use of electricity consumption as an input for cost ......use of electricity consumption as an input...

Use of Electricity Consumption as an Input for

Cost Effective Traffic Forecasting

Tissa U Liyanage and Amal S Kumarage

1

Transportation Engineering Division

Department of Civil Engineering

University of Moratuwa

Sri Lanka

Second International Conference on Transport and Logistics System

(INCOTALS)

21st and 22nd August 2006 – Colombo, Sri Lanka

IntroductionIntroduction

�The household electricity consumption units (EcuEcu)

and the incomeincome are correlated

R2 = 0.9012500

15000

Ele

ctr

icity C

on

su

mp

tio

n (

kW

h/y

r)

2

5000

7500

10000

0 25000 50000 75000 100000

Income Group ($US/yr)

Ele

ctr

icity C

on

su

mp

tio

n (

kW

h/y

r)

Household Electricity Versus Income (US 1997)

Source: Jacobson et al, 2004

IntroductionIntroduction

�Vehicle ownershipownership / trip ratestrip rates and the household

income income are correlated

0.50

0.60

0.70

0.80

Vehic

le O

wners

hip

2&3 Wheelers 4 Wheelers

Commercial Total

3

Vehicle ownership versus income

Source: Kumarage, 2001

0.00

0.10

0.20

0.30

0.40

0.50

0 5000 10000 15000 20000 25000 30000 35000 40000

Per Capita Income (US$/yr)

Vehic

le O

wners

hip

The Hypotheses The Hypotheses

)1()( −−−−−−−−−= incomeHouseholdfEcu

)2()( −−= incomeHouseholdfownershipVehicle

4

)2()( −−= incomeHouseholdfownershipVehicle

)(EcufownershipVehicle =

Hence

)( EcuftripsbasedMode =

Therefore

ObjectivesObjectives

1.To determine if electricity consumption can be

used as the primary independent independent

explanatory variableexplanatory variable for traffic forecasting.

5

explanatory variableexplanatory variable for traffic forecasting.

2.To verify its suitabilitysuitability for determining

suburban travel behavior

Study AreaStudy Area

6

Colombo District Map

Study Area – Maharagama Divisional

Secretariat Division

MethodologyMethodology

Data From a Household Survey

Household Characteristics

Ten Types of

Household

Data Collection

Physical Features

7

of Household

Groups

Cross Classification with Electricity Consumption Units (ECU)

Average Trip Rates Vehicle Ownership

Data Analysis

Physical Features

Utility Features

Results

Classification of Household TypesClassification of Household Types

Table 1: Classification of HH on Physical and Utility Type

High (H) Medium (M) Low (L)

Roof Type Asbestos Metal Cadjan

Tile Straw

Concrete

Wall Type Brick Cmt: Blocks Soil Blocks

Stones Mud/others

Quality Level of Material / AccessoryHousehold

Classification

Some examples for quality

level of household types:

Highest Quality – HHHHH

Lowest Quality – LLLLL

8

Stones Mud/others

Floor Type Tile Cement Mud/others

Terrazzo

Granite

Cooking Source Gas Firewood Kerosene

Toilet Type Commode Pour Flush Others

Household Type - Roof X Wall X Floor X Cooking X Toilet

Many combinations in

between are available

Cross Classification of HH Groups with EcuCross Classification of HH Groups with Ecu

0

5

10

15

20

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10

% P

opula

tion

(1) Nos (2) (%) (3) kWh (4) CoV

G1(Kerosene users) 38 2 - -

G2 74 3 72 33

G3 170 8 88 30

Household, Group

Population

Monthly

Electricity

ConsumptionHousehold Groups

Table 2 : HH Groups Based on Physical/Utility Type and Ecu Level

9

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10

HH Group No

G3 170 8 88 30

G4 257 12 91 33

G5 299 14 109 31

G6 310 15 118 30

G7 291 14 134 26

G8 290 14 144 25

G9 145 7 147 26

G10 252 12 167 28

Total 2126 100

0

50

100

150

200

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10

HH Group No

Ecu (

kW

h)

µσ=CoV

GroupHHaforEcuMean=µ

DeviationdardS tan=σ

Population Distribution

Electricity Consumption

Coefficient of variance -

Household Groups and EcuHousehold Groups and Ecu

�The average electricity consumption (Ecu)

increases as quality of household increases

�Higher degree of similarity for Ecu within

Summary:

10

�Higher degree of similarity for Ecu within

Household Group (low CoV)

Vehicle Ownership (Average / household)Vehicle Ownership (Average / household)

BicyclesBicycles Characteristics:

�Ownership reduces as

Ecu increases

ECU Vs Vehicle Ownership R2 = 0.84

0.15

0.20

V:

Ow

ners

hip

11

�Linearly correlated

0.00

0.05

0.10

60 80 100 120 140 160 180

ECU (kWh)

V:

Ow

ners

hip

Bicycle


M Bike & 3WheelerM Bike & 3Wheeler Characteristics:

