September, 2012 Cube, The Global Software 1
CUBE theCUBE theGLOBAL SOFTWAREGLOBAL SOFTWARE
September, 2012 Cube, The Global Software 2
For Futura 2009
Prepared by
Len Johnstone of Oriental Consultants and
Nate Chanchareon of Citilabs
Develops software for the modeling of transportation systems
Offices USA : Tallahassee, San Francisco Europe : Paris, Milan Asia : Beijing, Mumbai,
Bangkok Coming Soon in 2013 2,500 cities on 6 continents in more than 70
countries
Citilabs – the Company
Overview of Citilabs
September, 2012 Cube, The Global Software 4
CUBE from the Middle East to Western Asia
• The Middle East• India• South East Asia• Philippines• Korea• China
Highlighting of Selected Examples
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CUBE from the Middle East to Western Asia
• The Middle East–Egypt–Cairo–Doha–Kuwait
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Objectives of the Study in Egypt– Formulate NationalNational Transportation Master PlanTransportation Master Plan for
Egypt, viewing the target year 2027;• Identify high-priority projectshigh-priority projects and strategiesstrategies whose implementation
is to be achieved urgently, within the overall master plan framework
– Creation of a National Geodatabase linking demographics and transport
– Development of National Economic and Freight forecasts
– Carry out technology transfertechnology transfer for transport planning
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Egypt Today
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Egyptian Model
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Cargo Modal Transfer
Commodity Group: 1. Agricultural Products 2. Foodstuffs and Animal Fodder 3. Solid Mineral Fuels 4. Petroleum Products 5. Ores and Metal Waste 6. Metal Products 7. Crude and Manufactured Minerals Building Materials 8. Fertilizers 9. Chemicals10. Machinery and Miscellaneous Articles11. Live Animal and Animal Products
Cargo Modes:1. Road2. Rail3. IWT4. Pipeline
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Cargo Model Structure
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Key Questions
• Impact of Shifting cargo from road to other sectors?
• What additional Infrastructure is needed?• Impact of removal of fuel subsidy
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Objectives of the Study in Cairo
– Formulate an Urban Transportation Master Urban Transportation Master PlanPlan in the Greater Cairo Region, viewing the target year 2022;
• Identify high-priority projectshigh-priority projects whose implementation is to be achieved urgently, within the overall master plan framework
– Conduct a feasibility studyfeasibility study for the selected high-priority project(s); and
– Carry out technology transfertechnology transfer for urban transport planning
13
Population by QismCREATS Study Area Year
2001 Population: 14.3 Million
CREATS Study Area Year CREATS Study Area Year 2001 Population: 2001 Population:
14.3 Million14.3 Million
14
A Snapshots of the Transport Model in Cairo
15
Snapshots of the Transport Model -Modal split
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In Doha, as a planning and GIS Tool
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Kuwait model structure
IN Kuwait, as a Highway Analysis Tool for Upgrade of Access Roads from Western Kuwait
Output from this block show traffic volume on all link and
centroid connecters
Node a
Node b
A= node a
B=node b
Vol = turning volume (pcu/hr)
Node aNode a
Node b
Node aNode b
Right turn vol
Straight turn vol
Left turn vol
Concept for turning volume results
September, 2012 Cube, The Global Software 21
CUBE from the Middle East to South Asia
• India
September, 2012 Cube, The Global Software 2222
Formulation of Travel Demand Model for RouteFormulation of Travel Demand Model for Route
Selection & Techno-Economic Feasibility for Selection & Techno-Economic Feasibility for
Proposed Light Rail Transit (LRT) Corridor Project Proposed Light Rail Transit (LRT) Corridor Project
between Joka and Barrackpur in between Joka and Barrackpur in KolkataKolkata Urban AreaUrban Area
September, 2012 Cube, The Global Software 2323
