Issues in Urban Travel Demand Modelling : ICT Implications and Trip timing choice

ICT Implications and Trip Timing Choice

Maria Börjesson

Doctoral Thesis inInfrastructure

withspecialisation inTransport andLocation Analysis, August2006

DivisionofTransportandLocationAnalysis

DepartmentofTransportandEconomics

RoyalInstituteofTechnology

ISSN1653-4468

ISBN 13: 978-91-85539-04-8

ISBN 10: 91-85539-04-X

Akademisk avhandling som med tillstånd av Kungliga Tekniska Högskolani Stockholm

framläggestill oentliggranskningför avläggandeavteknologie doktorsexamenfredagen

den 29september2006,KungligaTekniskaHögskolan,Stockholm

c

cations and Trip Timing Choice. Department of Transport and Economics,

KTH, Stockholm. ISBN10: 91-85539-04-X.

Abstract

Travel demand forecasting is essential for many decisions, such as

infrastructure investments and policy measures. Traditionally travel

demand modelling has considered trip frequency, mode, destination

and route choice. This thesis considers two other choice dimensions,

hypothesised to have implicationsfor travel demand forecasting. The

rstpartinvestigateshowtheincreasedpossibilitiestoovercomespace

that ICT (information and communication technology) provides, can

beintegratedintraveldemandforecastingmodels. Wendthat

possi-bilitiesofmodellingsubstitutioneectsarelimited,irrespectiveofdata

source and modelling approach. Telecommuting explains, however, a

very smallpart ofvariation inworktrip frequency. Itis thereforenot

urgenttoincludeeectsfromtelecommutingintraveldemand

forecast-ing. Theresults indicate thattelecommuting isa privilege forcertain

groups of employees, and we thereforeexpectthat negative attitudes

from management, job suitability and lack of equipment are

impor-tant obstacles. We nd also that company benets can be obtained

from telecommuting. No evidences that telecommuting gives rise to

urbansprawlis,however,found. Hence,thereisgroundforpromoting

telecommuting froma societal, individualand company perspective.

Thesecondpartdevelopsadeparturetimechoicemodelinamixed

logit framework. This model explains how travellers trade-o travel

time, travel time variability, monetary and scheduling costs, when

choosing departure time. We explicitly account for correlationin

un-observed heterogeneity over repeated SP choices, which was

funda-mental for accurate estimation ofthe substitution pattern. Temporal

constraints at destination are found to mainly restrict late arrival.

Constraints at origin mainly restrict early departure. Sensitivity to

traveltimeuncertaintydependsontriptypeandintendedarrivaltime.

Given appropriate input data and a calibrated dynamic assignment

model, the modelcan beapplied to forecast peak-spreading eects in

congested networks. Combined stated preference (SP) and revealed

preference (RP) data is used, which has provided an opportunity to

compare observed andstated behaviour. Such analysishaspreviously

notbeencarriedoutandindicatesthattherearesystematicdierences

inRP andSP data.

Keywords: Travel Demand, ICT, Tele-substitution, Revealed

Pref-erence, Stated Preference, Mixed Logit, Unobserved Heterogeneity,

In research, as in most human activities, interdependence certainly is more

valuable than independence. This thesis is the result of ve years of work

through which I have been accompanied and supported by others who have

added to this work indierentways.

First,IwouldliketothankmysupervisorStaanAlgers,atrulygenerous

and considerate person with a refreshing sense of humour. He has very

unselshly shared his ideas, knowledge and experiences with me during the

past years. Thanks alsotoLars-Göran Mattsson,head of the department of

Transport and Economics, for always having kept an eye on my work and

supporting mewith valuable commentsand advice.

I would further like to thank all members of the SILVESTER project

team, Leonid Engelson, Andrew Daly, Staan Algers, Jonas Eliasson and

Fredrik Davidson. They have substantially contributed to the development

of the second part of this thesis. I am particularly grateful to the project

leader Leonid Engelson for careful reading and valuable comments on the

work. Leonidhasalsobeen veryencouragingand helped metostructure the

work. ExtensivecommentsfromanddiscussionswithAndrewDalyhavehad

adirectimpactonthenalformandquality ofthesecondpartof thethesis.

