Supply Chain Management Journal

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Supply Chain Management Journal

Improving Freight Operations and Planning by Applying Data Mining

Techniques Over Data Collected From Intelligent Transport Systems

Florin Codrut NEMTANU, Valentin IORDACHE, Angel Ciprian

CORMOSUniversity Politehnica of Bucharest

[email protected]

AbstractThese days, freight transport domain faces many challenges that need to be

addressed using appropriate data. A changing regulatory and technological environment forlogistics, energy, environmental and safety considerations, impacts on globalcompetitiveness, and the need to do more using fewer resources are some of thechallenges. With the adoption of just-in-time supply chain management solutions, andincreasing congestion on motorways, better knowledge of freight movements can serve toimprove freight operations and planning. Considering the current advanced IntelligentTransport Systems technologies and the large amount of high quality data available andimproved collection methodologies, data mining software will produce results that revealnew information about the freight transport system. Data mining can provide informationbeyond the use of general statistical analysis, the original databases being used to derivevarious rules and patterns which could be applied to support decision-making. Thisinformation will be the key for better-managed freight operations and long-range planningand forecasting.

Keywords: freight transport, data mining, intelligent transport systems,datasources

Introduction

Road freight transport plays a veryimportant role in the whole supply chain, andat some points the distribution of goods canbecome the bottleneck of it. IntelligentTransport Systems involve the application ofinformation and communication technologiesto the planning and operation of transportsystems(B. McQueen & J. McQueen,1999),making possible the efficient use ofresources,reduce environmental impacts and increase

safety and, last but not least, ITS systems,for many cases, ensure the achievement of acertain level of transport service (which, if notensured, it would make it useless). Currently,there are many organizations that storedhuge amounts of data generated over time.The availability of detailed data obtained fromITS systems opens the door to systematicevaluation of freight transportation systemperformance. Data mining methods ofanalysis are modern and powerful tools thatallow discovering in these data relationshipsand patterns that

characterize the

effectiveness offreight transport,thus occupying animportant place inany business. Theyhave the power ofpredicting potentialproblems based onarchived or real-time data, butbefore using them,the resultingmodels from thedata miningprocess must betried out andtested.

Data miningtechniques arewidely used inmany fields suchas financialanalysis,marketing, humanresources,

astronomy,business, fraud

detection, medical

and healthresearch andscientific discovery.In transportdomain they wereused in areas liketrafficmanagement,accident analysis,pavementmanagementsystems, travelforecasting,informationsystems or publictransport. Relatedto freighttransport, some ofthe benefits of datamining are:

- It speeds upthe processof dataanalysis.

1- It reveals

facts aboutcustomers.
http://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Fraudhttp://en.wikipedia.org/wiki/Fraudhttp://en.wikipedia.org/wiki/Fraudhttp://en.wikipedia.org/wiki/Marketinghttp://en.wikipedia.org/wiki/Marketing


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2- It analyzes and evaluates loadingpatterns.

3- It generatesroute-based

performancemeasures.

2011, Volume 2, Number 1 57


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- Itdetermines

thedistributionsche

dules.

- Itenhancesef

ficiencyandsavesm

oney.

Byanalyzing dataprovided fromITSsystem

s, likespeeds,speedpatterns,vehicleweights,

congestion andaccidentanalysis,volumes oftraffic,pavementconditions orweathercharacteristicsone cancreatemodelsthatsupportfreightdeliveryandplannin

g.Potentialapplicationsmayincludearrivaltime

prediction,scheduleadjustment,transitplannin

g andscheduling, realtimeservicemanagementandothers.

In orderto solveaspecificproblem, atfirst,onemustidentifywhatkind ofdata isavailable,

collectit, seewhatpart ofthatdata isuseful,extractit andtransform it soit canbe usedby thedataminingapplication.This iscalledsourcing andaggregation ofdata.