Develop a Travel
Demand Model and
predict ridership on
the proposed Kolkata
Light Rail Transit
Project corridor
Study ObjectiveStudy Objective Primary DataPrimary Data
Volume Count & OD surveys Volume Count & OD surveys
Road inventory survey Road inventory survey
• Speed and Delay SurveySpeed and Delay Survey
Willingness - to - Shift/Pay surveysWillingness - to - Shift/Pay surveys
Secondary DataSecondary Data
2001 Census data2001 Census data
Land use maps, existing and proposedLand use maps, existing and proposed
Bus/Suburban train Transport Bus/Suburban train Transport
operational details - coverage/route operational details - coverage/route
maps / frequency / performance / fare maps / frequency / performance / fare
structurestructure
Population & Employment detailsPopulation & Employment details
Master Plan for kolkataMaster Plan for kolkata
Data Collection
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Methodology
Kolkata Network
RSI Survey HHI Survey
Base year travel pattern
Base year model Development and
ValidationScreen line
volume count, cordon count, speeds, Trip
length
Generalize Cost Skims
Calibration (Trips Distribution and Mode
split Parameters)
Calibrated ModelFuture Transport Network
Horizon year Planning data
Base year planning data
Trip Generation & Attraction
Relationship
Ridership estimation
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Zoning
• KMC – 146
• HMC – 53
• Salt lake city – 5
• New Town – 8
• Outside city – 269
• External Zones – 9
• Total – 490 Zones
• Total road length : 1773 Kms
• No. of Nodes : 1969
• No. of Links : 2565
Road Network
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Transit NetworkTransit NetworkTram routesTram routes
Sub urban routesSub urban routes
Metro routeMetro route
Shared Auto RoutesShared Auto Routes
Sub urban Rail Routes – 16Tram routes – 20Metro Route – 1Shared Auto Routes – 49
September, 2012 Cube, The Global Software 2727
Transit NetworkTransit NetworkMini bus RoutesMini bus Routes
City Bus routes - 251 Mofussil Bus routes – 149Mini bus Routes - 138
City bus RoutesCity bus Routes
Mofussil bus RoutesMofussil bus Routes
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Cost ParametersCost Parameters
Auto Fare -minimum 10 Rs and 7 Rs / km,
Taxi fare <2 kms 20 Rs (>2 km 10 Rs/km)
Vehicle operating cost Car - 6.5 Rs/km TW - 1.95 Rs/Km
Value of Time
S NO Mode VOT/Min
1 Walk 0.19
2 Bicycle 0.15
3 Taxi 0.30
4 Auto rickshaw 0.31
5 Two Wheeler 0.37
6 Car 0.49
7 Bus 0.20
DISTANCE (kms)
SUB-URBAN Rail Fare ( Rs)
1-5 3
6-10 3
11-15 4
16-20 5
21-25 6
26-30 6
31-35 7
36-40 8
41-45 9
46-50 10
51-55 11
56-60 12
61-65 12
66-70 13
71-75 14
76-80 15
81-85 16
86-90 17
Sub –urban rail fareSub –urban rail fare
Distance (km)
Bus Fare
6 4.00
8 4.50
10 5.00
12 6.00
14 8.00
27 14.00
Bus fareBus fare
Metro fareMetro fare
Distance (km)
Metro Fare
Up to 5 4
5 – 10 6
>10 8
September, 2012 Cube, The Global Software 2929
Validation- Private vehicles
Screen line 1 – North - South
Mode
Direction 1 Direction 2
Assigned Observe
d %Difference
Assigned
Observed
%Difference
Two wheeler 1582 1621 2% 973 1052 7%Car 2712 2697 -1% 2224 2542 12%Auto rickshaw 915 928 1% 1422 1504 5%Taxi 1684 1877 10% 1097 1175 7%
Screen line 2- North south Direction 1 Direction 2
Mode Assigned Observe
d %Difference
Assigned
Observed
%Difference
Two Wheeler 416 415 0% 610 621 2%Car 915 903 -1% 1097 1190 8%Auto rickshaw 69 71 2% 11 11 0%Taxi 415 370 -22% 383 350 -9%
September, 2012 Cube, The Global Software 3030
Validation-Private vehicles-Cordon
Mode
Inbound traffic Outbound Traffic
Assigned
Observed
%Difference
Assigned
Observed
%Difference
Two wheeler 844 740 -14% 503 554 9%
Car + Taxi 1309 1225 -7% 816 728 -12%
Auto rickshaw 739 806 8% 1084 1130 4%
September, 2012 Cube, The Global Software 3131
PT Validation
North- South
Screen line Assigned Observed
%Difference Assigned
Observed %Difference
1 75969 66894 14% 70073 61985 13%
2 33605 30229 11% 69679 62449 12%
3 12128 10562 15% 20145 18893 7%
East-west
4 103623 91565 13% 72094 63976 13%
5 63424 57089 11% 63965 74568 -14%
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Flow Diagrams