Jonas Eliasson has contributed to the SP design and to inspiring, amusing

and interesting discussions. I would also like to thank Pia Koskenoja for

valuablecomments inthe nal seminar.

Iamgratefultomyfriends andcollegesatTLAfor allgenerous support.

I will miss them all. I want to thank my friend and former college Muriel

Besser Hugosson in particular. Her wise, cheerful and generous coaching in

researchaswellasmorepersonalmattershavemadetheseyearseasier,more

ecient and a lotmore fun.

IfurtherwanttoacknowledgetheSwedishAgencyforInnovationSystems

and the Swedish Road Administration forhaving provided necessary funds.

ToLisa and Peter, thank youfor always helpingme out in critical

situa-tions; you have been wonderful during this lastphase. Finally,I would like

to thank my darlingsKalle, Malte and Johan for all moral support,

encour-agement, understanding and love.

Stockholm, August 2006

1 Introduction and Scope of the Thesis 1

2 Research Context 3

2.1 ICT and Travel

A Backward Glanceand WiderPerspectives . . . 3

2.2 DepartureTimeChoiceModellingand TravelTimeUncertainty 5

3 Dierent Approaches in Travel Demand Modelling 9

4 Methods and Data 11

4.1 Methods . . . 11

4.2 Data . . . 13

5 Results and Discussion 17

5.1 Paper I, 'A Communication Choice Model' . . . 17

5.2 Paper II, 'Telecommuting and work travel demand modelling

in Sweden' . . . 18

5.3 PaperIII,'CompanyIncentivesandToolsforPromotingT

elecom-muting' . . . 19

5.4 PaperIV,'DepartureTimeModelling: ApplicabilityandTravel

Time Uncertainty. ' . . . 20

5.5 Paper V, 'Joint RP-SP Data in Mixed LogitAnalysis of Trip

TimingDecisions' . . . 22

6 What Have the Findings Brought to Travel Demand

Mod-elling? 25

(I) Börjesson, M., 2003. A Communication Choice Model.

(II)Börjesson, M., 2003. Telecommutingandwork traveldemand modelling

in Sweden.

(III) Robèrt, M., Börjesson, M., 2006. Company Incentives and Tools for

PromotingTelecommuting. Environment and Behavior, 38(4).

(IV) Börjesson, M., 2006. Departure Time Modelling: Applicability and

Travel Time Uncertainty.

(V) Börjesson, M.,2006. JointRP-SP Datain MixedLogitAnalysisof Trip

Urban regionsthroughoutthe world arestrugglingwithfast growingcar use

and increasing congestion as a consequence. Foreconomical, environmental

and socialreasons, and considering the large amount of latent demand, new

physical infrastructurecannot alonesolvethese problems. Society is, onthe

otherhand,dependingonhighaccessibilityinallsegmentsofthepopulation,

particularly to labour markets. This development underscores the need for

policy innovations and eective utilization of new technology, which is an

importantchallenge for modern travel behavioural modelling.

Thisthesis addressestwoissues,suggested aspotentialsolutionstothese

problems. The objective of the rst two papers I - II is to investigate if

and howthe increasedpossibilitiestoovercomespace thatICT(information

andcommunicationtechnology)provides,canbeintegratedinexistingtravel

demand forecast models. Another important objective is to investigatehow

a new data source, the Communication Survey (SIKA 2002), can be used

to improve modelling with regard to ICT. KOM is a one-day travel and

communication diary survey, carried out on a regular basis, on commission

oftheSwedishInstituteforTransportandCommunicationsAnalysis(SIKA).

Paper I, 'A Communication Choice Model', focuses on substitution

ef-fects of postal and banking activities. Paper II, 'Telecommuting and work

travel demand modelling inSweden', investigateshow telecommuting could

be implemented in conventional travel demand models. Specically, it aims

at forecasting future commuting frequency. Paper III, 'Company Incentives

and ToolsforPromotingTelecommuting',alsoconsiders telecommuting,but

is more policy oriented. It aims at identifying company incentives for

pro-moting telecommutingto reducecommuting.