Fig

ur

e 1.Step


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softhe

datamining

process


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Data

sourcing Data

Delivery

of

and

exploration

personalized

aggregation

(DataMining)

information

henextstep is

choosingadataminingapplicationbasedondifferentcriterialikecomp

lexity,algorithmsincluded,easeofuse

and

lastbutn

otleast,thecos

t.Finally,thepatt

ernsandmodelsp

ro

ducedbyt

heapplicationwill

beverified,int

erpretedandtu

rn

edintoknowledge.

n thispaperwe

analyze thesourcesavailableforITSdata,recommendmethods for

choosingtheappropriateminingtechniqueandsoftware

toolandsuggestpotentialapplicationsin thecontext of freight

transportati

on.

1.D

atasourcing

andaggregati

on

Inorderto

ass

esstheim

pactofafreightm

anagementpro

gram,itisimporta

nt


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totakeintoaccountthe

effectsofalltypesofvehiclesandtheentiretransportationsystem.Th

atiswhy,whendevelopingfreightperformancemodels,thefreightoperator

ssho

uldconside

rusingdatafromI

ntelligentTran

sportSystems.Manytypesofda

ta

areavailab

leandfromdifferentsources,

anditistheminersresponsibility

to

integrate,select,filter,transformorforma

t thatdata.

TSsourcesuse amassivenumber of senso

rsembeddedin theinfrastructure, invehicles orinportabledevice

s,thatwill

generatevastamountso

fdataaboutloc

ation,route,l

ane,numberofv

eh

icles,length

andweightofthevehicle,date,ti

me,positionorspeed.CollectingI

TS


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datamethodshavebeen

evolvingconsiderablyfromtheuseoftraditionalon-roadsensorstoFloati

ngCarmethodswhicharemorecost-effectivesolutions.

hemostwidely

used

classoftraff

icsensorsisrepresentedbyloo

pdetectors,whicharesystems

em

beddedinr

oadwaysthatsensethepresen

ceofavehicleoverthem.Theya

re

usedtocountvehicles,determinetheirspeed

(iftwoconsecutiveloopsareusedon thesamelane)orclassifyvehiclesbasedonlengthandnumber of axles(alsousingmultip

leloops).Othersources forspeedmeasurearedigitaltachographswhichrecords avehicle'sspeedovertime,infraredsenso

rs andmicro

waveradar

technologywhichcandetectmo

vingvehicles.Laserscannersa

re

alsousedto

create3Dmodelsofvehicleswitht

hepurposeofclassification.

Weig

h-


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In-Motion isaneffectivetoo

lforweightenforcementandcollectionofdata

abouttruck

weights,speeds,a

xleconfigur

ationsandvolumesw

ithoutrequiringthevehicle tostop.Theyare

usuallybasedonbendingplate,loadcell orpiezoelectricalsensors.

ecauseofit

sabilitytotransm

itmo

vingimages

inaclosedcircuit

,

58

2

0

1

1

,

Volu

me

2,

N

u

m

b

e

r

1


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enablinghumanoperators indecision

making, videocamerasquicklyfoundapplication inthefield oftrafficsurveill

anceanddetection. Astechnologieshavedeveloped,imageanalysisbegan

to beautomatedbasedonelectronicprocessing of informationusing

differentalgorithms andtechniques.CCTV(ClosedCircuitTeleVision)trafficmonitor

ingsystem

s areused todetectcongestion, tonoticeaccidents, to

readthelicenseplatesof carsor toclassifyvehicles.

Radio-FrequencyIDentification(RFID)is atechnologythatusescommunicationthroughthe use

of radiowavestoexchange databetween areaderand anelectronic tagattached to anobject,for thepurposeofidentificationandtracking. RFIDtagscanincorpo

ratedata

abouttheowner,vehiclecharacteristics,thetype of

goodstransported,origin,destination etc.Newertagshavethepossibility toconnecttoexternalsensorsandcreatedatalogs.

