CUBE the
GLOBAL SOFTWARE
September, 2012
Cube, The Global Software
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For Futura 2009
Prepared by
Len Johnstone of Oriental Consultants
and
Nate Chanchareon of Citilabs
September, 2012
Cube, The Global Software
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Overview of Citilabs
Citilabs – the Company
 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
CUBE from the Middle East to
Western Asia
•
•
•
•
•
•
September, 2012
The Middle East
India
South East Asia
Philippines
Highlighting of Selected
Korea
Examples
China
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CUBE from the Middle East to
Western Asia
• The Middle East
–Egypt
–Cairo
–Doha
–Kuwait
September, 2012
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Objectives of the Study in Egypt
– Formulate National Transportation Master
Plan for Egypt, viewing the target year 2027;
• Identify high-priority projects and strategies 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 transfer for transport
planning
September, 2012
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Egypt Today
September, 2012
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Egyptian Model
September, 2012
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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. Chemicals
10. Machinery and Miscellaneous Articles
11. Live Animal and Animal Products
September, 2012
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Cargo Modes:
1. Road
2. Rail
3. IWT
4. Pipeline
9
Cargo Model Structure
September, 2012
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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
September, 2012
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Objectives of the Study in Cairo
– Formulate an Urban Transportation Master
Plan in the Greater Cairo Region, viewing the
target year 2022;
• Identify high-priority projects whose implementation is to be
achieved urgently, within the overall master plan framework
– Conduct a feasibility study for the selected
high-priority project(s); and
– Carry out technology transfer for urban
transport planning
September, 2012
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Population by Qism
CREATS
CREATSStudy
StudyArea
AreaYear
Year
2001
Population:
2001 Population:
14.3
14.3Million
Million
13
A Snapshots of the Transport Model
in Cairo
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Snapshots of the Transport Model
-Modal split
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In Doha, as a planning and GIS Tool
September, 2012
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September, 2012
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IN Kuwait, as a Highway Analysis Tool for Upgrade of Access Roads from Western
Kuwait
Kuwait model structure
Output from this block show
traffic volume on all link and
centroid connecters
Concept for turning volume results
Node b
Right turn vol
Node b
Node a
Node a
Straight turn vol
Node b
Node a
Left turn vol
A= node a
B=node b
Vol = turning volume (pcu/hr)
Node a
CUBE from the Middle East to
South Asia
• India
September, 2012
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Formulation of Travel Demand Model for Route
Selection & Techno-Economic Feasibility for
Proposed Light Rail Transit (LRT) Corridor Project
between Joka and Barrackpur in Kolkata Urban Area
September, 2012
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Study Objective
Data Collection

Develop a Travel
Demand Model and
predict ridership on
the proposed Kolkata

Primary Data

Volume Count & OD surveys

Road inventory survey
•
Speed and Delay Survey

Willingness - to - Shift/Pay surveys
Secondary Data

2001 Census data

Land use maps, existing and proposed

Bus/Suburban train Transport
operational details - coverage/route
Light Rail Transit
maps / frequency / performance / fare
structure
Project corridor
September, 2012