The second part of the thesis, paper IV - V, develops a departure time

choice model. Thismodelexplainshowtravellerstradeotraveltime,travel

time variability, monetary and scheduling costs, when choosing departure

time. Thisworkwaspart ofalargerprojectcalledSILVESTER (Simulation

of choice between starting times and routes), which aims to develop an

ap-plication to forecast peak-spreading eects in Stockholm. SILVESTER was

largely triggered by the tollstrial inStockhom, taking place January- July

2006. SPand RPdata, usedto estimatedthe demand model, were collected

within theSILVESTERproject. The demand modelpresented inthis thesis

willnowbeimplementedwithacalibrateddynamicassignmentmodelwithin

the SILVESTER project. Eects frominfrastructureinvestments andpolicy

measures can then be calculated usingthis application.

InpaperIV,'DepartureTimeModelling: ApplicabilityandTravelTime

mation. However, there are certain downsides using stated preference data,

as opposed to revealed preference data (RP), describing travellers' actual

choices. A common feature of SP data is that response scale is distorted.

Paper V, 'Joint RP-SP Data in Mixed Logit Analysis of Trip Timing

Deci-sions', therefore extends the analysis in paper IV by estimating the model

2.1 ICT and Travel

A Backward Glance and Wider Perspectives

Thediscussionaboutwhethertelecommunicationssubstitutepassenger

trans-portationhasbeenappearingintransportliteraturesincethe1970s.

Accord-ingtoMokhtarian(1998),theoilcrisiswasthestartofitall,whichcoincided

with the startof theinformationera. One ofthe leadingideas wasthatICT

would create the 'death of distance' in a longer perspective, i.e. that the

spatial distance between home, work and stores etc., would play a less

im-portant role for activity participation. In this context redenitions of the

concept accessibilityhave frequently been discussed, see Shen (2000).

Thisrevolutionaryperspective wasverymuchanoutcome ofthe

fascina-tion ofthe tremendoustechnologicaldevelopment. This resultedina

simpli-ed and technologyfocusedway oflookingathuman activities. Mostly very

specic and direct eects from telecommunication services were discussed

and analyzed withthe prospects that thesecould be asolutiontothe

trans-portationproblems. Theinteractionwasoftencategorizedasby Mokhtarian

and Meenakshisundaram (1999):

Substitution: Telecommunicationsreplace travel.

Generation: Telecommunications stimulate or complement travel, for

in-stance by increasing access to information.

Modication: Telecommunicationsneithergeneratenorsubstitutetravel,but

change the travel pattern, for instance by time, mode orroute.

Neutrality: The use of telecommunications and travel is not inuenced by

each other.

Indirect(orrebound) eects: The eectsdescribedaboveinuencethe travel

demand and land use in a longer perspective. This may give rise to more

spread out residential locationsand subsequent eects onlocal and regional

travel patterns and mode choices.

Numerousstudieswere carriedoutwiththeobjectivetodeterminewhich

of these eects that were the most dominant. However, very little

clear-cut results came out of these. Mokhtarian and Meenakshisundaram also

note that many of these studies were short-term, and therefore tended to

overestimate substitution eect and underestimate the more long-term and

complex generation eects. When correlation between amount of transport

and telecommunications was found, which was the case in many studies,

chair studies', whichmainlyexposedideas forfutureresearch. Thesewere in

general verytechnology focused, and Salomonemphasises that these should

not beconfusedwithforecasts. OneexampleisGrübler (1989),whoshowed

howdierenttechnologieshaveemergedandvanishedinS-shapessinceabout

1800, with increasing frequency. He argued that the length of canals,

rail-roads and surface roads, relative to their saturation level, all followed a

S-shapedpattern. Grüblerthenarguedthatthisdevelopmentpatternis

gener-ally applicable whenforecasting penetration patterns of modern technology,

which is just what Salomon objected to. Mattsson and Höjer and

Matts-son (2000)alsocriticizedGrüblerforthisreason,andfurtherquestioned the

quality of the data.