TheGlobalPosition

ingSystem(GPS)is awell-knownspace-basedglobalnavigationsatellitesystem(GNSS)thatprovideslocationandtimeinformation.

Following the

development of


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cooperativesystems newmethods oftrafficdataacquisit

ionhaveemerged.Collecting"floating cardata"methodisbasedonvehicles thatserveasmobilesensors.Relevantinformation(speed

values,location,direction oftravel,activation of internalsystems -ABS,ESP - ,informationfromrainsensors,thermometer,etc.)areaggregated

andsent

anonymously totrafficmanagementcentersor othervehicles.

Roadsurfacecondition andweathersensorsareused todeterminetemper

ature,dry, wetor icecondition of theroad,thepresence ofsnow orrainandtheir

quantity, dewpointandrelativehumidity.

Most of the rawdatacollecte

d bythe ITSsystems andsensorspresentedaboveis neverused assuch.Usuallydata is

aggregated

overtime orused tocalculate otherindiceslikevolume(the

numberofvehiclesthatpassedoverthedetector duringaperiodoftime),occupancy(theaverageoccupancy of the


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detectorduringaperiodoftime),vehiclekilomet

erstraveled (VKT,theestimated totalkilometerstraveled byvehicles in asegmentduringa timeperiod),vehiclehourstraveled (VHT,theestimated total

hoursvehicles spenttravelling in asegmentduringa timeperiod),density(thenumberofvehiclesoccupying agivenlengthof alane orroadway a t a

particular

instant), traveltime ordelay(K.A.Tufte).

Othersources

(privateorpublic)thatcan beused inconjunctionwithITSdataare

usuallydatabaseswhichcanprovideinformationabout:

1- a

ccess tophysi

calfacilitiesneededforfreightmovement;

2- s

afetyofvehiclesandprevention oflossof ordamageto

products/

freightbeingdelivered;

3- capac

ity oftheroadtomovefreight atdesiredvolume/weightlevel;

4- t

raveltime(dwelltime,processing

timeandtransittime);

5- tonnageandvalue of

shipments todifferentmarkets;

6- freightproductivity

(annual


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kilometerspertruck);

7- s

hipment

rates;

8- pricing;

9

- infras

tructure,adm

inistrative,enforcement

cos

t;

10-

eco

n

o

m

ic,

lo

gi

sti

cs

,

b

us

in

es

s,

re

g

ulat

or

y

co

st

s

in

cu

rr

ed

by

carri

er

s;

11- environmental,energy,social,safetycosts;

12- e

conomicanddemographicfactors

(trade

agreements,economy,inflation,fuelprice

s,andlogisticalfactorchanges).

Thesedatabases arecreatedeitherby thefreightoperator itselfor byentitieslikedepartmentsoftranspo

rt, datacollectionagencies,transportationresearchboards,committees onfreighttransportation,whoidentifyandpublicizesourcesof dataandneedsof data,

assistanalysts and


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decisionmakersin theeffective useoffreighttranspo

rt data.T

heaggregation ofdata isthemostdifficult

andtimeconsumingelementof thedataminingprocess

. Theminerhas tointegrateinformationfromseveral

sources, tochoosewhatdata isrelevant; onemightneed to

transform it inasuitableform, todealwithmissingvalues,

20

11,

Vo

lu

me 2, Number

1 59


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and toremoveduplicate orerroneousrecords

andextremevalues.

2.Data

exploration

Everyone has averydifficulttaskwhen itcomestochoosing whatdata

miningsoftware touse.Thisalsoappliesfor thefreightmanagersinvolved in

thisprocess

. Therightchoicemustmeetuserneedsand its

budget.Therefore, twomajorproblemsarise:the firstone istoselecttheproperdataminingtechnique thatcan beappliedaccording tothetype ofdataavailabl

e, andsecondischoosing of thesoftware itself.