Bus FlowBus Flow
Shared Auto FlowShared Auto Flow
Tram FlowTram Flow
Suburban FlowSuburban Flow
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Highway FlowHighway Flow
Transit FlowTransit Flow
Ridership Results-2016
From Via To Max. Sectional Load (PPHPD)
Daily Passenger-KM
Daily Ridership
Average Lead (KM)
Barrakpur Esplande Joka 13,796 4102440 294330 14
PPHPD: Peak passengers per hour per direction
Recent projects
• High Speed Rail Projects in Southern India– Chennai– Hyderabad– Bengaluru– Thiruvananthapuram
September, 2012 Cube, The Global Software 36
CUBE from the Middle East to Western Asia
• South East Asia–Thailand–Vietnam–Indonesia–Singapore–The Philippines
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Thailand
• Many uses of CUBE Software at both National and City Level such as
• National Level– Evaluate National Transport Plans– High Speed Rail Projects
• City Level – Bangkok• City Level - Phisanulok
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Socio-Economic DataSocio-Economic Data
Passenger DemandPassenger Demand FreightFreight
DistributionDistribution Freight DistributionFreight Distribution
Modal SplitModal Split Freight Modal SplitFreight Modal Split
Trip AssignmentTrip Assignment
National Model Structure
TruckTruckTruckTruckPassenger Car & PTPassenger Car & PTPassenger Car & PTPassenger Car & PT
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National Model :
NAM
• 937 Zones• 926 Internal
Zones• 11 External Zone
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Person TripPerson Trip
Highway Netwok
Rail Network Air Network
•Road Network Road Network 52,000 Km. 52,000 Km.
•Rail Network Rail Network 4,200 Km.4,200 Km.
•Air NetworkAir Network
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National Model : NAM – National Model : NAM – Road NetworkRoad Network
Curve based Curve based on MOT GISon MOT GIS
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Freight Transport Model
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OUTPUTOUTPUT
• Pcu-Km
• Pcu-Hr
• Speed
• Average Trip Length
• V/C
•Ton-KmTon-Km
•Ton-HrTon-Hr
•SpeedSpeed
•Average Trip Average Trip LengthLength
FreightFreightPerson TripsPerson Trips
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Freight Transport Model
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Vietnam
• National Model• City Models
– Hoh Chin Minh City
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MULTI MODAL MULTI MODAL TRANSPORT MODEL TRANSPORT MODEL OF VIETNAMOF VIETNAM
National Transport Model
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Toll Expressway Link Between China and Hanoi
Project Value -1 Bil USD
September, 2012 Cube, The Global Software 48Model Enhancement with Economic Integration48
• CURRENT STATUS– Economic Cost estimated for each scenario– With and without Cases from CUBE Voyager
written to CSV Files– Economic evaluation Spread sheet linked directly
to Voyager output files
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Indonesia, a new government sponsored model is under development for Jakarta
Road Network
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PublIc Transport
Public Transport Modeling in Singapore using TRIPS and CUBE
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Some facts on Singapore• Land area: 700 km2
• Population: 5 million
• Over 50% use public transport
• Daily Rides: – Bus (3.0 million)– Rail (1.6 million)
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Public Transport in Singapore
• Bus/MRT/LRT are main modes• 2 major multi-modal operators
– SBS Transit– SMRT
SBS TransitBus AreaRail LinesSMRTBus AreaRail Lines
Central Area
SBS TransitBus• 2,800 buses• 254 routes• 2.3 million rides daily• 16 bus interchanges, 16 terminals• Over 3,000 bus stopsRail• 360,000 rides daily• 15 MRT stations, 19 LRT stations in
operation
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Building up Modelling Expertise• Acquired TRIPS in 2001• Evaluate impact of route
changes• Assess viability of new route
proposals• Test many options before
determining the best proposal• Being self-sufficient in
transport modelling.
