70% 2 80% 80% 80% 80% 80% 3 90% 90% 90% 90% 90% Service frequency 1 Headway * 0.5 Headway * 0.5 Headway * 0.5 Headway * 0.5 2 Headway * 1.0 Headway * 1.0 Headway * 1.0 Headway * 1.0 3 Headway * 1.5 Headway * 1.5 Headway * 1.5 Headway * 1.5 Interchanges 0 Direct Direct Direct Direct 1 1 Transfer 1 Transfer 1 Transfer 1 Transfer 2 2 Transfers 2 Transfers Blank Blank Seat Choice 1 Pre-booked Seating Pre-booked Seating Pre-booked Seating Pre-booked Seating 2 Assigned Seating Assigned Seating Assigned Seating Assigned Seating 3 First Come First Serve First Come First Serve First Come First Serve First Come First Serve Level of trip information provided 1 Mobile Schedule Mobile Schedule Mobile Schedule Mobile Schedule 2 Real-Time Schedule Real-Time Schedule Real-Time Schedule Real-Time Schedule 3 Pre-Posted Schedule Pre-Posted Schedule Pre-Posted Schedule Pre-Posted Schedule Time spent in car, bus, train, or airplane 1 Existing * 0.75 Existing * 0.75 Existing * 0.75 Existing * 0.75 Existing * 0.75 2 Existing * 1.00 Existing * 1.00 Existing * 1.00 Existing * 1.00 Existing * 1.00 3 Existing * 1.25 Existing * 1.25 Existing * 1.25 Existing * 1.25 Existing * 1.25 Total travel cost (standard) 1 Existing * 0.75 Existing * 0.75 Existing * 0.75 Existing * 0.75 Existing * 0.75 2 Existing * 1.00 Existing * 1.00 Existing * 1.00 Existing * 0.80 Existing * 1.00 3 Existing * 1.25 Existing * 1.25 Existing * 1.25 Existing * 1.00 Existing * 1.25 4 Existing * 1.50 Existing * 1.50 Existing * 1.50 Existing * 1.25 Existing * 1.50 Total travel cost (premium) 1 Cost * 1.25 Cost * 1.25 Cost * 1.25 2 Cost * 1.50 Cost * 1.50 Cost * 1.50 3 Cost * 1.75 Cost * 1.75 Cost * 1.75 4 Cost * 2.00 Cost * 2.00 Cost * 2.00

Appendix Page 50 Access

Transit Time Transit Time Distance to Auto Time to to Bay & Walk Time to Distance to Auto Time to to Union Walk Time to Distance to Auto Time to Transit Time Walk Time to Num City Latitude Longitude Postal Code Bay & Dundas Bay & Dundas Dundas Bay & Dundas Union Station Union Station Station Union Station Pearson Pearson to Pearson Pearson 1 Ajax 43.842 -79.023 L1S 44.9 40 100 480 47.3 38 90 480 45.2 40 150 680 2 Ajax 43.870 -79.046 L1T 45 50 105 480 47.6 39 90 480 45.8 42 120 680 3 Ajax 43.872 -79.012 L1Z 46.9 42 120 510 49.4 41 110 510 56.7 44 150 700 4 Aurora 43.997 -79.469 L4G 49.2 44 70 480 51.8 44 55 491 58.1 46 80 600 5 Brampton 43.783 -79.702 L6P 43 41 120 410 42.1 39 110 420 16.7 18 60 210 6 Brampton 43.755 -79.753 L6R 49.6 45 110 460 48.8 43 105 470 22.6 25 70 210 7 Brampton 43.733 -79.733 L6S 46 43 110 435 45.1 41 90 440 20.7 23 60 180 8 Brampton 43.717 -79.700 L6T 37.9 41 85 400 37.1 39 75 410 19.3 19 45 140 9 Brampton 43.703 -79.761 L6V 44.9 41 100 460 44.2 39 70 465 24.3 22 70 220 10 Brampton 43.679 -79.735 L6W 40.9 39 75 420 40.2 37 60 420 20.3 20 60 200 11 Brampton 43.681 -79.785 L6X 45.9 44 90 480 45.1 42 80 490 25.2 26 80 240 12 Brampton 43.659 -79.752 L6Y 42.2 41 80 440 41.4 38 70 445 21.5 22 75 220 13 Brampton 43.726 -79.794 L6Z 48.3 43 100 500 47.5 41 80 510 27.6 24 75 265 14 Brampton 43.703 -79.823 L7A 51.1 48 120 525 50.4 46 90 540 30.5 29 100 300 15 Burlington 43.374 -79.760 L7L 50.3 44 85 600 49.1 39 80 595 45.3 36 110 613 16 Burlington 43.380 -79.815 L7M 54.3 48 130 630 53.7 46 120 628 47.3 38 100 589 17 Burlington 43.348 -79.779 L7N 53.9 48 115 632 53.2 46 100 630 49 41 140 650 18 Burlington 43.357 -79.839 L7P 57.7 51 120 669 57 48 110 667 50.2 41 155 628 19 Burlington 43.337 -79.800 L7R 57.5 50 105 661 56.8 48 85 659 52.5 42 130 665 20 Burlington 43.324 -79.808 L7S 58.1 51 120 682 57.4 48 110 679 53.2 42 140 686 21 Burlington 43.310 -79.843 L7T 61.4 52 120 725 60.7 50 115 722 54.5 43 145 710 22 Caledon 43.800 -79.877 L7C 60.5 52 120 621 59.9 50 110 630 40.3 34 100 463 23 Caledon 43.894 -79.762 L7E 54.7 54 90 552 53.5 49 80 558 28.8 32 90 371 24 Caledon 43.875 -80.001 L7K 73.2 64 180 768 72.5 61 160 777 53 45 140 530 25 Clarington 44.058 -78.775 L0B 91.8 81 999 930 94.1 88 999 930 102 88 999 1140 26 Clarington 43.913 -78.593 L1B 81.5 64 160 930 83.8 62 144 935 77.4 60 150 910 27 Clarington 43.915 -78.687 L1C 75.7 62 146 840 78 60 131 840 85.8 64 150 1067 28 Clarington 43.906 -78.790 L1E 65.6 58 135 730 67.9 56 120 735 75.7 60 126 960 29 East Gwillimbury 44.107 -79.491 L9N 61.1 53 110 628 63.7 53 100 646 61.5 56 105 751 30 Georgina 44.279 -79.323 L0E 82.2 60 200 960 84.7 70 190 980 91 70 210 1094 31 Georgina 44.225 -79.468 L4P 69.1 61 140 816 71.8 61 130 834 78 63 160 941 32 Halton Hills 43.594 -79.922 L0P 61.6 55 140 660 60.4 50 140 670 41.3 37 120 570 33 Halton Hills 43.648 -79.911 L7G 60.2 57 105 627 59.5 55 90 632 40 39 110 398 34 Halton Hills 43.632 -80.037 L7J 75.4 66 145 788 74.8 64 120 793 55.2 48 125 559 35 King 44.039 -79.601 L0G 57.5 60 100 645 69.3 52 100 660 49.1 38 120 585 36 King 43.940 -79.537 L7B 52.2 51 130 465 54.9 50 130 480 38.4 33 120 472 37 Markham 43.881 -79.264 L3P 36.4 34 60 400 38.9 32 75 420 44.7 34 120 540 38 Markham 43.849 -79.326 L3R 28.8 28 75 330 31.3 26 75 345 37.1 28 80 450 39 Markham 43.843 -79.271 L3S 35.2 35 100 350 37.7 34 100 360 39.6 36 110 480 40 Markham 43.822 -79.395 L3T 25.4 30 60 250 28 28 60 270 33.1 28 90 360 41 Markham 43.885 -79.235 L6B 38.3 36 90 430 40.7 35 85 440 46.4 35 75 600 42 Markham 43.890 -79.336 L6C 34.2 35 110 400 36.6 34 100 420 42.6 35 90 540 43 Markham 43.899 -79.265 L6E 38.8 38 60 435 41.2 37 50 440 46.9 37 90 590 44 Markham 43.849 -79.331 L6G 28.5 27 70 340 30.9 25 50 340 36.6 25 60 480 45 Markham 43.910 -79.246 L0H 41 39 110 580 43.4 38 92 590 49.3 38 90 615 46 Milton 43.516 -79.870 L9T 59.4 53 115 622 58.7 51 95 626 39.1 35 135 433 47 Mississauga 43.713 -79.643 L4T 32.9 32 80 320 32.1 30 75 330 8 13 30 90 48 Mississauga 43.698 -79.622 L4V 30.9 30 90 300 30 28 75 300 5.1 10 15 30 49 Mississauga 43.637 -79.619 L4W 31.5 31 75 270 30.6 29 70 270 10 11 30 100 50 Mississauga 43.617 -79.582 L4X 24.1 27 70 220 23.2 25 60 240 13 18 45 135 51 Mississauga 43.603 -79.594 L4Y 22.9 27 70 240 22 25 60 240 14.4 20 50 150 52 Mississauga 43.613 -79.647 L4Z 35.1 34 90 300 33.8 29 80 310 14.4 15 40 180 53 Mississauga 43.586 -79.610 L5A 26.4 28 75 270 25.7 26 60 270 16.7 21 50 225 54 Mississauga 43.577 -79.630 L5B 27.2 30 70 300 26.4 28 70 300 19.4 21 60 250 55 Mississauga 43.564 -79.650 L5C 29.3 34 80 320 28.6 32 60 330 21.3 22 75 270 56 Mississauga 43.583 -79.564 L5E 21.6 24 70 240 20.8 21 50 240 16.3 16 60 240 57 Mississauga 43.563 -79.583 L5G 23.9 27 60 270 23.2 25 50 265 18.6 19 75 270 58 Mississauga 43.541 -79.614 L5H 27.7 28 75 310 26.9 26 60 310 22.3 20 105 330 59 Mississauga 43.517 -79.633 L5J 31.2 31 60 350 30.5 28 45 345 25.9 23 90 360 60 Mississauga 43.532 -79.661 L5K 31.5 32 110 360 30.7 30 75 360 26.2 24 80 330 61 Mississauga 43.537 -79.692 L5L 35.4 38 95 380 34.7 36 90 390 25.6 23 90 320 62 Mississauga 43.567 -79.716 L5M 44.3 41 75 400 43.5 40 60 410 23.6 23 75 300 63 Mississauga 43.587 -79.757 L5N 43.2 41 70 450 42.5 38 50 450 22.6 22 80 280 64 Mississauga 43.688 -79.631 L5P 29.3 29 80 290 28.6 27 80 300 4.2 9 20 45 65 Mississauga 43.604 -79.669 L5R 37 35 90 330 36.2 33 80 340 16.3 16 60 220 66 Mississauga 43.679 -79.676 L5S 35.3 35 70 340 34.6 33 70 360 18.8 15 45 120 67 Mississauga 43.656 -79.669 L5T 34.6 32 90 330 33.9 30 90 340 13.9 13 45 150 68 Mississauga 43.595 -79.691 L5V 39.5 38 110 360 38.8 35 80 370 18.9 20 60 240 69 Mississauga 43.631 -79.717 L5W 37.7 34 90 390 37 32 80 400 17.1 16 60 180 70 Newmarket 44.041 -79.474 L3X 53.1 47 70 540 55.7 46 65 550 57.1 49 90 655 71 Newmarket 44.059 -79.458 L3Y 53.1 45 95 573 55.7 45 85 591 62 47 110 696 72 Oakville 43.477 -79.703 L6H 40.4 38 90 444 39.7 35 80 442 35.5 30 85 420 73 Oakville 43.468 -79.661 L6J 40.9 38 80 418 40.3 36 70 416 36 31 145 436 74 Oakville 43.440 -79.685 L6K 40.9 37 95 465 40.2 35 90 463 36 29 130 483 75 Oakville 43.407 -79.711 L6L 45.4 41 90 522 44.7 39 90 520 40.4 34 145 540 76 Oakville 43.443 -79.739 L6M 44.2 42 105 507 43.5 40 90 505 39.3 35 90 482 77 Oshawa 43.916 -78.863 L1G 62.6 54 146 677 64.9 52 130 682 72.7 56 136 911 78 Oshawa 43.892 -78.842 L1H 62.2 51 130 680 71.6 55 140 690 70.2 53 120 914 79 Oshawa 43.890 -78.877 L1J 57.8 49 100 640 60.2 47 100 645 67.9 51 155 874 80 Oshawa 43.930 -78.842 L1K 65.6 58 130 710 68 57 120 720 75.6 60 120 944 81 Oshawa 43.953 -78.890 L1L 67.1 62 120 710 69.7 62 110 720 76.5 63 100 932 82 Pickering 43.834 -79.106 L1V 39 37 90 400 41.5 36 75 420 48.8 39 120 600 83 Pickering 43.818 -79.092 L1W 40 37 90 420 42.6 35 75 430 49.8 38 135 620 84 Pickering 43.854 -79.096 L1X 40.5 38 100 420 43 36 100 435 50.3 39 140 620 85 Pickering 43.959 -79.085 L1Y 55.7 51 90 615 58.2 49 90 620 65.7 59 110 780 86 Richmond Hill 43.855 -79.401 L4B 28.7 30 75 300 31.2 30 75 315 32.3 30 120 390 87 Richmond Hill 43.870 -79.439 L4C 34 35 70 300 36.5 33 70 320 32.5 30 100 400 88 Richmond Hill 43.942 -79.455 L4E 42.5 40 90 400 45 38 90 420 40.4 40 120 490 89 Richmond Hill 43.894 -79.422 L4S 35.1 35 90 320 37.6 34 90 360 36.3 36 120 450 90 Scugog 44.168 -79.103 L0C 81.4 87 120 900 83.9 76 106 900 89.9 77 130 1050 91 Scugog 44.095 -78.974 L9L 77.8 70 140 858 80.4 70 130 867 86.3 70 180 1054 92 Toronto 43.806 -79.208 M1B 28.4 29 80 320 30.8 28 60 330 38.2 31 100 525 93 Toronto 43.787 -79.155 M1C 34 34 75 315 36.4 32 45 320 43.8 35 90 550 94 Toronto 43.766 -79.191 M1E 21.1 32 60 260 34.3 31 30 260 41.7 34 110 500 95 Toronto 43.768 -79.218 M1G 28 30 90 260 30.4 28 60 260 37.8 31 105 480 96 Toronto 43.766 -79.240 M1H 25.8 29 70 240 28.3 27 45 250 35.7 30 100 450 97 Toronto 43.746 -79.236 M1J 18.4 31 60 220 22.4 29 40 220 38.8 35 100 460 98 Toronto 43.729 -79.264 M1K 17.1 26 50 180 19.6 24 50 190 36.3 32 90 420 99 Toronto 43.709 -79.285 M1L 12.9 23 45 150 16.2 22 45 150 36.3 33 85 405 100 Toronto 43.726 -79.232 M1M 15.6 25 55 190 16.9 26 45 190 40.1 38 95 470 101 Toronto 43.696 -79.264 M1N 11.5 20 55 140 12.9 21 50 150 38.5 38 90 435 102 Toronto 43.759 -79.271 M1P 19.7 27 70 225 22.1 25 70 240 33.6 28 90 430 103 Toronto 43.746 -79.306 M1R 15.6 20 60 180 18 18 60 190 31.9 26 90 380 104 Toronto 43.793 -79.271 M1S 23.5 27 70 270 25.9 25 70 280 33.3 28 75 460