Todetermine thepropertechnique, onehas tounderstand thedatasources, dataparameters(numerical,logical,

Booleanetc.)

andqualityof data.Databases canberelational,

spatial,temporal,deductive,objectoriented,transactional,textualandmanymore.

Techniquessuch as

clustering,

classification,

association,

numeric

prediction,neural

networks,

genetic

algorithms andothersareavailabl

e. In(M.F.

Jiang etal,1999)theauthorsillustrate therelationshipbetween sixtypes ofdataminingtechniques andmostcommon four types of databases:

-

transactional

databases

can

be

analyzed

using

association

rules

techniqu

e;- relation databases c b


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alanalyzedclassificationtechniques;

1- t

extualdatabas

escanbeana

lyzedusing

patternbased

s

imilarity

searchtechnique;

2- t

emporal

databasescan

beanalyzed

us

ingpatte

rnbasedsimilaritysearch

ortimeseriesdiscoveryt

ec


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hniques.

N

ext weprovidesomeexamples of appliedtechniquestakenfromavailable

literaturerelatedtotransportdomain.

Theclassificationtechniq

ue isused tomine adatabase withlogicalandnumericalvaluescontaininginformationaboutasphaltprojects tofindrulesandpatterns thatwillsupport

decision

makingwithin apavementmanagementsystem(K.

Nassar,

2007).

Whensearching in adatabase withstatistics ofacciden

ts thatcontainsgroupsofattributes,associationrulestechnique canbe usedtodiscovercommoncombinationsofattributes thatoccurmost

frequentlywithin agivendata set(P.Haluzova,2008).

In (D.H.Lee etal.,2004)aninvestigation oftrafficincidentsituations isdone byusing arelationfinder

algorithm and


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clusteringtechniquesover adatabase withnumerical

recordsgrouped intocategories.

Identification ofrear-endcrashpattern

s oninstrumentedfreeways isanalyzed in (A.Pande& M.

Abdel-Aty,2005)over a

trafficsurveillancedatabaseusingneuralnetworkstechniques.

In (R.E.Turochy &B.D.Pierce,2004)theauthorsminedanarchiveoftraffic

datafor a

short-termtrafficforecastingusing aform ofnonparametric

regressionmethodcalledk-NN(NearestNeighbor).

As seenfrom

above,nocertaintechnique canberecommendedandeveryminerinfreight

transportdomainshouldestablish fromthestartwhattechnique issuitablefor usewith hisdataset.Whenone isnotsureaboutthat hemustchoosea

software that

uses avarietyof dataminingtechniques,although thiscould

be anexpensivechoice.

In (K.Collieret al.,1999)theauthorspropose

d amethodologyforselecting fromamongtheassortment of commerciallyavailabl

e dataminingsoftware tools.Theysuggestfourcategories of criteriaforevaluating dataminingtools:performance,functionality,usability, andsupportofancillary

activities.


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Takingintoaccountthedomainwe arediscussing inthis

paperweselected in thefollowing someof themostimportantcriteria:

1-Platformvariety.Itisk

nownthattheplatfor

ms

olutionsadopte

dinthetransportsectorarediverseandsome

whatindividualistic(proprie

tary).

2- Het

erogeneousDat

aAccess. Itisnecessarytocover alarg

evarietyofdatabases,becausedata is

obtainedusingdifferenttechnologies,

different

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methodsandmay

havedifferentstructures.

- Datasize.

Itis a

very

importantcriterionbec

ause

inthe

transportdomain

data

set

stendtobeverylarge.

1- Effi

cie

ncy.

Thesoftwaremustproduc

eresultsin areasonableamount oftime,workingwithmultipledatabasetyp

es,usingalltheinformationfromthemwithoutmakingcontradictoryoruni

ntelligible

results.

2- Rob

ustness.The

softwaremustbestable,withoutcra

shingregularlyandespeciallyinanalysis

thatrequirelongworkingtimes.