Population & Employment details

Master Plan for kolkata
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Methodology
RSI Survey
Kolkata
Network
Screen line
volume count,
cordon count,
speeds, Trip
length
Future
Transport
Network
HHI Survey
Base year
planning data
Base year
travel pattern
Trip Generation &
Attraction
Relationship
Base year model
Development and
Validation
Generalize
Cost Skims
Calibration (Trips
Distribution and Mode
split Parameters)
Calibrated Model
Horizon year
Planning data
Ridership
estimation
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Zoning
•
•
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•
•
•
•
September, 2012
KMC – 146
HMC – 53
Salt lake city – 5
New Town – 8
Outside city – 269
External Zones – 9
Total – 490 Zones
Road Network
•
Total road length : 1773 Kms
•
No. of Nodes : 1969
•
No. of Links : 2565
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Transit Network
Tram routes
Sub urban routes
Metro route
Sub urban Rail Routes – 16
Tram routes – 20
Metro Route – 1
Shared Auto Routes – 49
September, 2012
Shared Auto
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Routes Cube, The Global Software
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Transit Network
Mini bus Routes
Mofussil bus Routes
City bus Routes
City Bus routes
- 251
Mofussil Bus routes –
149
Mini bus Routes
- 138
September, 2012
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Cost 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
Sub –urban rail fare
Value of Time
S NO
Mode
VOT/Min
1
Walk
0.19
Bus Fare
2
Distance
(km)
Bicycle
0.15
6
4.00
3
Taxi
0.30
8
4.50
4
Auto rickshaw
0.31
10
5.00
5
Two Wheeler
0.37
12
6.00
6
Car
0.49
14
8.00
7
Bus
0.20
27
14.00
Bus fare
Metro fare
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Distance
(km)
Metro Fare
Up to 5
4
5 – 10
6
>10
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DISTANCE
(kms)
SUB-URBAN
Rail Fare ( Rs)
1-5
6-10
11-15
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
71-75
76-80
81-85
86-90
3
3
4
5
6
6
7
8
9
10
11
12
12
13
14
15
16
17
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Validation- Private vehicles
Mode
Two wheeler
Car
Auto rickshaw
Taxi
September, 2012
Screen line 1 – North - South
Direction 1
Direction 2
Assigne
Assigned
Observed %Difference
d
Observed %Difference
1582
1621
2%
973
1052
7%
2712
2697
-1%
2224
2542
12%
915
928
1%
1422
1504
5%
1684
1877
10%
1097
1175
7%
Screen line 2- North south
Direction 1
Direction 2
Assigne
Mode
Assigned
Observed %Difference
d
Observed %Difference
Two Wheeler
416
415
0%
610
621
2%
Car
915
903
-1%
1097
1190
8%
29
Auto rickshaw
69
71
2%
11
11
0%
Software 370
29
Taxi Cube, The Global
415
-22%
383
350
-9%
Validation-Private vehicles
-Cordon
Inbound traffic
Mode
September, 2012
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 30
8%
1084
1130
4%
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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
Shared Auto Flow
Suburban Flow
Bus Flow
Tram Flow
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Transit Flow
Highway Flow
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Ridership Results-2016
From
Via
To
Barrakpur
Esplande
Joka
PPHPD: Peak passengers per hour per direction
Max. Sectional
Daily
Daily
Average Lead
Load (PPHPD) Passenger-KM Ridership
(KM)
13,796
4102440
294330
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Recent projects
• High Speed Rail Projects in Southern India
– Chennai
– Hyderabad
– Bengaluru
– Thiruvananthapuram
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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National Model Structure
Socio-Economic Data
Passenger Demand
Freight
Distribution
Freight Distribution
Modal Split
Freight Modal Split
Passenger Car & PT
Truck
Trip Assignment
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National Model : NAM
• 937 Zones
• 926 Internal Zones
• 11 External Zone
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Person Trip
• Road Network 52,000 Km.
• Rail Network 4,200 Km.
• Air Network
Highway Netwok
Rail Network
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Air Network
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National Model : NAM – Road Network
Curve based on MOT GIS
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Freight Transport Model
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OUTPUT
Person Trips
• Pcu-Km
• Pcu-Hr
• Speed
• Average Trip Length
• V/C
September, 2012
Freight
• Ton-Km
• Ton-Hr
• Speed
• Average Trip Length
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Freight Transport Model
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Vietnam
• National Model
• City Models
– Hoh Chin Minh City
September, 2012
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National Transport
Model
MULTI MODAL
TRANSPORT MODEL
OF VIETNAM
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Toll Expressway
Link Between
China and Hanoi
Project Value
-1 Bil USD
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• 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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Global
Software
Model Enhancement
Economic
Integration
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Indonesia, a new government sponsored
model is under development for Jakarta
R
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d
N
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w
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r
k
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P
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I
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T
r
a
n
s
p
o
r
t
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Public Transport Modeling in Singapore
using TRIPS and CUBE
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 Transit
Bus Area
Rail Lines
SMRT
Bus Area
Rail Lines
Central Area
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SBS Transit
Bus
• 2,800 buses
• 254 routes
• 2.3 million rides daily
• 16 bus interchanges, 16 terminals
• Over 3,000 bus stops
Rail
• 360,000 rides daily
• 15 MRT stations, 19 LRT stations in
operation
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
September, 2012
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[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
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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)
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Limitations and
Challenges
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
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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
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
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Issues and Imperatives for Integrated Public
Transport Planning for Metro Manila
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
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Land Area: 636 sq. km
Population (2007): 11.55 million
Population Density (2007): 18,166 persons/ sq. km
Futura Asia-Pacific 2009
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100-Km radius
Land Area: 38,544 sq. km
Population (2000): 27.4 million
Population Density (2000): 712 persons/ sq. km
Futura Asia-Pacific 2009
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)
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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
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.
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Metro Manila Vehicle
Registration (1981-2005)
•Metro Manila accounts for around 30% of all registered vehicles
•Increase in number of Utility Vehicles (UV) and Tricycles
1,600,000
1,400,000
Number of units
1,200,000
1,000,000
800,000
600,000
400,000
200,000
20
05
20
03
20
01
19
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
-
Year
Car new
Car renewal
Utility Vehicle (UV) new
Utility Vehicle renewal
Motorcycle new
Motorcycle renewal
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 railbased transit services
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Formal vs. Informal Transport
FX’s – Informal
Taxis – Formal/Informal
Jeepneys – Informal
MRT/LRT – Formal
Buses – Formal/Informal
Tricycles – Informal
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MRT2
LRT1
MRT3
pnr
MRT7
Northrail
MRT2
MRT4
MRT8
LRT1
MRT3
PNR Southrail
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
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Development of Public Transport
Planning Support System
Conceptual Framework
Review of Existing
Transport Data
Compilation of Public
Transport
Supply Data
September, 2012
Compilation of Public
Transport
Demand Data
Review of Existing
Transport Policies and
Regulatory Framework
Database and GIS
Development
Organizational and
Change Management
Study
Information System
Development/ Model
Development
Review of Existing
Transport Planning
Practices and Methods
Development of Public
Transport Planning
and Decision Support
Futura Asia-Pacific 2009
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Survey Data
Base GIS Data
82
CUBE Application for converting JICA STRADA Data
September, 2012
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Promotion of Non-Motorized
Transport
Marikina
Bikeways
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Development of Bicycle Planning Toolkit
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Case Studies from the USA
I. CALIFORNIA HIGH-SPEED RAIL:
Ridership and Revenue Forecasting Study
• 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
Project
Objectives
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
September, 2012
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Model Service, Amenities and Cost Scenarios to Maximize
Ridership
Level of Service
Frequency
Speed
Service Amenities
On-Time Reliability
Seating Comfort
Safety and Security
User Productivity
Other
Station Location
Other
Costs
Fares
Parking
Driving Cost
Other
Ridership and Revenue Forecasts
Sensitivity Analyses
Travel Times
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Amenities
Costs
Using Cube for HSR Ridership
Study
 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)
92
II. TRANSBAY RIDERSHIP FORECASTING MODEL
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
September, 2012
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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
September, 2012
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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
September, 2012
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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
An Example of Macro-, Messo- and Microscopic Model
Using Cube: Houston-Galveston Area Council (HGAC)
•
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
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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
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THE END
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