Appendix Page 51 Access

105 Toronto 43.781 -79.304 M1T 20.1 24 60 240 22.5 22 60 250 29.9 25 80 420 106 Toronto 43.818 -79.282 M1V 28.1 32 75 315 30.5 30 80 320 38 33 90 470 107 Toronto 43.798 -79.321 M1W 21.7 26 60 260 24.1 24 60 270 31.6 28 80 420 108 Toronto 43.826 -79.222 M1X 30.7 33 90 345 33.1 31 60 350 40.6 34 100 540 109 Toronto 43.801 -79.359 M2H 20.9 25 60 240 23.4 23 60 260 28.2 27 75 390 110 Toronto 43.780 -79.349 M2J 17.8 21 50 210 20.2 20 50 230 26.2 22 70 380 111 Toronto 43.777 -79.383 M2K 20.7 26 55 200 23.2 24 55 215 24.4 22 80 360 112 Toronto 43.753 -79.380 M2L 14.1 23 45 145 23.4 21 45 170 23.8 20 60 320 113 Toronto 43.791 -79.411 M2M 16.2 28 50 200 26.7 27 50 210 24.6 23 65 340 114 Toronto 43.768 -79.409 M2N 13.4 24 33 160 24.7 25 34 180 21.8 20 60 320 115 Toronto 43.748 -79.400 M2P 11.4 21 32 140 12.6 25 33 160 21.5 19 45 300 116 Toronto 43.778 -79.441 M2R 16.3 30 55 190 18.5 34 55 210 22.7 24 75 300 117 Toronto 43.753 -79.330 M3A 15.3 21 60 190 17.7 19 60 200 29.1 26 75 370 118 Toronto 43.746 -79.358 M3B 15.7 21 50 160 18.1 19 50 180 26.4 22 60 340 119 Toronto 43.726 -79.343 M3C 12.2 17 52 140 14.6 15 55 150 32.5 28 75 345 120 Toronto 43.751 -79.446 M3H 14 25 45 160 16.7 30 45 180 18.4 18 60 265 121 Toronto 43.763 -79.489 M3J 18.6 28 60 210 19.9 33 60 230 18 20 60 240 122 Toronto 43.734 -79.469 M3K 14.4 25 45 160 15.7 31 40 180 16.3 16 60 220 123 Toronto 43.735 -79.510 M3L 20.5 30 60 210 34.8 32 60 220 13.8 15 60 200 124 Toronto 43.731 -79.493 M3M 17 27 60 190 18.3 33 51 205 14.9 16 55 200 125 Toronto 43.756 -79.518 M3N 23.6 30 70 240 36.4 32 60 250 15.4 15 60 210 126 Toronto 43.727 -79.312 M4A 12.7 17 50 150 15.1 15 50 170 32.6 25 80 370 127 Toronto 43.707 -79.308 M4B 11 21 50 120 14.2 18 47 140 35.3 31 90 390 128 Toronto 43.691 -79.313 M4C 8.8 16 35 100 10.1 17 32 110 38 33 73 385 129 Toronto 43.678 -79.296 M4E 7.9 15 45 96 9.2 16 35 100 34.9 33 90 410 130 Toronto 43.706 -79.370 M4G 8.8 15 40 90 12.2 17 40 110 29.4 30 75 310 131 Toronto 43.704 -79.348 M4H 10.4 16 40 95 12.9 14 40 110 35.3 30 80 340 132 Toronto 43.686 -79.336 M4J 6.9 14 35 80 8.8 17 31 90 37 32 70 360 133 Toronto 43.680 -79.352 M4K 4.9 11 25 60 9.8 13 25 70 35.4 30 65 345 134 Toronto 43.671 -79.321 M4L 6 11 30 70 7.7 14 35 80 33.3 31 85 385 135 Toronto 43.663 -79.344 M4M 4 9 25 50 4.9 10 27 50 30.5 27 85 360 136 Toronto 43.727 -79.396 M4N 8.5 15 28 105 9.8 20 28 120 23.9 22 60 275 137 Toronto 43.709 -79.398 M4P 6.2 13 21 75 7.4 17 22 90 25.4 24 60 280 138 Toronto 43.713 -79.406 M4R 7.3 14 33 90 8.6 18 34 105 22.3 23 80 270 139 Toronto 43.702 -79.389 M4S 6.2 12 26 70 7.2 16 27 90 24 27 80 290 140 Toronto 43.690 -79.387 M4T 5.1 10 19 53 5.7 15 20 70 25 27 75 300 141 Toronto 43.686 -79.398 M4V 3.9 8 23 46 5.2 14 24 65 24.2 25 80 280 142 Toronto 43.676 -79.382 M4W 2.9 8 16 35 4.1 12 17 50 30.5 30 65 310 143 Toronto 43.668 -79.370 M4X 2.5 6 20 30 3.7 10 22 43 30 28 70 330 144 Toronto 43.673 -79.377 M4Y 2 5 12 19 3.3 10 12 36 29.6 27 62 315 145 Toronto 43.656 -79.366 M5A 1.9 5 14 24 2.2 7 15 26 28.5 26 80 340 146 Toronto 43.659 -79.380 M5B 0.5 3 6 6 1.9 8 9 23 29.2 27 70 325 147 Toronto 43.657 -79.383 M5C 1.1 5 11 15 1.1 5 7 13 27.9 24 70 330 148 Toronto 43.649 -79.376 M5E 1.7 6 14 22 0.9 4 10 10 27.4 22 65 340 149 Toronto 43.657 -79.386 M5G 0.2 1 2 2 1.6 7 8 21 27.9 27 70 320 150 Toronto 43.651 -79.396 M5H 1.3 5 10 13 1.2 5 6 12 27.2 25 60 320 151 Toronto 43.647 -79.387 M5J 1.7 6 13 20 1 5 6 6 26.3 21 65 325 152 Toronto 43.648 -79.386 M5K 1.5 6 12 16 1.2 4 5 5 26.8 23 65 330 153 Toronto 43.652 -79.387 M5L 1.5 6 12 17 0.6 2 7 7 27 23 65 330 154 Toronto 43.731 -79.418 M5M 10.1 19 40 110 11.4 23 40 130 19.8 18 60 265 155 Toronto 43.711 -79.419 M5N 7.9 16 40 90 9.2 20 40 110 20.5 21 80 255 156 Toronto 43.697 -79.412 M5P 5.4 11 32 65 6.8 17 33 85 21.6 23 75 260 157 Toronto 43.674 -79.402 M5R 3.2 7 20 36 4.5 13 17 52 24.7 27 58 285 158 Toronto 43.664 -79.398 M5S 2.1 4 16 22 3.2 8 15 38 28.2 28 60 300 159 Toronto 43.654 -79.398 M5T 2.3 5 8 15 2.4 7 14 30 26.7 25 70 300 160 Toronto 43.646 -79.399 M5V 2.9 9 25 32 2.7 8 18 23 25.8 21 75 310 161 Toronto 43.647 -79.375 M5W 1.7 7 15 21 0.9 4 8 8 27.7 23 70 335 162 Toronto 43.649 -79.381 M5X 0.9 4 8 11 0.6 2 8 9 27.1 24 70 325 163 Toronto 43.723 -79.444 M6A 11 21 30 120 12.6 27 29 140 16.9 17 40 240 164 Toronto 43.708 -79.444 M6B 10.1 19 25 110 11.9 24 23 125 18.3 19 70 230 165 Toronto 43.690 -79.433 M6C 7.2 15 34 80 8.5 20 33 100 22 23 70 245 166 Toronto 43.689 -79.449 M6E 8.6 16 45 100 9.8 21 42 120 19.7 23 70 230 167 Toronto 43.669 -79.420 M6G 4.9 10 24 51 6.2 15 23 70 24.4 27 54 270 168 Toronto 43.665 -79.435 M6H 5.8 12 35 70 7.2 17 29 81 20.9 26 55 255 169 Toronto 43.650 -79.413 M6J 2.7 8 13 32 3.8 10 22 44 25.1 25 70 290 170 Toronto 43.643 -79.428 M6K 5.5 14 35 66 5.2 13 28 60 22.6 20 70 280 171 Toronto 43.714 -79.485 M6L 15.7 24 60 160 32.1 28 60 180 14.6 15 45 190 172 Toronto 43.693 -79.479 M6M 11.8 22 45 135 13.2 27 42 145 14.5 16 50 190 173 Toronto 43.675 -79.479 M6N 10.8 21 50 120 13.3 23 50 130 16.5 20 105 210 174 Toronto 43.662 -79.459 M6P 8.5 18 36 90 8.5 18 36 90 22.6 22 50 230 175 Toronto 43.648 -79.446 M6R 5.7 13 29 70 6.8 15 37 80 21.7 22 62 260 176 Toronto 43.654 -79.483 M6S 9.7 20 35 110 12 17 34 120 17 2 43 215 177 Toronto 43.664 -79.389 M7A 1.3 3 12 16 2.6 9 14 34 28.6 28 65 3.6 178 Toronto 43.723 -79.282 M7Y 15.7 23 50 160 18.1 21 50 180 35.6 31 95 410 179 Toronto 43.607 -79.500 M8V 15.8 20 63 155 15.1 18 63 154 17.4 18 70 240 180 Toronto 43.601 -79.537 M8W 18.7 21 70 195 18 19 55 195 13.7 15 60 210 181 Toronto 43.652 -79.512 M8X 14.4 22 39 140 13.7 20 37 150 14.6 18 40 180 182 Toronto 43.634 -79.497 M8Y 12.8 19 50 135 11.9 17 45 135 17.8 17 52 220 183 Toronto 43.630 -79.517 M8Z 15.2 18 50 150 14.5 16 50 155 15.4 14 51 200 184 Toronto 43.666 -79.525 M9A 16.9 25 55 170 16.1 23 50 180 9.9 15 50 160 185 Toronto 43.652 -79.552 M9B 22.4 24 60 180 21.7 22 55 195 9.2 11 50 145 186 Toronto 43.645 -79.573 M9C 21.4 22 60 210 20.6 20 55 215 9.2 10 41 125 187 Toronto 43.758 -79.559 M9L 24.8 33 90 270 34.6 35 92 263 14.6 18 70 185 188 Toronto 43.735 -79.536 M9M 22.2 30 65 230 31.8 29 60 240 10.9 12 60 180 189 Toronto 43.705 -79.515 M9N 21.6 29 60 175 19 31 60 190 12.9 15 42 160 190 Toronto 43.693 -79.531 M9P 16.7 30 60 185 18.8 28 55 200 9.8 15 40 135 191 Toronto 43.689 -79.556 M9R 28.1 27 60 210 27.3 25 60 220 7.6 10 35 105 192 Toronto 43.741 -79.582 M9V 24.5 33 80 260 32.5 32 75 270 10.3 16 45 140 193 Toronto 43.718 -79.580 M9W 30.7 32 80 250 30 29 80 260 6.8 10 37 120 194 Uxbridge 44.109 -79.126 L9P 72.6 66 100 791 75.6 73 90 801 81.1 66 160 946 195 Vaughan 43.826 -79.587 L4H 34.4 41 120 390 47.3 45 100 370 26.2 25 120 270 196 Vaughan 43.812 -79.449 L4J 32.9 32 60 240 35.5 30 60 250 28.4 25 90 300 197 Vaughan 43.810 -79.505 L4K 24.1 35 75 270 41.8 34 75 300 22.3 20 100 270 198 Vaughan 43.793 -79.580 L4L 30.5 37 100 330 41.9 37 90 340 19.1 21 80 210 199 Vaughan 43.859 -79.516 L6A 36.3 42 90 345 43.9 40 70 360 28.2 27 70 360 200 Vaughan 43.813 -79.642 L0J 43.9 43 125 105 42.7 38 123 540 18 21 105 240 201 Whitby 43.961 -78.965 L1M 62 57 100 667 64.6 57 90 676 72.3 61 85 886 202 Whitby 43.882 -78.934 L1N 53.4 45 90 575 56 44 95 583 63.7 48 120 813 203 Whitby 43.893 -78.971 L1P 51.6 47 150 561 54.2 47 135 570 61.8 50 137 800 204 Whitby 43.912 -78.938 L1R 56.8 51 150 610 59.4 50 162 620 67 54 160 848 205 Whitchurch-Stouffville 43.979 -79.254 L4A 48.7 50 100 560 51.2 48 90 570 62.4 50 150 680