3- Alg

orithmicvariety.It isaveryimportantcrit

erion if


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thenecessaryalgorithmsare

known.Ifnot,thesoftwareshouldhaveasmanyalgorithmsimplemented

aspossible.

4- Mo

delvalidation. Itis areq

uiredcharacteristicofthesoftwarebecaus

eobt

ainedmodelshave tobevali

datedbeforebeingdeployedintoaworkingenvironment.

5- Dat

acleansingandDat

afiltering. Itisanimportantcharacteris

ticthatallowstheuser toworkwiththe

values

ofthedataset.

6- Dat

avisuali

zation.Thesoftwareshouldreporttheres

ultsin avarietyofgraphicalmethods.

7- Error

reporting.Shouldbeeasy touseandhelp

theuser.

8- Use

rinterface.Ithastobeeas

y tonav


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igateanduncomplicated

bothfordataaggregationstepandforvisualizationoftheresults, apoor

interfacecanleadtomistakesandmisinterpretationsoftheanalysis.

9- Cos

t.

To

choosetheappropriatesoftware foreachcriterion

consideredimportant aweightaccording toitsimportance willbeassigned. Theneachsoftware toolwill bescoredforcomparison,makingitpossibleto find

thesuitableone.

Variousdataminingtoolcomparisonsandevaluationsweremadeovertime.(J.F.ElderIV &D.W.

Abbott,1998)andKDNug

getswebsite

madesomeofthem.Butusingtheseevaluations isnot

alwaysthebestideabecausesoftware toolsarerapidlyevolving,incorporating


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newcharacteristicsandtechniques.Everyonemust

complete theirowncomparisonstudythrough whichtochoosetheappropriatetool.Goodhelp isgivenbyorganizationsthatrunsurveysamongthe

miningtoolusersandcreatepopularityreports. If asoftware toolisextensivelyused,thiscouldbe anindication thatit willprobably getgoodscores

onmany

evaluationcriteria.Themostrecentsurveyis madeby

RexerAnalytics (K.Rexer,2010).

3.Applicationsinfreighttransportationplanning.

Thediscoveredknowledgefrom

thedataminingprocesswill beevaluated andinterpreted andaccording to[P.Haluzova,2008]therearethreecategories of thediscoveredknowledge:

- Evi

dentknowledge,whichis

thecommonknowledgeofanexpert.Even ifitdoesnotofferanythingnew,it

canshowusthatthemethodappliedisworkingwell.

-

Interestingknowledge

that


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yieldsanewpointofvie

w.Andthisisthemainaimofprocess.

1- Kn

owledgethatseemstobeunc

lear orisatvariancewithexpertkno

wledge.Usuallythistypeofknowledg

esho

uldbeeliminated,butatso

metimesmustbetakenintoconsiderationbecause itcanexposeanewpointof

viewthatappliestoalltheproblems.

In (R.Kohavi,2001)theauthorstatesthatdataminingservestwogoals:

insightwhich is

identifyingpatterns andtrendthat areeasy tounderstand and

use,andpredictionwhichmeansbuildinga modelthat willpredictbasedoninputdata.Eachdataminingapplication intransportengineering willhave toreach

one of thesetwogoals.

In (J.Patnaiket al.,2006)theauthorsstudiedthedevelopment ofaneffective busscheduling planusingdataminingtechniques on

APCdata


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(AutomaticPassengerCounters)applying amethod

ologyfor

clustering thestatevariables (thenumberofservedpassen

gersand

haltingstationsin eachvehicletrip)andusingthat forservice

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11,

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planning. Toanalyzethedata,extractit and

determinedemandpatterns dataminingtechniques likeclassificationand

regressiontreesandhierarchicalclusteringwereused.Thesameidea

may beappliedwhen itcomestogoodsdistributionwithregularschedules,

usingdatafromRFIDtags orotheridentificationinstruments.