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Model Development• Maps from street
directories• Link speeds from onboard
bus equipment• Lines information from
public transport guides• Demand matrices from
ticketing data• Development data from
various agencies
[ez-link reader][IDFC console]
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Calibration
• Total Boarding and Passenger KM by route and direction
• Total passenger volume leaving towns
• Station-to-Station movements for MRT
• Heaviest load points by route
58
Major Applications
• Implementation of over 20 new routes and more than 50 route changes (2001–2008)
• Commencement of North-East MRT Line and Sengkang LRT East Loop (2003)
• Opening of Punggol LRT East Loop and Sengkang LRT West Loop (2005)
Limitations and Challenges
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Strictly Public Transport• Demand matrices
built directly from smart card data
• Does not account for effects on private transport modes
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Limited Output from Reports
• Planners need detailed breakdown of passenger impact in terms of fares, journey times and number of transfers for any service proposal
• Too many skimming process slow down model run times
• Detailed computations still done manually outside of model
Migration to CUBE and its benefits
Migration to CUBE• Upgraded to CUBE since Jul 2008• Ease updating of network and matrices• Enhance evaluation of proposals
• Automate generation of useful planning data
• Better interface with own systems
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Network Map
• Use of layers
• Easier to navigate and update interactively
• Wider choice of colour sets
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Inputs in Database Formats
• Nodes, links, matrix records can be maintained in DBF formats easily editable in Excel
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Assignment• Program boxes reduced significantly• Can put more functions in each program group
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Select Link
• Ability to select line, nodes, links or a combination of criteria
• For example: -
MW[1] =
SELECTLINK((L=12809-40025* + LINE=2400)
& (L=12200-40026* + LINE=2400))
for a new bus service connecting 2 different MRT stations
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Further Development Work
• Sensitivity tests of assignment parameters and fare models
• Improve quality of reports• Matrix estimation using screen line flows• Path analysis with through fares
Issues and Imperatives for Integrated Public Transport Planning for Metro Manila
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Urbanization Trend in Metro Manila
• High population growth rates and in-migration• 13 percent of the country’s population are packed in only about
0.2 percent of the country’s land area• Metro Manila dominates the economy accounting for 43.5 percent
of the country’s GDP in 2000• The effect of rapid urbanization of the metropolis spilled over the
adjoining municipalities• Comprised of 17 cities and municipalities
Futura Asia-Pacific 2009
Futura Asia-Pacific 2009 71
Land Area: 636 sq. km Population (2007): 11.55 million Population Density (2007): 18,166 persons/ sq. km
Futura Asia-Pacific 2009
Land Area: 38,544 sq. km Population (2000): 27.4 million Population Density (2000): 712 persons/ sq. km
100-Km radius
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Development Pattern• Uncontrolled development that has encouraged urban sprawl, or
low density development (residential) at the outer areas• Proliferation of low-income households, i.e. ‘informal settlers’, in
the inner city areas
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Increasing Travel Demand• Drastic increase in motorized trips in Metro Manila
– 10.6 million trips (1980)– 16.95 million trips (1996)
• Serious increase in car ownership– 10% (1980)– 20% (1996)
Futura Asia-Pacific 2009
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Economic Costs of Traffic Congestion
• 100 Billion Pesos (in 1996 values) is lost each year due to road congestion
• Based on travel time delays and 50% of hourly income across different occupation groups
• In addition, reduction in the urban quality of life increases health and living costs
Futura Asia-Pacific 2009
Source: Economic Impact of Traffic Congestion in Metro Manila,” A Study conducted by University of the Philippines National Center for Transportation Source: Economic Impact of Traffic Congestion in Metro Manila,” A Study conducted by University of the Philippines National Center for Transportation Studies (NCTS) for the NEDA Legislative Executive Development Advisory Committee (LEDAC), 2000.Studies (NCTS) for the NEDA Legislative Executive Development Advisory Committee (LEDAC), 2000.
