ALI RAZA KHAN GRADUATE STUDENT, CONCORDIA UNIVERSITY, MONTREAL
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
Rapid Transit – 1863
Cable Cars – 1880
Electric railway – 1890
Automobile – 1930
4 Foundation laid down by Mr. Jean Drapeau
• May 23, 1962 – Construction starts
5000 workers
• October 14, 1966 – Opened to public
5 6 Small Gauge of 2.5
meters
• Allowed double track
in 7.1m x 4.9 m tunnel
7 8 9 Subway Cars
• MR-63 built by Canadian Vickers (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
Metro 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 Jean Talon 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