It is now clear that ICT impacts on the transportation system reach far

beyond and are far more complex than the ones most frequently addressed

in literature. The progressive information and communication technology

development has changed, and will in the forthcoming decades continue to

change, organisation of work and rms (Castells 1996), education, shopping

and entrainment industries and land use patterns etc. This reformation

oc-curs in various spheres and levels of society and has thereby a fundamental

impact onconsumers' behaviour.

On the other hand, analyzing very specic behavioural adaptations like

the ones listedabove,inresponse tospecictechnologicalinnovations has in

general not been very successful. These studies have also become

substan-tially fewer. The limitations of them are primarily related to two factors.

First,itseemsthatICTprimarilyacts atthetravelpatternlevel,incontrast

tothenumberoftrips. Importantimpactsarepresumablysucheectsastrip

chaining,destinationchoice,departuretimechoiceandmeetingsarranged at

shortnoticeinuencingtravelplanning. In alongerperspectivealsolanduse

and mode choice may be aected. Deriving causal relationships and

calcu-lating the 'net'eect of allthese eectsis noteasily done. Second, there are

many factors that simultaneously act on travel patterns, inaddition to and

jointlywithinformationtechnology. Such factorsare, forinstance, economic

growth, environmentalawareness, work situationsand policymeasures, etc.

Inthisperspectiveitisnot onlydicultbut alsolessmeaningfultocalculate

very specic impactsfrom informationtechnology.

To exemplify, at rst glance the substitution eect from

teleconferenc-ing is obvious. On the other hand, this contact (trip or communication)

might never been taken if society had been less information and

technol-ogy dependent and globalized. These kindsof discussions become, however,

highlyhypotheticalandthusnot veryinteresting. Anotherexampleisurban

urban sprawl. However, also avariety of other factors rule this process, e.g.

decreasing travel times and economical growth, and it is very diculty to

assess the eects of these factors isolatedfrom eachother.

2.2 Departure Time Choice Modelling and Travel Time

Uncertainty

Trip timing choice has received increased interest as a consequence of the

time-of-day varying tolls trialin Stockholm (Jan-July 2006). It is expected

that driver's preferences for dierent departure times are an important

di-mension for policy evaluation in congested regions. In aninternational

per-spective there is quitesubstantialevidence of peak spreading eects (Porter

etal.1995). Tracowsinpeakhourhavegrownlessthanthoseinthe

over-allpeakperiodandpeakperiodtracvolumeshavegrownless thanthosein

the o-peak. Thereisalsothe reverseofpeakspreading, peakconcentration,

resulting fromcapacity expansion. In Stockholm, this was manifestedwhen

the capacity of the most essential motorway was increased, with subsequent

increase of peakiness of the trac ow.

Travel demand modelling and forecasting has traditionally been based

upon a four dimensional model, includingthe frequency, destination, mode

and route choice dimensions. The three former choice dimensions are

nor-mallyuniedunder theconcept demand modelling. Given level-of-service in

the trac system (travel times and cost, etc.), a demand model computes

trac volumes ineach originand destinationrelation. A routechoice model

assignsthexed demandtothe roadnetwork andcalculatestraveltimesand

cost, etc. Assignment models have traditionallybeen based on steady-state

equilibrium trac ows and thus lack the time dimension. Since demand

diers largely between on and o peak, trac is often modelled separately

for peak and o peak conditions, and the distribution of demand between

peakando-peakhours isusuallyxed. Peak-spreading eectscan thusnot

bestudied by this approach.

Fewexistinglarge-scale modellingapplicationsintheworlddeal withthe

trip timingdimensionoftravellers'behaviour. Suchanapplicationhasto

in-tegrateadynamicassignmentmodelwithabehaviouralmodelthatdescribes

individuals'responsestonewtravelconditions. Dynamicassignmentmodels

dier from steady-state assignment models in the possibility of modelling

temporalvariationsinows and traveltimeswithin apeakperiod,resulting

from queues buildingup and dissolving.