Bay and Dundas Greyhound Union Station Front Pearson International

Appendix Page 52 Distance Auto Time Transit Time Walk Time Distance Auto Time Transit Time Walk Time Distance Auto Time Transit Time Walk Time Num Borough Longitude Latitude from Bus from Bus from Bus from Bus from Rail from Rail from Rail from Rail from Airport from Airport from Airport from Airport Borough 1 Ahuntsic-Cartierville 45.535 -73.710 14.6 25 45 170 17 23 45 175 15.1 20 80 170 Ahuntsic-Cartierville 2 Anjou 45.601 -73.559 11.4 18 40 140 13.3 23 52 165 25.8 27 85 310 Anjou 3 Côte-des-Neiges—Notre-Dame-de-Grâce 45.491 -73.632 8.4 18 19 95 6.9 16 19 80 13.5 16 50 155 Côte-des-Neiges—Notre-Dame-de-Grâce 4 Lachine 45.445 -73.685 16.3 19 50 175 14 16 50 160 6.9 9 45 80 Lachine 5 LaSalle 45.429 -73.631 13.8 19 44 160 11.4 16 45 130 12.2 14 65 140 LaSalle 6 Le Plateau-Mont-Royal 45.522 -73.577 1.6 4 10 17 3.6 9 19 42 22.6 23 60 230 Le Plateau-Mont-Royal 7 Le Sud-Ouest 45.456 -73.594 10.6 17 30 100 6.9 13 30 75 15.6 17 55 180 Le Sud-Ouest 8 L’Île-Bizard—Sainte-Geneviève 45.497 -73.874 35.3 41 90 370 33 38 90 370 18.4 27 80 180 L’Île-Bizard—Sainte-Geneviève 9 Mercier—Hochelaga-Maisonneuve 45.567 -73.529 8 16 30 90 10 19 43 110 29.9 30 75 320 Mercier—Hochelaga-Maisonneuve 10 Montréal-Nord 45.600 -73.630 14.2 20 52 170 16.1 25 60 190 24 27 95 270 Montréal-Nord 11 Outremont 45.513 -73.606 5 13 30 52 5 14 35 50 16.5 23 65 190 Outremont 12 Pierrefonds-Roxboro 45.468 -73.866 32.4 36 82 365 30.1 33 75 360 15.5 22 70 160 Pierrefonds-Roxboro 13 Rivière-des-Prairies—Pointe-aux-Trembles 45.658 -73.540 18.5 29 75 225 20.4 34 80 250 30.7 33 120 390 Rivière-des-Prairies—Pointe-aux-Trembles 14 Rosemont—La Petite-Patrie 45.532 -73.614 5.5 11 22 60 6.1 13 30 75 17.2 23 70 195 Rosemont—La Petite-Patrie 15 Saint-Laurent 45.516 -73.668 13.5 25 47 155 16.9 24 35 150 13.2 20 75 150 Saint-Laurent 16 Saint-Léonard 45.587 -73.591 11.4 19 40 135 13.3 24 55 160 21.1 24 90 270 Saint-Léonard 17 Verdun 45.452 -73.573 10.9 19 26 105 6.3 16 33 80 18.1 20 60 200 Verdun 18 Ville-Marie 45.509 -73.568 2.1 6 3 13 1.7 4 12 17 20.3 20 45 235 Ville-Marie 19 Villeray—Saint-Michel—Parc-Extension 45.550 -73.599 6.1 13 25 70 8.4 18 33 95 18.7 24 70 220 Villeray—Saint-Michel—Parc-Extension Egress Appendix Page 53 Destination

1 Montreal Distance (km) 550 Travel Time (min) 380 Auto Frequency (per day) Cost ($) 56 Distance (km) 650 Bus Travel Time (min) 534 (Grey) Frequency (per day) 8 Cost ($) 46 Distance (km) 550 Bus Travel Time (min) 360 (Mega) Frequency (per day) 13 Cost ($) 25 Distance (km) 550 Travel Time (min) 350 Rail Frequency (per day) 8 Cost ($) 94 Distance (km) 500 Travel Time (min) 70 Air Frequency (per day) 14 Cost ($) 225 Distance (km) 550 Travel Time (min) 180 HSR Frequency (per day) 20 Cost ($) 150

Appendix Page 54 Schedules

Avg Freq Avg TT Avg Cost 14.33 1.17 225.12

Destination Mode Date Frequency Travel Time Travel Cost Montreal Greyhound 2012.10.24 9 8.84 45.50 Montreal Greyhound 2012.10.27 8 9.00 45.50 Montreal Greyhound 2012.11.21 9 8.84 45.50 Montreal Megabus 2012.10.24 13 5.96 16.54 Montreal Megabus 2012.11.18 13 5.97 41.00 Montreal Megabus 2012.10.31 13 5.97 16.38 Montreal Via Rail 2012.10.24 9 5.82 92.50 Montreal Via Rail 2012.10.24 7 5.86 96.00 Montreal Via Rail 2012.11.12 9 5.81 96.00 Montreal Westjet 2012.10.24 10 1.17 236.00 Montreal Westjet 2012.10.27 4 1.17 240.75 Montreal Westjet 2012.11.20 10 1.20 166.70 Montreal Air Canada 2012.10.24 22 1.16 258.86 Montreal Air Canada 2012.10.28 17 1.16 226.00 Montreal Air Canada 2012.11.15 23 1.16 222.39

Appendix Page 55 Origin 1 Ajax L1S Destination 1 Ahuntsic-Cartierville Scenario 1

Auto Bus Rail Air HSR Auto Bus Rail Air HSR Access Mode 0 2 1 3 2 Access Mode Transit (Cost = $3-$6) Drop Off (Cost = $0) Taxi (Cost = $10-$30) Transit (Cost = $3-$6) Access Time 0 1.5 1.5 0.5 0.5 Access Time 150 57 20 45 Egress Mode 0 3 3 1 1 Egress Mode Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) Pick Up (Cost = $0) Pick Up (Cost = $0) Egress Time 0 1.5 0.5 1 1 Egress Time 37.5 11.5 20 23 On Time 0.7 0.8 0.9 0.7 0.9 On Time 70 80 90 70 90 Frequency 0 1 1 1 0.5 Frequency 11 8 14 10 Transfers 0 2 0 0 1 Transfers 2 Transfers Direct Direct 1 Transfer Seat Choice 0 3 3 2 1 Crowding First Come First Serve First Come First Serve Assigned Seating Pre-booked Seating Trip Information 0 2 3 2 1 Trip Information Real-Time Schedule Pre-Posted Schedule Real-Time Schedule Mobile Schedule Travel Time 0.7 1.3 1 1.3 0.7 Travel Time 4.43 9.69 5.83 1.52 2.10 Travel Cost 0.75 1 1 1.5 1 Cost 42.00 36.00 94.00 338.00 150.00 Premium Cost 0 0 1.25 1.75 1.25 Premium Cost 118.00 592.00 188.00

Access Travel Times Total Travel Time 4.43 12.81 6.98 2.18 3.23 Bus Rail Air HSR Total Travel Cost 42.00 61.00 114.00 358.00 155.00 Auto Time 40 38 40 38 Transit Time 100 90 150 90 Walk Time 480 480 680 480 Access Check 2 1 4 2

Destination SP Single Auto Bus Rail Air HSR Grey Mega Distance (km) 550 650 550 550 500 550 Travel Time (min) 380 534 360 350 70 180 Frequency (per day) 8 13 8 14 20 Cost ($) 56 46 25 94 225 150