September, 2012 Cube, The Global Software 76Futura Asia-Pacific 2009
Metro Manila Vehicle Registration (1981-2005)
-
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Year
Num
ber o
f uni
ts
Car new Car renewal Utility Vehicle (UV) new Utility Vehicle renewal Motorcycle new Motorcycle renewal
•Metro Manila accounts for around 30% of all registered vehicles•Increase in number of Utility Vehicles (UV) and Tricycles
Source: Land Transportation Office (LTO)Source: Land Transportation Office (LTO)
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Public Transport Trend
• Increasing travel demand• Share of public transport is still high but this
may not be sustained in the future• Low quality road-based public transport
services• Lack of integration between road and rail-
based transit services
Futura Asia-Pacific 2009
September, 2012 Cube, The Global Software 78
Formal vs. Informal Transport
Futura Asia-Pacific 2009
MRT/LRT – Formal
Taxis – Formal/Informal
Buses – Formal/Informal
FX’s – Informal
Jeepneys – Informal
Tricycles – Informal
MRT2
MRT3
pnr
LRT1
PNR Southrail
MRT3
MRT8
MRT2
LRT1
MRT7Northrail
MRT4
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Public Transport Planning Issues• Increasing travel demand• Increasing demand for new paratransit modes e.g. FX Taxi• Increased preference for higher quality modes• Increasing ownership and use of private modes, namely car and
motorcycle• Low quality of road-based PT services
– Oversupply– Inadequacy in planning and operations management
Futura Asia-Pacific 2009
September, 2012 Cube, The Global Software 82Futura Asia-Pacific 2009
Conceptual FrameworkReview of Existing
Transport Data
Compilation of PublicTransport
Supply Data
Compilation of PublicTransport
Demand Data
Review of ExistingTransport Policies and
Regulatory Framework
Organizational andChange Management
Study
Database and GISDevelopment
Review of ExistingTransport Planning
Practices and Methods
Information SystemDevelopment/ Model
Development
Development of PublicTransport Planning
and Decision Support
Survey Data
Base GIS Data
Review of ExistingTransport Data
Compilation of PublicTransport
Supply Data
Compilation of PublicTransport
Demand Data
Review of ExistingTransport Policies and
Regulatory Framework
Organizational andChange Management
Study
Database and GISDevelopment
Review of ExistingTransport Planning
Practices and Methods
Information SystemDevelopment/ Model
Development
Development of PublicTransport Planning
and Decision Support
Survey Data
Base GIS Data
Development of Public Transport Planning Support System
September, 2012 Cube, The Global Software 83Futura Asia-Pacific 2009
CUBE Application for converting JICA STRADA DataCUBE Application for converting JICA STRADA Data
September, 2012 Cube, The Global Software 84
Promotion of Non-Motorized Transport
Futura Asia-Pacific 2009
MarikinaBikeways
September, 2012 Cube, The Global Software 85
Development of Bicycle Planning ToolkitDevelopment of Bicycle Planning Toolkit
Futura Asia-Pacific 2009
September, 2012 Cube, The Global Software 86Futura Asia-Pacific 2009 86
Case Studies from the USA
I. CALIFORNIA HIGH-SPEED RAIL:I. CALIFORNIA HIGH-SPEED RAIL:Ridership and Revenue Forecasting StudyRidership and Revenue Forecasting Study
Project Objectives
• Evaluate HSR alternatives– Statewide
– Into and out of the San Francisco Bay Area
• Produce performance and evaluation measures– Ridership and revenues, user benefits
– Time and cost savings for new riders
– Impacts on other modes
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Proposed Approach• Use existing models to build high speed rail networks • Develop Logit mode choice models from new data• Perform Assignment to look at ridership• Use Cube PT (Public Transport) Module to:
