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 Island of Montreal 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.