September, 2012Cube, The Global Software1 CUBE the GLOBAL SOFTWARE.

September, 2012 Cube, The Global Software 1

CUBE theCUBE theGLOBAL SOFTWAREGLOBAL SOFTWARE


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


CUBE from the Middle East to Western Asia

• The Middle East• India• South East Asia• Philippines• Korea• China

Highlighting of Selected Examples



• The Middle East–Egypt–Cairo–Doha–Kuwait


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


Egypt Today


Egyptian Model


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


Cargo Model Structure


Key Questions

• Impact of Shifting cargo from road to other sectors?

• What additional Infrastructure is needed?• Impact of removal of fuel subsidy


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


In Doha, as a planning and GIS Tool

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


CUBE from the Middle East to South Asia

• India


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


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


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


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


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


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


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


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%


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%


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%


Flow Diagrams

Bus FlowBus Flow

Shared Auto FlowShared Auto Flow

Tram FlowTram Flow

Suburban FlowSuburban Flow


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



• South East Asia–Thailand–Vietnam–Indonesia–Singapore–The Philippines


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


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


National Model :

NAM

• 937 Zones• 926 Internal

Zones• 11 External Zone


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


National Model : NAM – National Model : NAM – Road NetworkRoad Network

Curve based Curve based on MOT GISon MOT GIS


Freight Transport Model


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


Freight Transport Model


Vietnam

• National Model• City Models

– Hoh Chin Minh City


MULTI MODAL MULTI MODAL TRANSPORT MODEL TRANSPORT MODEL OF VIETNAMOF VIETNAM

National Transport Model


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


Indonesia, a new government sponsored model is under development for Jakarta

Road Network


PublIc Transport

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)


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


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.


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]


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


Strictly Public Transport• Demand matrices

built directly from smart card data

• Does not account for effects on private transport modes


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


Network Map

• Use of layers

• Easier to navigate and update interactively

• Wider choice of colour sets


Inputs in Database Formats

• Nodes, links, matrix records can be maintained in DBF formats easily editable in Excel


Assignment• Program boxes reduced significantly• Can put more functions in each program group


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


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


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


Land Area: 38,544 sq. km Population (2000): 27.4 million Population Density (2000): 712 persons/ sq. km

100-Km radius


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


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)



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 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)


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



Formal vs. Informal Transport


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


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



Conceptual FrameworkReview of Existing

Transport Data

Compilation of PublicTransport

Supply Data


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


Supply Data


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


CUBE Application for converting JICA STRADA DataCUBE Application for converting JICA STRADA Data


Promotion of Non-Motorized Transport


MarikinaBikeways


Development of Bicycle Planning ToolkitDevelopment of Bicycle Planning Toolkit


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


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

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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


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


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


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


• 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)


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


THE END

September, 2012Cube, The Global Software1 CUBE the GLOBAL SOFTWARE.

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Transcript of September, 2012Cube, The Global Software1 CUBE the GLOBAL SOFTWARE.