– Code transit route networks– Access and Egress to trains– Park & Ride and Pedestrian / Bike Catchment Area– Define Fares and penalties– Model Service Scenarios
90
Model Service, Amenities and Cost Scenarios to Maximize Ridership
91
Ridership and Revenue Forecasts
Sensitivity Analyses
Travel Times Amenities Costs
Fares
Parking
Driving Cost
Other
Costs
On-Time ReliabilitySeating ComfortSafety and SecurityUser ProductivityOther
Service Amenities
Frequency
Speed
Station Location
Other
Level of Service
Using Cube for HSR Ridership Study
92
Public Transport projects are much easier to code and manage in Cube than any other software. PT is very flexible.
Project required running over 150 alternatives. It was easy to set up the scenarios in Cube Catalogs and Scenario Manager
Public Transport Module made it easy to manage transport networks for the entire state of California which included thousands of bus and rail lines.
Cube Reports was very helpful in creating various ridership reports ( by purpose, mode of access, egress, class, region, corridor etc)
II. TRANSBAY RIDERSHIP FORECASTING MODELII. TRANSBAY RIDERSHIP FORECASTING MODEL
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Transbay Ridership Study - Overview
• Determine future transit ridership at Transbay Terminal
– AC Transit (Bus bay requirements)
• Analyze the impact of capacity constraints on Transit
• More accurate ridership estimates with improved travel forecasting tools
94
Proposed TRANSBAY Terminal in San Francisco
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Innovative Features of This Project
• New Mode choice model with
detailed transit modes• New capability to model Transit crowding
– Model passenger perception that travel time is more onerous when they have to stand or when the vehicle is crowded
– Increased wait times when passengers are unable to board a crowded vehicle
• Apply a range of capacity assumptions for BART • Analyze ridership and traffic volumes for Peak Hours
95
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Transit crowding model
• When trains are too crowded, riders can:– Wait for next train– Switch to bus or ferry– Switch to auto
• Includes feedback to mode choice models
96
III. Hurricane Evacuation Modeling in Texas
Study Motivation
• In September 2005, Hurricane Rita landed east of Houston
• Well over 1 million people attempted to evacuate from the eight county region
• Severe congestion as a result
Retreat!
• Evacuation routes became “parking lots”.
• Some people spent more than 18 hours on the evacuation routes
• Fatal accidents, abandoned cars, and other safety issues
September, 2012 Cube, The Global Software 101
• Simulation of Transportation Evacuation with Cube: A need to simulate traffic conditions during a region-wide or sub-area evacuation situation
• Simulation is comprised of two key elements:
– The ‘demand’: the number of vehicles wishing to travel from their origin to their destination by time of day
– The ‘supply’: a representation of the roadway infrastructure, and traffic control systems
• Simulation model allows HGAC to test different demand and supply scenarios separately or together
• Results are:
– evaluation statistics for investment – benefit analysis
An Example of Macro-, Messo- and Microscopic Model Using Cube: Houston-Galveston Area Council (HGAC)
September, 2012 Cube, The Global Software 102
Simulation Process – Begins with Regional Model
• Region-wide analysis: current HGAC modeling system in Cube Voyager provides regional traffic flows.
• Important for regional air quality analysis and capital improvement
• However: Is not adequate for the simulation of evacuation situations as it does not simulate the flow of individual vehicles in detail
• But provides a representation of the region’s roadway system and peak period travel demand
September, 2012 Cube, The Global Software 104
THE END