(1982). However, the work by Vickrey (1969), who originated much of the

conceptually frameworkof subsequent studies, consideredboth demand and

supply inacontinuousanddeterministicframework. This,rathertheoretical

contribution, appliedasinglelinkbottleneckmodel. Focusingmainlyon

op-timaltolling schemes, this work was further developed ina series of articles

by Arnott, de Palma and Lindsey (1998). Hyman (1997) and van Vuren et

al. (1999) also further developed the work by Vickery, and named this

ap-proach "equilibriumschedulingtheory". Buildingonthis theory, a software

product was developed by Hague Consulting Group (1999), in which the

demand model was integrated with the dynamic assignment models

CON-TRAM, SATURN or TRIPS.

Theutilityfunctiondeveloped byVickeryand Smallisbasedontradeo

between traveltime and shiftfrom preferredarrival(or departure)time. As

notedby Smallandmanyotherauthors, the MNLapproachisinappropriate

for departure time modelling (section 4.1 discusses dierent model types

in more detail). This relates to the obvious ordering of consecutive time

intervalsinatriptimingmodel,whichinducesaspeciccorrelationstructure.

The MNL model cannot accommodate a correlation structure among the

alternatives.

Several studies on departure time choice have therefore used more

com-plex model structures. de Jong et al. (2003) and Hess (2005) estimated

error component (mixed) logit models, designed to induce correlation and

heteroskedasticity between departure time intervals. Alternatives with

de-parture times closer to each other are assumed to share more unobserved

attributes than more distant alternatives. The models also include a mode

choice dimension. Estimationthusindicatesthe relativesensitivityof

depar-ture time andmode choice shift,resultingfromchangesingeneralised travel

costs. They nd that, unless the time shifts considered are very large, the

departure time choice is normally more sensitive to travel cost than modal

choice.

Thereis alsoanother class of choice models,GEV models, which can

ac-commodate a exible correlation structure, but still has closed form choice

probabilities. In particular Ordered GEV (OGEV) modelshould beapplied

in cases where there is a natural ordering of the alternatives, and is thus

well suited fordeparture time choice. This modelwas rst appliedby Small

(1987), to an arrival time choice model. Bhat (1998a) jointly modelled

de-parture time and modelchoice applying acombined OGEV-MNL model.

Triptimingmodels inthe literature haveused eitherSP orRP data. No

previous published study has, however, used joint RP and SP data. There

These have, as virtually all choice models including travel time variability,

used SPdata. The exception is Lamand Small (2001), who have used data

from a HOT lane 1

project in California,in order to estimatewillingness to

payforreductionintraveltimevariability. Thisstudy doesnot, ontheother

hand, consider departure time choice.

There are two dierent approaches in previous studies on travel time

uncertainty modelling,the mean-variance approach and the endogenous

ex-pected scheduling approach. The former argues that the disutility of travel

time variation arises from the diculty of planning the day because of not

knowing exactly when to arrive. This disutility could be captured directly

by aseparate variablesuchasthe standard deviationoftraveltimeor

some-thing equivalent. The latter approach assumes that the cost of travel time

uncertaintyarisefromtheriskofarrivingatatimedierentfromthedesired

one and should be represented by the expected value of the schedulingcosts

variables(equallingthe arrivaltime shiftearlier/later fromthe preferred

ar-rival). The theory of this approachwasrst proposed by Garver(1968) and

Polak (1987). Noland and Small (1995) then further developed the theory,

combining itwith the workof Small (1982).

AnexampleinSmalletal.(2000)illustrateshowtheexpectedscheduling

delayvariablescapturethedisutilityarisingfromtherisk ofarrivingatatime

dierent from the preferred arrival time (

P AT

). They describe a situation wherevepossiblearrivaltimes(

AT

)aregiven, intermsof shiftfrom

P AT

. These time shifts are -7, -4, -1, 5 and 9, where early arrival is coded as a

negativenumber. Assumingthesame probabilityforthese arrivaltimes, the

expected schedulingcosts are:

E(SDE) = E(max[P AT − AT, 0])) = (7 + 4 + 1 + 0 + 0)/5 = 2.4

(1)

E(SDL) = E(max[AT − P AT, 0])) = (0 + 0 + 0 + 5 + 9)/5 = 2.8

(2) This shows that the averages values of both

E(SDE)

and

E(SDL)

are positive, although the arrival time is either late or early. This is a

conse-quence of the uncertain travel time and ofthe fact that expected scheduling

costs arenon-linear functionsoftraveltime. This explainswhythe expected

scheduling variablescapture some of the disutilityarising fromit.