� Both ownership rates

increases and then reduces as

Ecu increases


0.20

0.25

V:

Ow

ners

hip

12

Ecu increases

� 3 wheelers rate peaks at a

lower Ecu level

� M: bikes peak at middle Ecu

level

� M: bikes remains at a lower

rate and 3W vanishes at

higher Ecu level

R2 = 0.93

0.00

0.05

0.10

0.15

60 80 100 120 140 160 180

ECU (kWh)

V:

Ow

ners

hip

3W M Bike


Cars & VansCars & Vans


0.30

0.40

0.50

V:

Ow

ners

hip

Characteristics:

� Both car & van ownership

increases as Ecu increases

� Vans - linearly correlated

13

R2 = 0.94

0.00

0.10

0.20

0.30

80 100 120 140 160 180

ECU (kWh)

V:

Ow

ners

hip

Car Van

� Vans - linearly correlated

� Cars – ownership rate

increases exponentially

� Car ownership begins only

at middle consumption level

Vehicle OwnershipVehicle Ownership

Summary:

�There is a good statistical fit between Ecu and

private vehicle ownership

14

�The ownership of different modes have

statistically different trends.

Ecu Vs Trip Rates

R2 = 0.98

R2 = 0.95

R2 = 0.76

R2 = 0.90

0.50

0.60

0.70

0.80

Pe

rso

n T

rip

Ra

te

Car

Van

Mo Bike

3W

Mode Based Trip Generation Mode Based Trip Generation

Van

3W

Car

15

0.00

0.10

0.20

0.30

0.40

60 80 100 120 140 160

Ecu (kWh)

Pe

rso

n T

rip

Ra

te

3W

M: Bike

Low Medium High

Mode Based Trip Generation Mode Based Trip Generation Ecu Vs Trip Rate

R2 = 0.75

R2 = 0.99

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

60 80 100 120 140 160 180

Pers

on T

rip R

ate

Bicycle

Walk

Bicycle

Walk

Ecu Vs Trip Rate

R2 = 0.43

R2 = 0.46

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

60 80 100 120 140 160 180

Pe

rso

n T

rip

Ra

te

Bus

Train

Train

Bus

16

60 80 100 120 140 160 180Ecu (kWh)

60 80 100 120 140 160 180Ecu (kWh)

Non Motorized:

� Trip rate reduces with

increased Ecu

� Walking trips are very

considerable than bicycles

trips

Public Transport:

� Trip Rate reduces at high

Ecu

� Train trip rates are very low

at all Ecu rates

ECU Vs Trip Generation (Totals) / Household

2.50

3.00

Mode Based TotalMode Based Total Trip Generation Trip Generation

Non Motorized, Private Motorized and Public TransportNon Motorized, Private Motorized and Public Transport

Characteristics:

� Significant Mode shift with

Ecu

17

R2 = 0.97

R2 = 0.99

R2 = 0.44

0.00

0.50

1.00

1.50

2.00

2.50

60 80 100 120 140 160 180

ECU (kWh)

V: O

wners

hip

Non Moto Pvt MotoPublic Modes TotalPoly. (Total)

Ecu

�Low consumers – non

motorized and public transport

� Middle consumers –

represented by all modes but

public transport dominates

� High consumers – Pvt:

motorized and public transport

ConclusionConclusion

Household electricity consumption (EcuEcu) can be

used as a surrogate variable for income for

forecasting:

Vehicle ownershipVehicle ownership

18

Vehicle ownershipVehicle ownership

Mode based trip generation Mode based trip generation

The methodology can be improved to have zonal

aggregate levelaggregate level traffic forecasting for small areas

(Gramaseva Division Zones)

Acknowledgement Acknowledgement

The authors wish to acknowledge with gratitude the

assistance extended by the Chairman of Lanka Electricity

Company Limited (LECO) and his staff by providing the

electricity related information. The data collection surveys

were financed as a short term research grant from the Senate

19

were financed as a short term research grant from the Senate

Research Fund, University of Moratuwa. This too is

acknowledged with gratitude by the authors.

Tissa U Liyanage and Amal S Kumarage

Thank You

Relationship between Cities / Town in Sri Lanka

SS

C – District capitals

U – Urban confluence

S - Satellite towns

20

Local Authority Boundary

C

U

U C

S

Source: Kumarage et al (1989)

Trip End Types of a Closed Small Community

S

S

CZone 1

Zone n

Zone 2

21

Closed Boundary

S

S

C

U

U

Zone 2

Zone 3

Zone 4

Population Data Available on GN

Division (Smallest Zone) Basis

Zone 4Zone n

Zone 2

Zone 5

22

Zone 2

Zone 1

Zone 4

Zone 6

Vehicle ownership Data Available on DS Division Basis

Zone 3

Zone 1

23

Zone 3

Zone 2

Electricity Consumption available on Provider’s own Grid Basis

Zone 2

24

Zone 2

Zone 2

CEBLECO

use of electricity consumption as an input for cost ......use of electricity consumption as an input...

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