Egress Travel Times Bus Rail Air HSR Auto Time 25 23 20 23 Transit Time 45 45 80 45 Walk Time 170 175 170 175 Egress Check 5 3 1 1 Appendix Page 56 Row Origin Destination SP Scenario AUTO.ONTIME AUTO.TT AUTO.TC BUS.ACC BUS.ACCT BUS.EGR 1 1 1 1 70 4.43 42 2 150 3 2 1 1 2 70 8.23 84 1 20 2 3 1 1 3 90 4.43 56 2 100 1 4 1 1 4 80 6.33 70 1 60 1 5 1 1 5 80 6.33 42 3 40 3 6 1 1 6 90 8.23 56 1 20 4 7 2 1 1 70 4.43 42 2 158 4 8 2 1 2 70 8.23 84 1 25 2 9 2 1 3 90 4.43 56 2 105 1 10 2 1 4 80 6.33 70 3 75 1 11 2 1 5 80 6.33 42 3 50 3 12 2 1 6 90 8.23 56 1 25 4 13 3 1 1 70 4.43 42 2 180 3 14 3 1 2 70 8.23 84 1 21 2 15 3 1 3 90 4.43 56 2 120 1 16 3 1 4 80 6.33 70 1 63 1 SP Table 17 3 1 5 80 6.33 42 3 42 3 18 3 1 6 90 8.23 56 1 21 4 19 4 1 1 70 4.43 42 2 105 2 20 4 1 2 70 8.23 84 1 22 2 21 4 1 3 90 4.43 56 2 70 1 22 4 1 4 80 6.33 70 1 66 1 23 4 1 5 80 6.33 42 3 44 3 24 4 1 6 90 8.23 56 1 22 4 25 5 1 1 70 4.43 42 2 180 3 26 5 1 2 70 8.23 84 1 21 2 27 5 1 3 90 4.43 56 2 120 1 28 5 1 4 80 6.33 70 2 180 1 29 5 1 5 80 6.33 42 3 41 3 30 5 1 6 90 8.23 56 1 21 4 Appendix Page 57 BUS.EGRT BUS.ONTIME BUS.DEPT BUS.TRANS BUS.SEAT BUS.INFO BUS.TT BUS.TC RAIL.ACC RAIL.ACCT 38 80 11 2 3 2 9.69 36 1 57 45 70 16 0 1 3 5.22 27 3 38 25 80 6 2 1 3 9.69 27 1 19 38 90 16 0 2 1 7.45 36 2 45 13 70 6 1 2 2 5.22 54 2 90 13 90 11 1 3 1 7.45 45 1 57 38 80 11 2 3 2 9.69 36 1 59 45 70 16 0 1 3 5.22 27 3 39 25 80 6 2 1 3 9.69 27 3 20 38 90 16 0 2 1 7.45 36 2 45 13 70 6 1 2 2 5.22 54 2 90 13 90 11 1 3 1 7.45 45 1 59 38 80 11 2 3 2 9.69 36 1 62 45 70 16 0 1 3 5.22 27 3 41 25 80 6 2 1 3 9.69 27 1 21 38 90 16 0 2 1 7.45 36 2 55 SP Table 13 70 6 1 2 2 5.22 54 2 110 13 90 11 1 3 1 7.45 45 1 62 68 80 11 2 3 2 9.69 36 1 66 45 70 16 0 1 3 5.22 27 3 44 25 80 6 2 1 3 9.69 27 2 28 38 90 16 0 2 1 7.45 36 2 28 13 70 6 1 2 2 5.22 54 2 55 13 90 11 1 3 1 7.45 45 1 66 38 80 11 2 3 2 9.69 36 1 59 45 70 16 0 1 3 5.22 27 3 39 25 80 6 2 1 3 9.69 27 2 55 38 90 16 0 2 1 7.45 36 2 55 13 70 6 1 2 2 5.22 54 2 110 13 90 11 1 3 1 7.45 45 1 59 Appendix Page 58 RAIL.EGR RAIL.EGRT RAIL.ONTIME RAIL.DEPT RAIL.TRANS RAIL.SEAT RAIL.INFO RAIL.TT RAIL.TC RAIL.PREM 3 12 90 8 0 3 3 5.83 94 118 1 23 80 8 2 3 1 4.08 71 142 4 23 70 4 0 2 2 7.58 71 89 4 12 80 12 2 1 2 5.83 94 165 1 35 90 12 1 1 3 7.58 141 212 2 68 70 4 1 2 1 4.08 118 177 3 12 90 8 0 3 3 5.83 94 118 1 23 80 8 2 3 1 4.08 71 142 4 23 70 4 0 2 2 7.58 71 89 1 12 80 12 2 1 2 5.83 94 165 1 35 90 12 1 1 3 7.58 141 212 2 68 70 4 1 2 1 4.08 118 177 3 12 90 8 0 3 3 5.83 94 118 1 23 80 8 2 3 1 4.08 71 142 4 23 70 4 0 2 2 7.58 71 89 3 12 80 12 2 1 2 5.83 94 165 SP Table 1 35 90 12 1 1 3 7.58 141 212 2 68 70 4 1 2 1 4.08 118 177 3 12 90 8 0 3 3 5.83 94 118 1 23 80 8 2 3 1 4.08 71 142 4 23 70 4 0 2 2 7.58 71 89 3 12 80 12 2 1 2 5.83 94 165 1 35 90 12 1 1 3 7.58 141 212 2 68 70 4 1 2 1 4.08 118 177 3 12 90 8 0 3 3 5.83 94 118 1 23 80 8 2 3 1 4.08 71 142 4 23 70 4 0 2 2 7.58 71 89 3 12 80 12 2 1 2 5.83 94 165 1 35 90 12 1 1 3 7.58 141 212 2 68 70 4 1 2 1 4.08 118 177 Appendix Page 59 AIR.ACC AIR.ACCT AIR.EGR AIR.EGRT AIR.ONTIME AIR.DEPT AIR.TRANS AIR.SEAT AIR.INFO AIR.TT 3 20 1 20 70 14 0 2 2 1.52 1 60 3 10 90 7 0 1 1 1.52 1 40 1 30 70 21 1 1 1 1.17 2 225 2 40 80 7 1 2 3 0.82 2 75 4 30 90 14 0 3 2 0.82 3 40 1 20 80 21 1 3 3 1.17 3 21 1 20 70 14 0 2 2 1.52 1 63 3 10 90 7 0 1 1 1.52 1 42 2 120 70 21 1 1 1 1.17 2 180 2 40 80 7 1 2 3 0.82 2 60 4 30 90 14 0 3 2 0.82 3 42 1 20 80 21 1 3 3 1.17 2 75 1 20 70 14 0 2 2 1.52 1 66 3 10 90 7 0 1 1 1.52 1 44 3 30 70 21 1 1 1 1.17 2 225 2 40 80 7 1 2 3 0.82 SP Table 2 75 4 30 90 14 0 3 2 0.82 3 44 1 20 80 21 1 3 3 1.17 2 40 1 20 70 14 0 2 2 1.52 1 69 3 10 90 7 0 1 1 1.52 1 46 4 30 70 21 1 1 1 1.17 2 120 2 40 80 7 1 2 3 0.82 2 40 4 30 90 14 0 3 2 0.82 3 46 1 20 80 21 1 3 3 1.17 2 30 1 20 70 14 0 2 2 1.52 1 27 3 10 90 7 0 1 1 1.52 1 18 4 30 70 21 1 1 1 1.17 2 90 2 40 80 7 1 2 3 0.82 2 30 4 30 90 14 0 3 2 0.82 3 18 1 20 80 21 1 3 3 1.17 Appendix Page 60 AIR.TC AIR.PREM HSR.ACC HSR.ACCT HSR.EGR HSR.EGRT HSR.ONTIME HSR.DEPT HSR.TRANS HSR.SEAT 338 592 2 45 1 23 90 10 1 1 169 254 3 38 3 35 90 20 1 2 225 282 1 38 1 12 80 30 0 3 282 423 1 19 2 68 70 10 0 3 225 450 2 135 3 12 70 30 1 2 169 212 3 57 4 23 80 20 0 1 338 592 2 45 1 23 90 10 1 1 169 254 3 39 3 35 90 20 1 2 225 282 1 39 1 12 80 30 0 3 282 423 1 20 2 68 70 10 0 3 225 450 2 135 3 12 70 30 1 2 169 212 2 135 4 23 80 20 0 1 338 592 2 55 1 23 90 10 1 1 169 254 3 41 1 35 90 20 1 2 225 282 1 41 1 12 80 30 0 3 282 423 1 21 2 68 70 10 0 3 SP Table 225 450 2 165 3 12 70 30 1 2 169 212 1 62 4 23 80 20 0 1 338 592 2 28 1 23 90 10 1 1 169 254 3 44 4 35 90 20 1 2 225 282 1 44 1 12 80 30 0 3 282 423 1 22 2 68 70 10 0 3 225 450 2 83 3 12 70 30 1 2 169 212 1 66 4 23 80 20 0 1 338 592 2 55 1 23 90 10 1 1 169 254 3 39 1 35 90 20 1 2 225 282 1 39 1 12 80 30 0 3 282 423 1 20 2 68 70 10 0 3 225 450 2 165 3 12 70 30 1 2 169 212 2 165 4 23 80 20 0 1 Appendix Page 61 HSR.INFO HSR.TT HSR.TC HSR.PREM 1 2.10 150 188 3 3.00 188 235 2 3.00 225 394 2 3.90 113 170 1 3.90 113 170 3 2.10 150 300 1 2.10 150 188 3 3.00 188 235 2 3.00 225 394 2 3.90 113 170 1 3.90 113 170 3 2.10 150 300 1 2.10 150 188 3 3.00 188 235 2 3.00 225 394 2 3.90 113 170 SP Table 1 3.90 113 170 3 2.10 150 300 1 2.10 150 188 3 3.00 188 235 2 3.00 225 394 2 3.90 113 170 1 3.90 113 170 3 2.10 150 300 1 2.10 150 188 3 3.00 188 235 2 3.00 225 394 2 3.90 113 170 1 3.90 113 170 3 2.10 150 300 Appendix Page 62 Origin 1 48 Destination 1 15 Scenario 1 5

Row 1 Reference TEXT

Auto Bus Rail Air HSR Auto Bus Rail Air HSR Access Mode 2 1 3 2 Access Mode Transit (Cost = $3-$6) Drop Off (Cost = $0) Taxi (Cost = $10-$30) Transit (Cost = $3-$6) Access Time 2.50 0.95 0.33 0.75 Access Time 2h 30m 0h 60m 0h 18m 0h 48m Egress Mode 3 3 1 1 Egress Mode Taxi (Cost = $10-$30) Taxi (Cost = $10-$30) Pick Up (Cost = $0) Pick Up (Cost = $0) Egress Time 0.63 0.19 0.33 0.38 Egress Time 0h 36m 0h 12m 0h 18m 0h 24m On Time 70 80 90 70 90 On Time 70% 80% 90% 70% 90% Frequency 11 8 14 10 Frequency 11 8 14 10 Transfers 2 0 0 1 Transfers 2 Transfers Direct Direct 1 Transfer Crowding 3 3 2 1 Crowding First Come First ServeFirst Come First Serve Assigned Seating Pre-booked Seating Trip Information 2 3 2 1 Trip Information Real-Time Schedule Pre-Posted Schedule Real-Time Schedule Mobile Schedule Travel Time 4.43 9.69 5.83 1.52 2.10 Travel Time 4h 24m 9h 42m 5h 48m 1h 30m 2h 6m Cost 42 36 94 338 150 Cost $42 $36 $94 $338 $150 Premium Cost 118 592 188 Premium Cost $118 $592 $188

Total Travel Time 4.43 12.81 6.98 2.18 3.23 Total Travel Time 4h 24m 12h 48m 6h 60m 2h 12m 3h 12m Total Travel Cost 42.00 61.00 114.00 358.00 155.00 Total Travel Cost $42 $61 $114 $358 $155 Lookup Appendix Page 63

Appendix I - Preliminary SP Procedures

Appendix Page 64 1. Access Mode output is based on a series of if-else statements that draws from the Ngene table and Variable sheet. The value listed on the Ngene table indicates a certain local access mode. The Excel formula used is =if(Ngene table variable = 1, Drop Off, if(Ngene table variable = 2, Transit, if(Ngene table variable = 3, Taxi, if(Ngene table variable = 4, Walk/Bike, else output text “error”)))). 2. Local Access Time output is obtained by multiplying the access time coefficient (50%, 100%, or 150%) with the baseline local access travel time correspondent with the above access mode. The Excel formula used is =(access time coefficient) * (if(Drop Off, Auto Time, if(Transit, Transit Time, if(Taxi, Auto Time, if(Walk/Bike, Walk Time))))). 3. Egress Mode output is based on a series of if-else statements that draws from the Ngene table and Variable sheet. The value listed on the Ngene table indicates a certain local egress mode. The Excel formula used is =if(Ngene table variable = 1, Pick Up, if(Ngene table variable = 2, Transit, if(Ngene table variable = 3, Taxi, if(Ngene table variable = 4, Rental, if(Ngene table variable = 5, Walk/Bike, else output text “error”)))). 4. Local Egress Time output is obtained by multiplying the egress time coefficient (50%, 100%, or 150%) with the baseline local egress travel time correspondent with the above egress mode. The Excel formula used is =(egress time coefficient) * (if(Pick Up, Auto Time, if(Transit, Transit Time, if(Taxi, Auto Time, if(Rental, Auto Time, if(Walk/Bike, Walk Time)))))). 5. On Time output is a simple multiplication of the Ngene table by a factor of 100 to output the probability of arriving on time as a percentage rather than a decimal value. The Excel formula used is =(Ngene table value * 100). 6. Frequency output is obtained by multiplying the frequency coefficient (50%, 100%, or 150%) with the baseline number of daily departures correspondent with each intercity travel mode alternative. If the resulting value is a decimal, then the number is rounded up to the nearest integer. The Excel formula used is =roundup(intercity departures per day * frequency coefficient, zero decimals). 7. Transfers output is based on a series of if-else statements that draws from the Ngene table and Variable sheet . The value listed on the Ngene table indicates the number of transfers required for the intercity trip. To add context, a text string is included. The Excel formula used is =if(Ngene table variable = 0, Direct, if(Ngene table variable = 1, 1 Transfer, if(Ngene table variable = 2, 2 Transfers, else output “error”))). 8. Crowding output is based on a series of if-else statements that draws from the Ngene table and Variable sheet. The value listed on the Ngene table indicates the type of seating choice available.

Appendix Page 65 The Excel formula used is =if(Ngene table variable = 1, Pre-booked Seating, if(Ngene table variable = 2, Assigned Seating, if(Ngene table variable = 3, First Come First Serve, else output text “error”))). 9. Trip Information output is based on a series of if-else statements that draws from the Ngene table and Variable sheet. The value listed on the Ngene table indicates the available trip information. The Excel formula used is =if(Ngene table variable = 1, Mobile Schedule, if(Ngene table variable = 2, Real-Time Schedule, if(Ngene table variable = 3, Pre-Posted Schedule, else output text “error”))). 10. Intercity Travel Time output is obtained by multiplying the intercity travel time coefficient (70%, 100%, or 130%) with the baseline intercity travel time of the respective intercity travel mode alternative. The resulting travel time is displayed in hours to help with data interpretation. The Excel formula used is =((intercity travel time coefficient * intercity travel time)/60). 11. Intercity Travel Cost output is obtained by multiplying the intercity travel time cost coefficient (75%, 100%, 125%, or 150%) with the baseline intercity travel cost of the respective intercity travel mode alternative. If the resulting value is a decimal, then the number is rounded up to the nearest dollar. The Excel formula used is =roundup(intercity travel cost coefficient * intercity travel cost, zero decimals). 12. Premium Travel Cost output is only applicable to the rail, airplane, and high speed rail intercity travel modes and is obtained by multiplying a cost multiplication factor (125%, 150%, 175%, or 200%) with the corresponding calculated intercity travel cost. The Excel formula used is =(premium cost multiplier * intercity travel cost).

Appendix Page 66

Appendix J - Index Match Function Procedure

Appendix Page 67  The match function has the form match(value, array, match type) and functions to dictate whether or not a certain row in the SP Table sheet contains the right values to match the designation inputs. In this case, the value is set to one (1). The match type set to zero (0), which indicates that the match function will find the first value equal to the set value of one (1). The array is the multiplication of each designation input (origin, destination, or SP scenario) with its corresponding column in the SP Table sheet. Starting at to top of the SP Table sheet, each origin, destination, and SP scenario is assigned a one (1) if it matches with the input or zero (0) if it does not match. The multiplication of the three values will either be one (1) if all three inputs match or zero (0) otherwise. Using this method, Excel scans through the entire SP Table sheet to find the first instance where all three inputs return a value of one (1). In the entire SP Table sheet, there should only be one instance where a match can occur and return the relative position of that value.  The index function has the form index(array, row_number) and functions to return the reference value from a range of values. In the index function, the match function is nested within the row_number entry. When the match function finds the correct row in the SP Table sheet, the resulting relative position is the row number of the index function. With the column of identification numbers in the SP Table sheet set as the array of cells in the index function, the final output from the index-match function is the identification number which matches the three designation criteria.