Sincepreferredarrivaltimeisrequired forcomputingthe schedulingcost

variables,thisapproachhasmostlybeenappliedinassociationwithtrip

tim-ingchoicemodelling. Therearerelativelyfewexamplesofstudiescomparing

1

A set of express lanes on an otherwise free and congested road oers high-quality

provide alinkbetween theseapproaches by theoreticallyshowing that if the

mean travel time is independent on time-of-day (which, on the other hand,

inunlikelyinacongested network)andnodummyvariableforlate arrivalis

included (a discrete arrivinglate penalty), both approaches are equivalent.

Thediscussionabout ofhowtraveltimevariabilityshouldberepresented

in choice models, partly originates from the fact that perception and

be-havioural response to uncertain travel times is still relatively unexplored.

Brownstone and Small (2005) review studies on value of time and value of

reliability using RP and SP data from two HOT lane projects in southern

California. These studies found that standard deviation captures

individ-ual's preferences only imperfectly according to the RP data, and that the

dierence between the 90th and the 50th percentile gave a better model t

(see for instance Lam and Small (2001)). Brownstone and Small suggest

that a likely reason for this is that driver's preferences are not symmetric.

That is, the chance of a shorter travel time does not inuence the driver's

preferences as much as the chance of a longer travel time. The dierence

between the 90th and the 50th percentile is abetter indicator of the chance

of being considerablydelayed, ascompared tothe standard deviation,which

might better reect what travellers actually care about. Brownstone and

Small also suggest that the poor results obtained from using the standard

deviation may arise fromincorrect measurements.

For an extensive review of travel time uncertainty and departure time

choice, see Bates et al. (2001), Noland and Polak (2002) and de Jong et al.

elling

The trend of transportation modelling is obvious. It is becoming

increas-ingly disaggregate in various directions, e.g. in the analysis of individual

behavioural responses and its underlying mechanisms, as well as in spatial

and temporal resolution. During recent years, much attention has been put

on activity-based research in the eld of travel demand, as a way to better

understand the underlying mechanisms of travel behaviour.

Activity-based approaches are characterized by two key ideas. First,

travel is a derived demand, arising from the need or wish to participate in

activities withinatime andspace continuum. This standsincontrast tothe

conventional model approach, in which the trip itself is the focus. Second,

activity-basedresearchemphasises thetemporalandspatialpossibilitiesand

constraints as well as the context in which individuals or households make

theirdecisions. Travelisassumedtocausedisutilityintermsofthemonetary

and time resources it requires. It occurs therefore only when the net utility

ofanactivityperformedatadistantlocation,andthetripthatisrequiredto

gothere,exceedsthe utilityoftheperformance oftheactivityatthe original

location.

The family of activity-based models includes a set of approaches and

methods rather than a model per se. These have been developed with a

varying degree of detail. Many of the eorts to implement activity-based

modelshaveappliedmicrosimulation,whichbynatureishighlydisaggregated

in that eachagent ismodelled individually. (Millerand Salvini2002)

Since the hypothesised implications of ICT-based services concern the

scheduling possibilities and relaxed constraints in time and space, it has

often been argued that activity based modelling isbettersuited for analysis

of this process. The work in this thesis starts, however, out from the more

conventionalmethodswherethetripisinfocus. Thisismotivatedforseveral

reasons. IncludingimplicationsfromICTinanykindofactivity-basedmodel

is complicated, and would require a considerable research eort, in terms

of time and data resources. It is therefore important to, at the rst stage,

enlargeknowledgeandunderstandingabouthowtomodelbehaviouraleects

from ICT, as much as possible. At this stage, it is less important what

model approach that is used. Many of the results from this work apply to

the modelling of implications from ICT use per se, and not to a specic

model approach. The conventional modelling approach is further very well

established and accumulated experiences from data collection, estimation

Ithasalsobeenarguedthattheactivitybasedframeworkisbettersuited

tomodeltriptimingchoice,sincethisiscloselyrelatedtoactivityscheduling.