Thetraffic

generated by

heavyfreightvehiclesis onecausefordisruptions of

thetrafficflowand theappearance of congestion.One ofthesolutions to thisproblem thatleads toimproved trafficflow isformation of vehicleplatoons.Truckscan

becomelargelyself-drivenwiththehelp ofAdvancedDriverAssistantSystems(ADAS)whichallowsautomaticallyformingof closeplatoons inorder toreduce

airdrag,fuel

consumptionand thespaceneededbytruckson the

road(E.Savelsberg,2008;P.Meisenet al.,2008).ADASenablestruckstoadaptthespeedto thetrafficenvironmentandfollowthetruck infront at

closedistance. Byusingrevolutionaryad-hocnetworks truckscouldcommunicatebydynamicallyformingcommunicationnetworks,whereeachvehicleisdirectly

linkedto itsnearest


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neighbors orto theintelligentinfrastructure.Benefits are

numerous. Thedistancebetweentrucksin aplatooncan bereduced whenusingwirelesscommunicationwiththetruck infrontbecause o f afasterrespons

e of theinvolvedsystems.Truckscancommunicatewitheachotherandformplatoons basedoncertaincharacteristics(destination,route,weightper

horsepower or

power-to-weightratio).Bycommunicatingwiththe

intelligentinfrastructurethisdatacan besentalso toa trafficmanagementcenterto buildadatabase thatcan beminedin ordertodiscoverpatterns and

predictivemodelsthatcan beused byfreightplanners (C.H.Cheong& M.H.Wong,2006).Theycanchooseacertainrouteand acertaintimewindowinwhich

there isa

biggerprobability tofindsimilartrucksandcreateplatoon

s.C

omplexica Inc.developed asystemcalledMINDSthatanalyzes data

from anarray ofin-vehiclesensors, to


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determinewhentruckdriversare indangeroffalling

asleep,usingadvancedneuralnetworktechnology.Manyothersystems thatdetectthedrowsiness of adriverareemerging orarealreadydeployed. The

informationprovided bysuchsystems canbeused inconjunctionwithdatarelatedto theroutetraveled bythedriver,speedprofile,weatherconditio

ns,time of

day(hour,day/night) tofindpatternscorresponding

to eachdriver,andadaptdrivers'workinghoursaccordingly,thusincreasingsafetybypreventingaccidents.

Amore

rationaluse of resources canreduce

thenumberoffreightvehicleson theroadandincrease theefficiency of freighttransportation.Similartoalreadyexistingcar-sharingservices, truck-sharingcould

beimplem

ented.Differentcompanies maysharethesamevehicles

todelivergoods iftheyhavesimilarroutes,to avoidsituations inwhichvehiclesare notfullyloaded,or toshortenthedistributiontimeandavoidpeakhours.

Aninteresting taskis tomatchuserprofilesbasedonvariousinformationtypesbut thiscan besolvedreliablyusingpatternanalysistechniques. Foreachvehicleand

eachfreight


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operator a userprofilemustbecreatedcontainingprefere

ncesliketype ofgoods,weight,volume, alongwithspecificationsconcerning the

journeyitself.

Thevehicleand thedrivercan bemonitoredusingin-vehicle

sensors, mapandtrajectoryinformationcan begathered withthehelp of

GPS,storinga userprofileon a

centralserver.Withsensornetworks alongtheroadmeasuri

ngtrafficflowandroadcondition,freightmanagers canrunpatternanalysistasks toobtaininformationaboutthetraveltime,fromdoor todoor,includin

gparkingandwaiting,and thecostneededtofollow acertainrouteandpredicttrafficloads inthefuture,

takingintoaccountspecialevents(suchasgames,festival

s orholidays), aswell asweatherforecasts,plannedroadmaintenanceand soon. Up-to-datetime-of-day andday-of-weektrafficstatistics wouldallowfreightoperators to

routetheirvehiclesas toavoidtrafficcongestion andbetterservetheircustomer. Onthelogisticlevel,the

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