Appendix Page 68

Appendix K - Travel Time Calculation Procedure

Appendix Page 69 1. Truncate the time value to only display whole numbers without rounding by using the Excel formula =trunc(time value, 0 decimals). Attach the text character “h” behind to designate hours. 2. Isolate the decimal value of time by using the Excel formula =mod(time value, 1 divisor), which would then be the decimal value of one hour. 3. Round the decimal value of time into a multiple of 0.1 using the Excel formula =mround(decimal time value, 0.1 multiple) to obtain a decimal value that could be translated into multiples of 6 minutes without decimals. A smaller time scale may be used in future iterations by using a 0.05 multiple to obtain multiples of 3 minutes. 4. Multiply the minute decimal value by 60 to output a number in minutes. Attach the text character “m” behind to designate minutes.

Appendix Page 70

Appendix L - HTML Output Code Example

Appendix Page 71

Scenario 1 of 6
From your home location in Ajax to Ahuntsic-Cartierville (click for map)

Appendix Page 73

Appendix Page 74

Appendix Page 75

	Driving / Carpooling / Passenger	Bus	Rail	Airplane	High Speed Rail
Access Method		Transit (Cost = $3-$6)	Drop Off (Cost = $0)	Taxi (Cost = $10-$30)	Transit (Cost = $3-$6)
Access Time		2h 30m	0h 60m	0h 18m	0h 48m
Egress Method		Taxi (Cost = $10-$30)	Taxi (Cost = $10-$30)	Pick Up (Cost = $0)	Pick Up (Cost = $0)
Egress Time		0h 36m	0h 12m	0h 18m	0h 24m
Percentage On Time	70%	80%	90%	70%	90%
Departures Per Day		11	8	14	10
Number of Transfers		2 Transfers	Direct	Direct	1 Transfer
Seat Choice		First Come First Serve	First Come First Serve	Assigned Seating	Pre-booked Seating
Trip Information		Real-Time Schedule	Pre-Posted Schedule	Real-Time Schedule	Mobile Schedule
Main Travel Time	4h 24m	9h 42m	5h 48m	1h 30m	2h 6m
Travel Cost	$42	$36	$94	$338	$150
First Class/Business Class Cost			$118	$592	$188
Total Travel Time	4h 24m	12h 48m	6h 60m	2h 12m	3h 12m
Total Travel Cost	$42	$61	$114	$358	$155

Appendix Page 76

Appendix M - AppleScript Code

Appendix Page 77 tell application "TextMate" activate tell application "System Events" keystroke "h" using {option down, control down, shift down} end tell end tell tell application "Microsoft Excel" tell active workbook tell worksheet "HTML" of active workbook

set value of cell "B1" to "1" repeat until value of cell "B1" = 206

set value of cell "B2" to "19" repeat until value of cell "B2" = 20

set value of cell "B3" to "1" repeat until value of cell "B3" = 7

set myvar to value of cell "C3" copy range range "A5:B209" delay 1

tell application "TextMate" tell application "System Events" keystroke "a" using command down keystroke delete key keystroke "v" using command down

delay 0.5

keystroke "s" using {command down, shift down} keystroke myvar

delay 0.5

keystroke return

delay 0.5

end tell end tell

tell application "Microsoft Excel" tell active workbook tell worksheet "HTML" of active workbook set value of cell "B3" to (value of cell "B3") + 1

delay 4

Appendix Page 78 end tell end tell end tell

end repeat

tell application "Microsoft Excel" tell active workbook tell worksheet "HTML" of active workbook set value of cell "B2" to (value of cell "B2") + 1 end tell end tell end tell

end repeat

tell application "Microsoft Excel" tell active workbook tell worksheet "HTML" of active workbook set value of cell "B1" to (value of cell "B1") + 1

delay 5

end tell end tell end tell

end repeat

end tell end tell end tell

Appendix Page 79

Appendix N - Demographics by Collector

Appendix Page 80 Distribution of HHLD Origin 200

180

160

140

Devon - Research Vendors

120 Metrolinx Internal

Transport Listserv 100 EngSoc Digest

Frequency Skulebook 80 UTEK Skule Nite Intercept

60 Reddit /Toronto Reddit /UofT Facebook - mba 40 Facebook - Skule Facebook - Profile 20

0

Ajax

King

Milton

Aurora

Scugog

Oakville

Oshawa

Toronto

Caledon

Whiteby

Vaughan

Uxbridge

Pickering

Georgina

Markham Brampton

Appendix Page 81

Clarington

Burlington

HaltonHills

Newmarket

Mississauga

RichmondHill EastGwillimbury

Whitchurch-Stouffville Distribution of Age 200

180

160

140 Devon - Research Vendors Metrolinx Internal

120 Transport Listserv

EngSoc Digest

Skulebook 100 UTEK

Frequency Skule Nite Intercept Reddit /Toronto 80 Reddit /UofT Facebook - mba 60 Facebook - Skule Facebook - Profile

40

20 Appendix Page 82

0 Between 18 and Between 25 and Between 31 and Between 41 and Between 51 and Over 65 Undisclosed 24 30 40 50 65

Distribution of Gender 160

140

120

Devon - Research Vendors Metrolinx Internal 100 Transport Listserv EngSoc Digest

Skulebook

80 UTEK

Skule Nite Intercept Frequency Reddit /Toronto Reddit /UofT 60 Facebook - mba Facebook - Skule Facebook - Profile 40

20 Appendix Page 83

0 Male Female Other Undisclosed

Distribution of Martial Status 250

200

Devon - Research Vendors Metrolinx Internal Transport Listserv 150 EngSoc Digest

Skulebook UTEK

Skule Nite Intercept Frequency Reddit /Toronto 100 Reddit /UofT Facebook - mba Facebook - Skule Facebook - Profile

50 Appendix Page 84

0 Single Married Divorced Widowed Undisclosed

Distribution of HHLD Size 70

60

50 Devon - Research Vendors Metrolinx Internal Transport Listserv 40

EngSoc Digest Skulebook

UTEK Frequency 30 Skule Nite Intercept Reddit /Toronto Reddit /UofT Facebook - mba 20 Facebook - Skule Facebook - Profile

10

Appendix Page 85 0

Distribution of HHLD Auto Ownership 120

100

Devon - Research Vendors 80 Metrolinx Internal Transport Listserv EngSoc Digest

Skulebook

60 UTEK

Skule Nite Intercept Frequency Reddit /Toronto Reddit /UofT Facebook - mba 40 Facebook - Skule Facebook - Profile

20 Appendix Page 86

0 0 1 2 3 More than 4 Undisclosed

Distribution of HHLD Income 70

60

50

Devon - Research Vendors Metrolinx Internal

40 Transport Listserv

EngSoc Digest Skulebook

Frequency UTEK 30 Skule Nite Intercept Reddit /Toronto Reddit /UofT 20 Facebook - mba Facebook - Skule Facebook - Profile 10

0 Appendix Page 87

Appendix O - Geographic Spread of Respondents

Appendix Page 88 Num City Postal Code2011 Population2011 PrivatePanel Dwelling CountNon-Panel Count% POP % Panel % Non-Panel 1 Ajax L1S 40462 15174 0 0 0.67% 0.00% 0.00% 2 Ajax L1T 47818 13921 2 0 0.79% 1.16% 0.00% 3 Ajax L1Z 21010 6383 0 1 0.35% 0.00% 0.58% 4 Aurora L4G 53203 18092 1 1 0.88% 0.58% 0.58% 5 Brampton L6P 61932 15053 1 0 1.02% 0.58% 0.00% 6 Brampton L6R 79689 19525 0 0 1.31% 0.00% 0.00% 7 Brampton L6S 54498 17318 0 0 0.90% 0.00% 0.00% 8 Brampton L6T 37509 13186 1 0 0.62% 0.58% 0.00% 9 Brampton L6V 42209 14334 2 0 0.70% 1.16% 0.00% 10 Brampton L6W 22194 8254 2 0 0.37% 1.16% 0.00% 11 Brampton L6X 54217 17171 1 0 0.89% 0.58% 0.00% 12 Brampton L6Y 74362 22755 1 0 1.23% 0.58% 0.00% 13 Brampton L6Z 32228 9710 0 0 0.53% 0.00% 0.00% 14 Brampton L7A 65043 17351 2 0 1.07% 1.16% 0.00% 15 Burlington L7L 45039 16797 0 0 0.74% 0.00% 0.00% 16 Burlington L7M 44646 16060 0 0 0.74% 0.00% 0.00% 17 Burlington L7N 12920 5528 0 0 0.21% 0.00% 0.00% 18 Burlington L7P 28461 10523 0 0 0.47% 0.00% 0.00% 19 Burlington L7R 16547 7880 0 0 0.27% 0.00% 0.00% 20 Burlington L7S 11905 6029 0 0 0.20% 0.00% 0.00% 21 Burlington L7T 16215 6978 0 0 0.27% 0.00% 0.00% 22 Caledon L7C 16734 5770 1 0 0.28% 0.58% 0.00% 23 Caledon L7E 34570 10961 1 0 0.57% 0.58% 0.00% 24 Caledon L7K 8331 2968 1 0 0.14% 0.58% 0.00% 25 Clarington L0B 15028 5830 0 0 0.25% 0.00% 0.00% 26 Clarington L1B 10768 4285 0 0 0.18% 0.00% 0.00% 27 Clarington L1C 39347 14508 0 0 0.65% 0.00% 0.00% 28 Clarington L1E 24860 8500 0 0 0.41% 0.00% 0.00% 29 East GwillimburyL9N 10484 3635 3 0 0.17% 1.73% 0.00% 30 Georgina L0E 19503 9410 0 0 0.32% 0.00% 0.00% 31 Georgina L4P 27874 10335 1 0 0.46% 0.58% 0.00% 32 Halton HillsL0P 8089 2850 0 0 0.13% 0.00% 0.00% 33 Halton HillsL7G 44150 15166 0 0 0.73% 0.00% 0.00% 34 Halton HillsL7J 13692 5000 0 0 0.23% 0.00% 0.00% 35 King L0G 41086 14602 0 0 0.68% 0.00% 0.00% 36 King L7B 7967 2683 0 2 0.13% 0.00% 1.16% 37 Markham L3P 39205 12893 0 1 0.65% 0.00% 0.58% 38 Markham L3R 62393 18690 2 2 1.03% 1.16% 1.16% 39 Markham L3S 57644 15011 0 3 0.95% 0.00% 1.73% 40 Markham L3T 48039 18017 0 1 0.79% 0.00% 0.58% 41 Markham L6B 22938 6636 0 0 0.38% 0.00% 0.00% 42 Markham L6C 40572 11670 1 1 0.67% 0.58% 0.58% 43 Markham L6E 26917 7968 1 0 0.44% 0.58% 0.00% 44 Markham L6G 3944 2289 0 0 0.06% 0.00% 0.00% 45 Markham L0H 3411 1208 0 0 0.06% 0.00% 0.00% 46 Milton L9T 78910 26157 1 0 1.30% 0.58% 0.00% 47 MississaugaL4T 39097 12407 2 0 0.64% 1.16% 0.00% 48 MississaugaL4V 0 0 0 0 0.00% 0.00% 0.00% 49 MississaugaL4W 21479 7513 0 0 0.35% 0.00% 0.00% 50 MississaugaL4X 19747 7004 1 0 0.33% 0.58% 0.00% 51 MississaugaL4Y 23989 9143 1 0 0.40% 0.58% 0.00% 52 MississaugaL4Z 37331 13069 3 0 0.62% 1.73% 0.00% 53 MississaugaL5A 47249 18426 0 0 0.78% 0.00% 0.00% 54 MississaugaL5B 59396 24060 1 0 0.98% 0.58% 0.00% 55 MississaugaL5C 30589 10157 2 1 0.50% 1.16% 0.58% 56 MississaugaL5E 12689 5391 0 0 0.21% 0.00% 0.00% 57 MississaugaL5G 20174 9162 0 0 0.33% 0.00% 0.00% 58 MississaugaL5H 17996 6632 0 0 0.30% 0.00% 0.00% 59 MississaugaL5J 28518 10638 1 1 0.47% 0.58% 0.58% 60 MississaugaL5K 14271 4753 0 1 0.24% 0.00% 0.58%

