ALI RAZA KHAN GRADUATE STUDENT, ,

CO-AUTHOR CIPRIAN ALECSANDRU ASSISTANT PROFESSOR, CONCORDIA UNIVERSITY, MONTREAL  History of urban transportation

 The Montreal subway system

 Modeling & travel demand forecasting

 Modeling the Montreal subway system

 Conclusions

2  Coaches – 1600

 Horse Omnibus – 1798

 Steam Engine – 1800

• Railroad – 1825

3  Electric Motor – 1830

– 1863

 Cable Cars – 1880

 Electric railway – 1890

 Automobile – 1930

4  Foundation laid down by Mr.

• May 23, 1962 – Construction starts

 5000 workers

• October 14, 1966 – Opened to public

5 6  Small Gauge of 2.5

meters

• Allowed double

in 7.1m x 4.9 m tunnel

7 8 9  Subway Cars

• MR-63 built by (1960’s)

• MR-73 built by Bombardier (1970’s)

• A total of 759 train cars, 336 MR-63, 423 MR-73 (2002, The Montreal

Metro: a source of pride)

• Require 750 volts of traction power

• Train set = Two motor cars, one trailer

10 11  Average 700,000 trips each week

 Managed by Societe de transport de Montreal, STM

 Budget of $ 880.3 million

 Long-range, coordinated and comprehensive planning

12  Model

• Description designed to show workings of a system

• Physical, Conceptual, Mathematical, Computer etc.

• Used to analyze complex system problems

13 Assumptions

Actual System Model

Analysis & Interpretation Computations

Findings/ Validation Results Recommendations

Future System

14  Transportation Database

• Two basic components

 Highway/transit network

 Network – Distance, Speed, Capacity

• Socioeconomic data

15  Transportation Database

• Transit Network Station # 2

Dwell Time  Headway & Level of Service

Headway

 Dwell time Distance

Station # 1 Time

16 Land use Socioeconomic Characteristics Characteristics  Travel Demand Forecasting

Trip • Forecast present & future travel Generation

Transit Highway • To determine different aspects System system Trip  Peak hour movements Distribution

 Ridership estimates Modal Split Auto  Air quality analysis Occupancy

Interzonal  Travel demand management Interzonal automobile transit trips trips

Transit Highway traffic Traffic Assignment assignment 17  Trip Generation

• Estimates number of persons or vehicle trips

 Productions

 Attractions

 Trip Distribution

• Links productions of one zone to attractions of the other zone

• Gravity Model – one of the methods

18  Trip distribution

• Gravity Model

 Based on gravitational theory of Newtonian physics

퐴푗 퐹푖푗 퐾푖푗 푇 푖푗 = 푃푖 푧표푛푒푠 푘=1 퐴푘 퐹푖푘 퐾푖푘  Friction factor – behaviour of a traveler in terms of

perception of distance

19  Trip distribution

• Gravity Model

 Gamma Function

푏 푐∗푡푖푗 퐹 푖푗 = 푎 ∗ 푡푖푗 ∗ 푒

• Travel time matrix

• Iterative process of balancing productions & attractions

20  Mode Choice

• allocate trips onto different transportation modes

 Traffic Assignment

• Assignment of predicted flows to actual routes

 Estimated data used to optimize system

21  Simulated in Visum 10

 Building the database

• GIS shape files

Lines

 Stations

22  Importing stations

 Tracing metro routes

 Initial network

• Metro lines

• Stations

23  Transportation Database

• Transportation Systems

 Metro Blue

 Metro Green

 Metro Orange

 Metro Yellow

 Walk

 Station to zone connection

24  Transportation Database

• Line Routes

 Name

 Direction

 Schedule/Timetable

25  Transportation Database

• Traffic Analysis Zones

 Superimposing different GIS

layers

 Metro Lines

 Stations

 Zones

26 27  Travel-demand estimation

• Trip Generation

 OD Survey 2003

 Obtained Productions and attractions

28  Productions & Attractions

29  Trip Distribution

• Gravity Model – Inputs

 Productions/Attractions

 Friction Factor Matrix

30  Trip Distribution

• Classify – Visum

• Kalibri –Visum

• Gravity Model

 Iterations

 OD Matrix

31  Trip distribution

• Distribution of trips from zones to stations

 Turnstile counter data

 Assign weight to stations

 Assign weight to connectors in Visum

32  Mode Choice

• One mode of travel

 Traffic Assignment

• Done by Visum

33 34 35 No. of Transfers in a day (24 hours) No. of Transfers during evening rush hour 250,000 80,000 227,161 70,000 66,693

200,000 60,000

50,000 150,000

40,000 106,298 101,122 31,016 100,000 29,774 30,000

20,000 48,362 14,990 50,000

10,000

- - Lionel Groulx Berri-UQAM Snowdon Lionel Groulx Berri-UQAM Snowdon Jean Talon

36  Analysis

Initial time Time profile with profile reduced dwell time • Dwell Time e c n a t s i  Optimize system D

 Provide extra train Time

 Optimize terminal times

37  Dwell time reduction

• Important for rapid transit

• Several methods can be used

 Markings on platforms

 Acceleration/deceleration of

trains

38  Analysis

푃 훼 = • Headway & Level of Service 퐶

60. 퐶푣. 푛 퐶표푓푓푒푟푒푑 =  Headway and LOS are interrelated ℎ푚푖푛

 Montreal’s metro operates at LOS E during rush hour

(0.31-0.38 m2/person) – TCQSM 2003

39 40 41  Limitations

• Distribution of trips between zones & stations

 Assumes lines running in opposite direction through a station are

used equally, may not be the case

• OD data for a period of 24 hours

 Assumes volume to be same throughout the day

 Peak hour volumes can vary

42 Speed of metro trains changes with distance between stations

43  Recognize and reorganise structure of service

• Efficient operations

 Mathematical & Computer Models

• Simulate operational characteristics of Montreal’s rapid transit

 Four step forecasting model

 Gravity Model

 Station turnstile counter data, has limitations

 State of the art software, Visum

 Boarding/Alighting, Transfers

 Analysis

44  Future work

• Provides a base for future work

• Reliable data can be used for Microscopic simulation - VISSIM

 Accurate results

 Pedestrian Simulation

 Boarding/Alighting

 Transfers

45 46