Appendix Page 89 61 MississaugaL5L 45572 15334 2 2 0.75% 1.16% 1.16% 62 MississaugaL5M 98792 29498 4 1 1.63% 2.31% 0.58% 63 MississaugaL5N 85120 27265 3 0 1.40% 1.73% 0.00% 64 MississaugaL5P 5 1 0 0 0.00% 0.00% 0.00% 65 MississaugaL5R 37198 12209 1 0 0.61% 0.58% 0.00% 66 MississaugaL5S 5 2 0 0 0.00% 0.00% 0.00% 67 MississaugaL5T 17 14 0 0 0.00% 0.00% 0.00% 68 MississaugaL5V 50765 13521 0 3 0.84% 0.00% 1.73% 69 MississaugaL5W 23453 6341 1 0 0.39% 0.58% 0.00% 70 NewmarketL3X 37567 11689 0 0 0.62% 0.00% 0.00% 71 NewmarketL3Y 45294 17305 2 0 0.75% 1.16% 0.00% 72 Oakville L6H 59917 20299 1 2 0.99% 0.58% 1.16% 73 Oakville L6J 24871 8568 0 2 0.41% 0.00% 1.16% 74 Oakville L6K 12392 5732 3 1 0.20% 1.73% 0.58% 75 Oakville L6L 28659 11114 0 0 0.47% 0.00% 0.00% 76 Oakville L6M 56689 18184 0 0 0.93% 0.00% 0.00% 77 Oshawa L1G 40859 18149 0 0 0.67% 0.00% 0.00% 78 Oshawa L1H 31177 14073 0 0 0.51% 0.00% 0.00% 79 Oshawa L1J 42215 17316 0 0 0.70% 0.00% 0.00% 80 Oshawa L1K 32648 10813 1 0 0.54% 0.58% 0.00% 81 Oshawa L1L 2959 1033 0 0 0.05% 0.00% 0.00% 82 Pickering L1V 52312 17493 3 3 0.86% 1.73% 1.73% 83 Pickering L1W 16907 6394 0 1 0.28% 0.00% 0.58% 84 Pickering L1X 16502 4848 0 1 0.27% 0.00% 0.58% 85 Pickering L1Y 2009 745 0 0 0.03% 0.00% 0.00% 86 Richmond HillL4B 35432 10805 0 2 0.58% 0.00% 1.16% 87 Richmond HillL4C 71220 26164 2 2 1.17% 1.16% 1.16% 88 Richmond HillL4E 44440 13516 0 3 0.73% 0.00% 1.73% 89 Richmond HillL4S 34449 9684 1 0 0.57% 0.58% 0.00% 90 Scugog L0C 8972 3313 0 0 0.15% 0.00% 0.00% 91 Scugog L9L 14663 5700 0 0 0.24% 0.00% 0.00% 92 Toronto M1B 67251 20881 4 0 1.11% 2.31% 0.00% 93 Toronto M1C 35601 11410 1 0 0.59% 0.58% 0.00% 94 Toronto M1E 46398 17543 1 0 0.76% 0.58% 0.00% 95 Toronto M1G 30243 10548 0 0 0.50% 0.00% 0.00% 96 Toronto M1H 23706 8949 1 0 0.39% 0.58% 0.00% 97 Toronto M1J 36163 12970 2 0 0.60% 1.16% 0.00% 98 Toronto M1K 47286 18458 3 0 0.78% 1.73% 0.00% 99 Toronto M1L 32981 12423 1 0 0.54% 0.58% 0.00% 100 Toronto M1M 22919 8896 0 0 0.38% 0.00% 0.00% 101 Toronto M1N 21505 9446 1 0 0.35% 0.58% 0.00% 102 Toronto M1P 43305 16675 3 0 0.71% 1.73% 0.00% 103 Toronto M1R 28943 11203 0 0 0.48% 0.00% 0.00% 104 Toronto M1S 36505 12498 1 0 0.60% 0.58% 0.00% 105 Toronto M1T 34364 12841 1 0 0.57% 0.58% 0.00% 106 Toronto M1V 56313 16510 1 2 0.93% 0.58% 1.16% 107 Toronto M1W 49590 16378 3 3 0.82% 1.73% 1.73% 108 Toronto M1X 14744 3574 0 1 0.24% 0.00% 0.58% 109 Toronto M2H 25331 9061 5 1 0.42% 2.89% 0.58% 110 Toronto M2J 54104 20503 4 3 0.89% 2.31% 1.73% 111 Toronto M2K 19897 8797 0 1 0.33% 0.00% 0.58% 112 Toronto M2L 12025 4201 1 2 0.20% 0.58% 1.16% 113 Toronto M2M 32696 13015 3 1 0.54% 1.73% 0.58% 114 Toronto M2N 67114 32185 2 1 1.11% 1.16% 0.58% 115 Toronto M2P 7813 3166 0 0 0.13% 0.00% 0.00% 116 Toronto M2R 39583 15545 2 1 0.65% 1.16% 0.58% 117 Toronto M3A 34435 13540 0 1 0.57% 0.00% 0.58% 118 Toronto M3B 13499 5241 1 1 0.22% 0.58% 0.58% 119 Toronto M3C 38289 16050 3 0 0.63% 1.73% 0.00% 120 Toronto M3H 34535 13785 3 1 0.57% 1.73% 0.58% 121 Toronto M3J 25356 10810 0 0 0.42% 0.00% 0.00%

Appendix Page 90 122 Toronto M3K 5889 2307 0 0 0.10% 0.00% 0.00% 123 Toronto M3L 18000 6564 0 1 0.30% 0.00% 0.58% 124 Toronto M3M 23727 9620 2 1 0.39% 1.16% 0.58% 125 Toronto M3N 42762 14558 1 0 0.70% 0.58% 0.00% 126 Toronto M4A 14150 6223 0 0 0.23% 0.00% 0.00% 127 Toronto M4B 18453 7820 1 0 0.30% 0.58% 0.00% 128 Toronto M4C 45822 20005 3 4 0.75% 1.73% 2.31% 129 Toronto M4E 24598 11346 3 1 0.41% 1.73% 0.58% 130 Toronto M4G 18030 7616 0 2 0.30% 0.00% 1.16% 131 Toronto M4H 18478 6457 0 0 0.30% 0.00% 0.00% 132 Toronto M4J 35146 15232 1 0 0.58% 0.58% 0.00% 133 Toronto M4K 31624 15058 2 2 0.52% 1.16% 1.16% 134 Toronto M4L 31544 14420 0 0 0.52% 0.00% 0.00% 135 Toronto M4M 23135 9969 1 0 0.38% 0.58% 0.00% 136 Toronto M4N 15194 6377 0 2 0.25% 0.00% 1.16% 137 Toronto M4P 19185 12028 1 0 0.32% 0.58% 0.00% 138 Toronto M4R 11048 5230 0 1 0.18% 0.00% 0.58% 139 Toronto M4S 25627 14039 1 1 0.42% 0.58% 0.58% 140 Toronto M4T 10094 5293 2 1 0.17% 1.16% 0.58% 141 Toronto M4V 17271 10096 0 0 0.28% 0.00% 0.00% 142 Toronto M4W 14022 7645 0 1 0.23% 0.00% 0.58% 143 Toronto M4X 20387 10689 0 4 0.34% 0.00% 2.31% 144 Toronto M4Y 26207 18779 2 5 0.43% 1.16% 2.89% 145 Toronto M5A 34649 19382 5 3 0.57% 2.89% 1.73% 146 Toronto M5B 11352 7425 0 0 0.19% 0.00% 0.00% 147 Toronto M5C 2974 1866 0 0 0.05% 0.00% 0.00% 148 Toronto M5E 6436 4280 0 1 0.11% 0.00% 0.58% 149 Toronto M5G 7001 4788 1 9 0.12% 0.58% 5.20% 150 Toronto M5H 1027 1103 1 0 0.02% 0.58% 0.00% 151 Toronto M5J 10454 7137 0 1 0.17% 0.00% 0.58% 152 Toronto M5K 5 2 0 0 0.00% 0.00% 0.00% 153 Toronto M5L 5 1 1 0 0.00% 0.58% 0.00% 154 Toronto M5M 25852 9941 1 0 0.43% 0.58% 0.00% 155 Toronto M5N 16349 6811 0 0 0.27% 0.00% 0.00% 156 Toronto M5P 18343 9181 1 0 0.30% 0.58% 0.00% 157 Toronto M5R 25056 14907 3 2 0.41% 1.73% 1.16% 158 Toronto M5S 13690 7622 1 12 0.23% 0.58% 6.94% 159 Toronto M5T 18705 9335 1 29 0.31% 0.58% 16.76% 160 Toronto M5V 30669 22424 3 2 0.51% 1.73% 1.16% 161 Toronto M5W 5 1 0 0 0.00% 0.00% 0.00% 162 Toronto M5X 5 2 1 0 0.00% 0.58% 0.00% 163 Toronto M6A 19754 7881 1 0 0.33% 0.58% 0.00% 164 Toronto M6B 29236 11530 1 2 0.48% 0.58% 1.16% 165 Toronto M6C 24256 10870 0 2 0.40% 0.00% 1.16% 166 Toronto M6E 37920 15295 0 1 0.62% 0.00% 0.58% 167 Toronto M6G 32075 14311 1 5 0.53% 0.58% 2.89% 168 Toronto M6H 42856 19383 1 3 0.71% 0.58% 1.73% 169 Toronto M6J 28949 13535 5 1 0.48% 2.89% 0.58% 170 Toronto M6K 35320 19536 0 1 0.58% 0.00% 0.58% 171 Toronto M6L 20807 7639 0 0 0.34% 0.00% 0.00% 172 Toronto M6M 41954 16336 3 0 0.69% 1.73% 0.00% 173 Toronto M6N 41312 16738 0 0 0.68% 0.00% 0.00% 174 Toronto M6P 37959 19322 1 2 0.63% 0.58% 1.16% 175 Toronto M6R 19439 9149 0 0 0.32% 0.00% 0.00% 176 Toronto M6S 31548 14240 1 1 0.52% 0.58% 0.58% 177 Toronto M7A 0 1 0 0 0.00% 0.00% 0.00% 178 Toronto M7Y 0 1 0 0 0.00% 0.00% 0.00% 179 Toronto M8V 31921 17173 1 0 0.53% 0.58% 0.00% 180 Toronto M8W 20046 9036 1 0 0.33% 0.58% 0.00% 181 Toronto M8X 10481 4577 0 0 0.17% 0.00% 0.00% 182 Toronto M8Y 19805 9413 0 0 0.33% 0.00% 0.00%

Appendix Page 91 183 Toronto M8Z 15302 6433 0 0 0.25% 0.00% 0.00% 184 Toronto M9A 33520 14360 0 3 0.55% 0.00% 1.73% 185 Toronto M9B 30182 11584 0 3 0.50% 0.00% 1.73% 186 Toronto M9C 36672 14462 1 0 0.60% 0.58% 0.00% 187 Toronto M9L 11998 4066 1 0 0.20% 0.58% 0.00% 188 Toronto M9M 20681 7149 0 0 0.34% 0.00% 0.00% 189 Toronto M9N 24946 10826 0 0 0.41% 0.00% 0.00% 190 Toronto M9P 20970 8097 1 2 0.35% 0.58% 1.16% 191 Toronto M9R 32581 12343 2 0 0.54% 1.16% 0.00% 192 Toronto M9V 55949 17584 2 0 0.92% 1.16% 0.00% 193 Toronto M9W 41164 14302 0 0 0.68% 0.00% 0.00% 194 Uxbridge L9P 16649 6208 0 0 0.27% 0.00% 0.00% 195 Vaughan L4H 58066 16343 0 1 0.96% 0.00% 0.58% 196 Vaughan L4J 76944 25135 1 1 1.27% 0.58% 0.58% 197 Vaughan L4K 15822 4871 0 0 0.26% 0.00% 0.00% 198 Vaughan L4L 55424 17745 1 2 0.91% 0.58% 1.16% 199 Vaughan L6A 77186 22761 2 1 1.27% 1.16% 0.58% 200 Vaughan L0J 4902 1440 0 0 0.08% 0.00% 0.00% 201 Whitby L1M 19534 6153 0 0 0.32% 0.00% 0.00% 202 Whitby L1N 46572 18044 0 0 0.77% 0.00% 0.00% 203 Whitby L1P 16456 5258 0 0 0.27% 0.00% 0.00% 204 Whitby L1R 38444 12120 0 0 0.63% 0.00% 0.00% 205 Whitchurch-StouffvilleL4A 33943 12352 0 0 0.56% 0.00% 0.00%

Appendix Page 92 City 2011 POP SM Panel Non-Panel Ajax 1.80% 1.16% 0.58% Aurora 0.88% 0.58% 0.58% Brampton 8.63% 5.78% 0.00% Burlington 2.90% 0.00% 0.00% Caledon 0.98% 1.73% 0.00% Clarington 1.48% 0.00% 0.00% East Gwillimbury 0.17% 1.73% 0.00% Georgina 0.78% 0.58% 0.00% Halton Hills 1.09% 0.00% 0.00% King 0.81% 0.00% 1.16% Markham 5.03% 2.31% 4.62% Milton 1.30% 0.58% 0.00% Mississauga 11.76% 12.72% 5.20% Newmarket 1.37% 1.16% 0.00% Oakville 3.01% 2.31% 2.89% Oshawa 2.47% 0.58% 0.00% Pickering 1.45% 1.73% 2.89% Richmond Hill 3.06% 1.73% 4.05% Scugog 0.39% 0.00% 0.00% Toronto 43.09% 63.01% 75.14% Uxbridge 0.27% 0.00% 0.00% Vaughan 4.75% 2.31% 2.89% Whitby 1.99% 0.00% 0.00% Whitchurch-Stouffville 0.56% 0.00% 0.00%

Appendix Page 93

Appendix P - Stata Input Code

Appendix Page 94 Panel - Stata Inputs 4 ASCLOGIT asclogit choice sp_tt sp_tc sp_acct sp_egrt dsp_acc2 dsp_egr2 dsp_egr3, case(id) alternatives(alt) casevars(dincome3 dincome8 dincome9 dincome10 dage1 dage2 dage3) basealternative(1)

Single NLOGIT nlogitgen speed = alt(none: 1, slow: 2|3, fast: 4|5) nlogittree alt_name speed, choice(choice) constraint 1 [none_tau]_cons=1 nlogit choice sp_tt sp_tc || speed: sp_acct sp_egrt dsp_acc2 dsp_egr2 dsp_egr3, base(none) || alt: dincome3 dincome8 dincome9 dincome10 dage1 dage2 dage3, base(1) case(id) noconstant constraint(1) predict double p*, pr predict condp1, condp hlevel(2) predict condp2, condp hlevel(1) predict iv, iv

Dual NLOGIT ALT nlogitgen accstation = alt(none: 1, bay: 2, union: 3|5, pearson: 4) nlogitgen accessibility = accstation(noaccess: none, access: bay|union|pearson) nlogittree alt_name accstation accessibility, choice(choice) constraint 1 [none_tau]_cons=1 constraint 2 [bay_tau]_cons=1 constraint 3 [pearson_tau]_cons=1 constraint 4 [noaccess_tau]_cons=1 nlogit choice sp_tt sp_tc || accessibility: sp_acct sp_egrt, base(noaccess) || accstation: dsp_acc1 dsp_acc3 dsp_egr2 dsp_egr3, base(none) || alt: dincome3 dincome8 dincome9 dincome10 dage1 dage2 dage3, base(1) case(id) noconstant constraint(1 2 3 4) predict double p*, pr predict condp1, condp hlevel(3) predict condp2, condp hlevel(2) predict condp3, condp hlevel(1) predict iv, iv

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Appendix Page 95

Appendix Q - Stata Outputs

Appendix Page 96 Panel - Asclogit - 3.0 results asclogit choice sp_tt sp_tc sp_acct sp_egrt dsp_acc2 dsp_egr2 dsp_egr3, case(id) alternatives(alt) casevars(dincome3 dincome8 dinco > me9 dincome10 dage1 dage2 dage3) basealternative(1) note: variable dsp_acc2 has 314 cases that are not alternative-specific: there is no within-case variability note: variable dsp_egr2 has 468 cases that are not alternative-specific: there is no within-case variability note: variable dsp_egr3 has 468 cases that are not alternative-specific: there is no within-case variability

Iteration 0: log likelihood = -1555.3602 Iteration 1: log likelihood = -1530.5388 Iteration 2: log likelihood = -1530.3259 Iteration 3: log likelihood = -1530.3259

Alternative-specific conditional logit Number of obs = 5370 Case variable: id Number of cases = 1074 Alternative variable: alt Alts per case: min = 5 avg = 5.0 max = 5 Wald chi2(35) = 208.70 Log likelihood = -1530.3259 Prob > chi2 = 0.0000 ------choice | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------alt | sp_tt | -.0798076 .0269314 -2.96 0.003 -.1325923 -.027023 sp_tc | -.0819872 .0113025 -7.25 0.000 -.1041396 -.0598347 sp_acct | -.1599542 .1081567 -1.48 0.139 -.3719374 .052029 sp_egrt | -.4964091 .1690159 -2.94 0.003 -.8276743 -.165144 dsp_acc2 | -.2274307 .1218825 -1.87 0.062 -.466316 .0114547 dsp_egr2 | .4017583 .1343315 2.99 0.003 .1384734 .6650432 dsp_egr3 | -.2196033 .1263599 -1.74 0.082 -.4672643 .0280576 ------+------alt1 | (base alternative) ------+------alt2 | dincome3 | -.8131615 .4849472 -1.68 0.094 -1.763641 .1373175 dincome8 | -.9232794 .3517439 -2.62 0.009 -1.612685 -.2338739 dincome9 | -.7528179 .3671958 -2.05 0.040 -1.472508 -.0331272 dincome10 | -.8020542 .3157924 -2.54 0.011 -1.420996 -.1831125 dage1 | 1.186606 .2820262 4.21 0.000 .633845 1.739367 dage2 | 1.576177 .3162503 4.98 0.000 .9563381 2.196017 dage3 | 1.186808 .2972065 3.99 0.000 .6042942 1.769322 _cons | -1.237336 .2475039 -5.00 0.000 -1.722435 -.7522374 ------+------alt3 | dincome3 | -.4819622 .3906287 -1.23 0.217 -1.24758 .283656 dincome8 | -.4476162 .3063508 -1.46 0.144 -1.048053 .1528204 dincome9 | -.3792978 .3259168 -1.16 0.245 -1.018083 .2594874 dincome10 | -.6131039 .2764336 -2.22 0.027 -1.154904 -.0713039 dage1 | -.1176546 .276053 -0.43 0.670 -.6587085 .4233993 dage2 | .5118237 .2953083 1.73 0.083 -.06697 1.090617 dage3 | .8058214 .2430507 3.32 0.001 .3294509 1.282192 _cons | -.256768 .2108161 -1.22 0.223 -.6699601 .1564241 ------+------alt4 | dincome3 | -.2627177 .411854 -0.64 0.524 -1.069937 .5445013 dincome8 | -.8747996 .3763595 -2.32 0.020 -1.612451 -.1371485 dincome9 | .163407 .3106851 0.53 0.599 -.4455246 .7723386 Page 1

Appendix Page 97 Panel - Asclogit - 3.0 results dincome10 | .2304774 .2557296 0.90 0.367 -.2707433 .7316981 dage1 | -.3950087 .3195481 -1.24 0.216 -1.021311 .231294 dage2 | 1.016004 .2897492 3.51 0.000 .4481055 1.583901 dage3 | .8218678 .2536818 3.24 0.001 .3246607 1.319075 _cons | .1770485 .3265146 0.54 0.588 -.4629084 .8170054 ------+------alt5 | dincome3 | -.7597411 .4321574 -1.76 0.079 -1.606754 .0872718 dincome8 | -.482767 .301871 -1.60 0.110 -1.074423 .1088893 dincome9 | .009981 .2905979 0.03 0.973 -.5595805 .5795425 dincome10 | .2988062 .2291343 1.30 0.192 -.1502887 .747901 dage1 | .0834472 .2622673 0.32 0.750 -.4305872 .5974817 dage2 | 1.117888 .2641256 4.23 0.000 .6002112 1.635565 dage3 | .9243627 .2336476 3.96 0.000 .4664219 1.382304 _cons | -.1447261 .2529503 -0.57 0.567 -.6404997 .3510474 ------

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Appendix Page 98 Panel - Nlogit - Single 3.0 results nlogit choice sp_tt sp_tc || speed: sp_acct sp_egrt dsp_acc2 dsp_egr2 dsp_egr3, base(none) || alt: dincome3 dincome8 dincome9 dincom > e10 dage1 dage2 dage3, base(1) case(id) noconstant constraint(1) note: none:sp_acct dropped because of collinearity note: none:sp_egrt dropped because of collinearity note: none:dsp_a~2 dropped because of collinearity note: none:dsp_e~2 dropped because of collinearity note: none:dsp_e~3 dropped because of collinearity note: branch 1 of level 1 is degenerate and the associated dissimilarity parameter ([none_tau]_cons) is not defined; see help nlogit for details tree structure specified for the nested logit model

speed N alt N k ------none 1074 --- 1 1074 388 slow 2148 --- 2 1074 154 +- 3 1074 167 fast 2148 --- 4 1074 154 +- 5 1074 211 ------total 5370 1074 k = number of times alternative is chosen N = number of observations at each level Iteration 0: log likelihood = -1557.5334 Iteration 1: log likelihood = -1557.1647 (backed up) Iteration 2: log likelihood = -1556.3367 (backed up) Iteration 3: log likelihood = -1551.0417 (backed up) Iteration 4: log likelihood = -1549.2007 (backed up) Iteration 5: log likelihood = -1547.5435 (backed up) Iteration 6: log likelihood = -1546.9218 Iteration 7: log likelihood = -1545.3515 Iteration 8: log likelihood = -1544.0001 Iteration 9: log likelihood = -1543.4739 Iteration 10: log likelihood = -1541.6371 Iteration 11: log likelihood = -1540.7363 Iteration 12: log likelihood = -1540.1845 Iteration 13: log likelihood = -1539.3801 Iteration 14: log likelihood = -1538.8353 Iteration 15: log likelihood = -1538.5864 Iteration 16: log likelihood = -1538.4129 Iteration 17: log likelihood = -1538.2078 Iteration 18: log likelihood = -1538.0914 Iteration 19: log likelihood = -1537.8672 Iteration 20: log likelihood = -1537.6542 Iteration 21: log likelihood = -1537.5809 Iteration 22: log likelihood = -1537.5608 Iteration 23: log likelihood = -1537.5245 Iteration 24: log likelihood = -1537.4966 Iteration 25: log likelihood = -1537.4586 Iteration 26: log likelihood = -1537.4569 Iteration 27: log likelihood = -1537.4423 Iteration 28: log likelihood = -1537.4255 Iteration 29: log likelihood = -1537.4131 Iteration 30: log likelihood = -1537.3996 Iteration 31: log likelihood = -1537.3929 Iteration 32: log likelihood = -1537.3879 Iteration 33: log likelihood = -1537.3872 Iteration 34: log likelihood = -1537.3856 Iteration 35: log likelihood = -1537.384 Page 1

Appendix Page 99 Panel - Nlogit - Single 3.0 results Iteration 36: log likelihood = -1537.3833 Iteration 37: log likelihood = -1537.3828 Iteration 38: log likelihood = -1537.3819 Iteration 39: log likelihood = -1537.3813 Iteration 40: log likelihood = -1537.3811 Iteration 41: log likelihood = -1537.3809 Iteration 42: log likelihood = -1537.3809 Iteration 43: log likelihood = -1537.3808 Iteration 44: log likelihood = -1537.3807 Iteration 45: log likelihood = -1537.3806 Iteration 46: log likelihood = -1537.3806 Iteration 47: log likelihood = -1537.3806 Iteration 48: log likelihood = -1537.3806 Iteration 49: log likelihood = -1537.3806 Iteration 50: log likelihood = -1537.3806

RUM-consistent nested logit regression Number of obs = 5370 Case variable: id Number of cases = 1074 Alternative variable: alt Alts per case: min = 5 avg = 5.0 max = 5 Wald chi2(40) = 131.67 Log likelihood = -1537.3806 Prob > chi2 = 0.0000 ( 1) [none_tau]_cons = 1 ------choice | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------alt | sp_tt | -.1080224 .0240758 -4.49 0.000 -.1552101 -.0608347 sp_tc | -.0441207 .010738 -4.11 0.000 -.0651668 -.0230746 ------speed equations ------none | sp_acct | (base) sp_egrt | (base) dsp_acc2 | (base) dsp_egr2 | (base) dsp_egr3 | (base) ------+------slow | sp_acct | -.123919 .1396082 -0.89 0.375 -.397546 .1497081 sp_egrt | -.3189908 .229872 -1.39 0.165 -.7695317 .1315502 dsp_acc2 | -.3897829 .1699118 -2.29 0.022 -.7228039 -.0567619 dsp_egr2 | .056709 .1456417 0.39 0.697 -.2287436 .3421615 dsp_egr3 | -.4422887 .1453532 -3.04 0.002 -.7271758 -.1574016 ------+------fast | sp_acct | -.2848791 .1338489 -2.13 0.033 -.5472181 -.0225401 sp_egrt | -.680418 .1745861 -3.90 0.000 -1.0226 -.3382355 dsp_acc2 | -.0172395 .1289079 -0.13 0.894 -.2698943 .2354152 dsp_egr2 | .6447963 .2057033 3.13 0.002 .2416251 1.047967 dsp_egr3 | -.1214835 .115755 -1.05 0.294 -.348359 .1053921 ------alt equations ------alt1 | dincome3 | (base) dincome8 | (base) dincome9 | (base) Page 2

Appendix Page 100 Panel - Nlogit - Single 3.0 results dincome10 | (base) dage1 | (base) dage2 | (base) dage3 | (base) ------+------alt2 | dincome3 | -1.149973 .4109123 -2.80 0.005 -1.955347 -.3446001 dincome8 | -1.121941 .3106663 -3.61 0.000 -1.730836 -.5130466 dincome9 | -.956646 .3217543 -2.97 0.003 -1.587273 -.3260191 dincome10 | -1.222058 .2666676 -4.58 0.000 -1.744717 -.6993996 dage1 | .3194136 .1910369 1.67 0.095 -.0550119 .693839 dage2 | .7762827 .2376787 3.27 0.001 .3104409 1.242124 dage3 | .5531629 .224381 2.47 0.014 .1133842 .9929415 ------+------alt3 | dincome3 | -.6665506 .3587152 -1.86 0.063 -1.369619 .0365182 dincome8 | -.6113712 .2806001 -2.18 0.029 -1.161337 -.0614052 dincome9 | -.5558927 .2979153 -1.87 0.062 -1.139796 .0280106 dincome10 | -.7888441 .2321021 -3.40 0.001 -1.243756 -.3339322 dage1 | -.1281741 .2309384 -0.56 0.579 -.5808051 .3244569 dage2 | .5015124 .2560519 1.96 0.050 -.0003401 1.003365 dage3 | .7019226 .2050496 3.42 0.001 .3000328 1.103812 ------+------alt4 | dincome3 | -.5068282 .360703 -1.41 0.160 -1.213793 .2001368 dincome8 | -.8997161 .3001013 -3.00 0.003 -1.487904 -.3115284 dincome9 | .0113768 .2676887 0.04 0.966 -.5132835 .5360371 dincome10 | .0726062 .2055477 0.35 0.724 -.33026 .4754723 dage1 | -.4348763 .2439352 -1.78 0.075 -.9129805 .0432279 dage2 | .8689121 .2419349 3.59 0.000 .3947284 1.343096 dage3 | .7030129 .2104063 3.34 0.001 .2906242 1.115402 ------+------alt5 | dincome3 | -.8524115 .3594088 -2.37 0.018 -1.55684 -.1479832 dincome8 | -.6980812 .269318 -2.59 0.010 -1.225935 -.1702275 dincome9 | -.1466803 .254591 -0.58 0.565 -.6456696 .3523089 dincome10 | .0278869 .1822163 0.15 0.878 -.3292505 .3850243 dage1 | -.2697498 .2128499 -1.27 0.205 -.686928 .1474284 dage2 | .8322535 .2189387 3.80 0.000 .4031414 1.261365 dage3 | .6823814 .1916118 3.56 0.000 .3068291 1.057934 ------dissimilarity parameters ------speed | /none_tau | 1 . . . /slow_tau | .7007467 .177365 .3531178 1.048376 /fast_tau | .5492376 .1694181 .2171843 .8812909 ------LR test for IIA (tau = 1): chi2(2) = 4.51 Prob > chi2 = 0.1048 ------

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Appendix Page 101 Panel - Nlogit - Double 3.0 results nlogit choice sp_tt sp_tc || accessibility: sp_acct sp_egrt, base(noaccess) || accstation: dsp_acc1 dsp_acc3 dsp_egr2 dsp_egr3, bas > e(none) || alt: dincome3 dincome8 dincome9 dincome10 dage1 dage2 dage3, base(1) case(id) noconstant constraint(1 2 3 4) note: noaccess:s~t dropped because of collinearity note: noaccess:s~t dropped because of collinearity note: none:dsp_a~1 dropped because of collinearity note: none:dsp_a~3 dropped because of collinearity note: none:dsp_e~2 dropped because of collinearity note: none:dsp_e~3 dropped because of collinearity note: branch 1 of level 1 is degenerate and the associated dissimilarity parameter ([noaccess_tau]_cons) is not defined; see help nlogit for details note: branch 1 of level 2 is degenerate and the associated dissimilarity parameter ([none_tau]_cons) is not defined; see help nlogit for details note: branch 2 of level 2 is degenerate and the associated dissimilarity parameter ([bay_tau]_cons) is not defined; see help nlogit for details note: branch 4 of level 2 is degenerate and the associated dissimilarity parameter ([pearson_tau]_cons) is not defined; see help nlogit for details tree structure specified for the nested logit model accessibil~y N accstation N alt N k ------noaccess 1074 --- none 1074 --- 1 1074 388 access 4296 --- bay 1074 --- 2 1074 154 |- union 2148 --- 3 1074 167 | +- 5 1074 211 +- pearson 1074 --- 4 1074 154 ------total 5370 1074 k = number of times alternative is chosen N = number of observations at each level Iteration 0: log likelihood = -1666.1534 Iteration 1: log likelihood = -1662.765 (backed up) Iteration 2: log likelihood = -1635.0659 (backed up) Iteration 3: log likelihood = -1566.2955 (backed up) Iteration 4: log likelihood = -1563.3922 (backed up) Iteration 5: log likelihood = -1558.1646 Iteration 6: log likelihood = -1556.2437 (backed up) Iteration 7: log likelihood = -1547.1579 Iteration 8: log likelihood = -1544.3829 Iteration 9: log likelihood = -1542.6544 Iteration 10: log likelihood = -1541.4376 Iteration 11: log likelihood = -1539.5963 Iteration 12: log likelihood = -1538.3097 Iteration 13: log likelihood = -1537.1886 Iteration 14: log likelihood = -1535.746 Iteration 15: log likelihood = -1534.3632 Iteration 16: log likelihood = -1534.1271 Iteration 17: log likelihood = -1533.3007 Iteration 18: log likelihood = -1532.3176 Iteration 19: log likelihood = -1531.6466 Iteration 20: log likelihood = -1531.0098 Iteration 21: log likelihood = -1530.7832 Iteration 22: log likelihood = -1530.159 Iteration 23: log likelihood = -1529.6126 Iteration 24: log likelihood = -1529.3319 Page 1

Appendix Page 102 Panel - Nlogit - Double 3.0 results Iteration 25: log likelihood = -1528.6919 Iteration 26: log likelihood = -1527.741 Iteration 27: log likelihood = -1526.9878 Iteration 28: log likelihood = -1526.8383 Iteration 29: log likelihood = -1526.3777 Iteration 30: log likelihood = -1526.1784 Iteration 31: log likelihood = -1525.7709 Iteration 32: log likelihood = -1525.6308 Iteration 33: log likelihood = -1525.5562 Iteration 34: log likelihood = -1525.2351 Iteration 35: log likelihood = -1524.5607 Iteration 36: log likelihood = -1524.4111 Iteration 37: log likelihood = -1524.1712 Iteration 38: log likelihood = -1523.9521 Iteration 39: log likelihood = -1523.6938 Iteration 40: log likelihood = -1523.614 Iteration 41: log likelihood = -1523.4745 Iteration 42: log likelihood = -1523.4471 Iteration 43: log likelihood = -1523.4063 Iteration 44: log likelihood = -1523.3862 Iteration 45: log likelihood = -1523.2854 Iteration 46: log likelihood = -1523.256 Iteration 47: log likelihood = -1523.2247 Iteration 48: log likelihood = -1523.1947 Iteration 49: log likelihood = -1523.1819 Iteration 50: log likelihood = -1523.1676 Iteration 51: log likelihood = -1523.1609 Iteration 52: log likelihood = -1523.1493 Iteration 53: log likelihood = -1523.149 Iteration 54: log likelihood = -1523.1419 Iteration 55: log likelihood = -1523.14 Iteration 56: log likelihood = -1523.1388 Iteration 57: log likelihood = -1523.1383 Iteration 58: log likelihood = -1523.1382 Iteration 59: log likelihood = -1523.1381 Iteration 60: log likelihood = -1523.1373 Iteration 61: log likelihood = -1523.1369 Iteration 62: log likelihood = -1523.1368 Iteration 63: log likelihood = -1523.1367 Iteration 64: log likelihood = -1523.1367 Iteration 65: log likelihood = -1523.1367 Iteration 66: log likelihood = -1523.1367 Iteration 67: log likelihood = -1523.1367 RUM-consistent nested logit regression Number of obs = 5370 Case variable: id Number of cases = 1074 Alternative variable: alt Alts per case: min = 5 avg = 5.0 max = 5 Wald chi2(44) = 116.87 Log likelihood = -1523.1367 Prob > chi2 = 0.0000 ( 1) [none_tau]_cons = 1 ( 2) [bay_tau]_cons = 1 ( 3) [pearson_tau]_cons = 1 ( 4) [noaccess_tau]_cons = 1 ------choice | Coef. Std. Err. z P>|z| [95% Conf. Interval] ------+------alt | sp_tt | -.1035978 .0218039 -4.75 0.000 -.1463327 -.0608629 Page 2

Appendix Page 103 Panel - Nlogit - Double 3.0 results sp_tc | -.0457078 .0101102 -4.52 0.000 -.0655234 -.0258923 ------accessibility equations ------noaccess | sp_acct | (base) sp_egrt | (base) ------+------access | sp_acct | -.139223 .0924691 -1.51 0.132 -.3204591 .0420132 sp_egrt | -.2387121 .1075306 -2.22 0.026 -.4494681 -.027956 ------accstation equations ------none | dsp_acc1 | (base) dsp_acc3 | (base) dsp_egr2 | (base) dsp_egr3 | (base) ------+------bay | dsp_acc1 | -.0224156 .147605 -0.15 0.879 -.3117161 .266885 dsp_acc3 | .1001762 .1811989 0.55 0.580 -.2549671 .4553195 dsp_egr2 | -.0025108 .1513459 -0.02 0.987 -.2991432 .2941217 dsp_egr3 | -.7555389 .2508557 -3.01 0.003 -1.247207 -.2638708 ------+------union | dsp_acc1 | .3132886 .0896976 3.49 0.000 .1374846 .4890926 dsp_acc3 | .1700937 .1139912 1.49 0.136 -.0533249 .3935124 dsp_egr2 | .0694204 .110622 0.63 0.530 -.1473947 .2862354 dsp_egr3 | -.1739716 .0902317 -1.93 0.054 -.3508225 .0028792 ------+------pearson | dsp_acc1 | .007225 .1599976 0.05 0.964 -.3063645 .3208146 dsp_acc3 | .1989413 .1257069 1.58 0.114 -.0474398 .4453224 dsp_egr2 | .4561276 .1836361 2.48 0.013 .0962074 .8160477 dsp_egr3 | .1589157 .1782265 0.89 0.373 -.1904019 .5082333 ------alt equations ------alt1 | dincome3 | (base) dincome8 | (base) dincome9 | (base) dincome10 | (base) dage1 | (base) dage2 | (base) dage3 | (base) ------+------alt2 | dincome3 | -.7605554 .3276709 -2.32 0.020 -1.402779 -.1183322 dincome8 | -.8514713 .2549158 -3.34 0.001 -1.351097 -.3518455 dincome9 | -.521885 .2682551 -1.95 0.052 -1.047655 .0038853 dincome10 | -.5220221 .2326132 -2.24 0.025 -.9779356 -.0661087 dage1 | .4904458 .1784431 2.75 0.006 .1407037 .8401879 dage2 | 1.131891 .207704 5.45 0.000 .7247991 1.538984 dage3 | .9067045 .1864657 4.86 0.000 .5412384 1.272171 ------+------alt3 | dincome3 | -.5824036 .295291 -1.97 0.049 -1.161163 -.0036439 dincome8 | -.5878501 .2335646 -2.52 0.012 -1.045628 -.1300719 dincome9 | -.3124439 .2435983 -1.28 0.200 -.7898879 .165 dincome10 | -.3906842 .2006915 -1.95 0.052 -.7840324 .002664 Page 3

Appendix Page 104 Panel - Nlogit - Double 3.0 results dage1 | .0089297 .1860851 0.05 0.962 -.3557904 .3736499 dage2 | .7519982 .221419 3.40 0.001 .318025 1.185971 dage3 | .8129899 .1757051 4.63 0.000 .4686143 1.157365 ------+------alt4 | dincome3 | -.5076689 .3026104 -1.68 0.093 -1.100774 .0854367 dincome8 | -.8004783 .2591183 -3.09 0.002 -1.308341 -.2926157 dincome9 | -.0831976 .2387494 -0.35 0.727 -.5511378 .3847427 dincome10 | -.0126185 .1782284 -0.07 0.944 -.3619398 .3367029 dage1 | -.1314997 .2175607 -0.60 0.546 -.5579108 .2949113 dage2 | .9441252 .2116173 4.46 0.000 .5293629 1.358888 dage3 | .7934591 .1832624 4.33 0.000 .4342714 1.152647 ------+------alt5 | dincome3 | -.7745831 .3106806 -2.49 0.013 -1.383506 -.1656604 dincome8 | -.6237404 .2340613 -2.66 0.008 -1.082492 -.1649887 dincome9 | -.1970784 .2301127 -0.86 0.392 -.6480911 .2539342 dincome10 | -.0245973 .1663114 -0.15 0.882 -.3505616 .3013669 dage1 | .0292878 .1802019 0.16 0.871 -.3239015 .382477 dage2 | .9538918 .1995421 4.78 0.000 .5627965 1.344987 dage3 | .8108011 .1732973 4.68 0.000 .4711446 1.150458 ------dissimilarity parameters ------accessibil~y | /noaccess_~u | 1 . . . /access_tau | .4445913 .1120946 .2248899 .6642927 ------accstation | /none_tau | 1 . . . /bay_tau | 1 . . . /union_tau | .4418968 .1030761 .2398713 .6439223 /pearson_tau | 1 . . . ------LR test for IIA (tau = 1): chi2(2) = 13.15 Prob > chi2 = 0.0014 ------

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Appendix Page 105