Some activity-based modelsalsooperateinatime-space continuum. Insuch

models, it is feasible to model trip timing choice as a continuous choice

and which reduces aggregation errors. The more conventional, trip based,

modellingapproachis,however, motivatedforthe samereasons asdiscussed

above. By developing activity and trip based applications parallel, sound

4.1 Methods

Discrete choice methods are used in all papers in this thesis. The rst two

papersapplythemultinomiallogitmodel(MNL).Thiswidelyusedmodel

re-liesontheassumptionthattheerrortermsofallalternativesareindependent

and identically Gumbel distributed. From this condition, the irrelevance of

independent alternatives(IIA) property follows. This property implies that

the cross-elasticity isconstant, i.e. thatif anattribute intheutilityfunction

of one alternative changes, the choice probabilities of all other alternatives

change by the same percentage. The MNL model also maintains the

as-sumptions of response homogeneity. All individuals are hence assumed to

be equallysensitive to the attributes included in the utility function.

(Ben-Akivaand Lerman1985)

However, there are also more general forms of discrete choice models

that relax these assumptions to various degrees. The most general of these

is the mixed logit model (Train 2003). The utility functions may include

several error components. The utility function of each alternative includes

oneindependentandidenticallydistributederrorterm,asintheMNLmodel.

Othererrorcomponentscanbespeciedsoastoachieveadesirable

variance-covariance structure of the model. The modeller species the distributions

of these. Several namesareusedforthis model,dependingonwhatproperty

that is emphasized. 'Mixed logit' isoften used for itsgenerality. It refers to

the fact that it is a mixture of logit models with a specied mixing

distri-bution, i.e. the distribution of the additionalerror components (Revelt and

Train 1998). The name 'Probit with a logit kernel' was used by Ben-Akiva

and Bolduc (1996). 'Probit' indicates that the mixing distribution in this

modelis normal.

Otherfrequentlyused termsare 'randomtaste logit'or'errorcomponent

logit'. Theformernameindicatesthatthismodelcanaccommodateresponse

heterogeneity. Thelatterreferstothefactthatthemodelallowsspecication

of an arbitraryerror-component structure. The dierence between the

'ran-domtastelogit'andthe'errorcomponentlogit'is,however, entirelyamatter

of interpretation (Train 2003). The substitution pattern of this model can

beveryexibleanddeterminedempirically. Thecross-elasticityoftwo

alter-nativesislargerthe smallerthestandard deviationofthe utilitydierenceis

between them. That is, the cross-elasticity of the choice between two

alter-natives is larger the more common unobserved attributes these alternatives

share, relativeto other alternatives.

two papers, assuming response heterogeneity also gives rise to a favourable

error-components structure.

In paper III, the normal distribution is applied to the cost parameter.

This distribution is by far the most common mixing distribution, because

it is easy to implement and works well technically. The limited number of

distributionsavailableinexitingsoftwarepackages isanotherreasonforwide

use of the normal distribution. However, more attention has recently been

paid tothe factthat thenormaldistribution isinappropriateinmany

appli-cations, due tothe fact that it isunbounded onboth sides and symmetrical

(Hess 2005). Itistypicallyunsuitablefortravelcostparameters, becausewe

knowapriori thatindividualsdonothaveapositivevaluationoftravelcost.

In paper III, the parameter value for the cost attribute is actually negative

for a non-negligiblepart of the population. Now, in this case it is still

pos-sible that some individualswould be willing to accept a salary reduction in

order to receive a exible oce. Since the normal distribution is used this

conclusion can, however, not be inferred fromthe data.

To avoid problems with wrong-signed random parameters we have

ap-plied the bounded and non-symmetrical

Johnson

0

_{s S}

B

distribution to

esti-mateschedulingandmonetarysensitivityinpapers IVand V.

Johnson

0

_{s S}

_B

distribution is highly exible and a nonlinear transformation of the normal

distribution. It can approximate the normal and log-normal distributions

but can also be specied to have a plateau or to be bi-modal shape (Train

and Sonnier 2005).

An important advantage of the MNL model is that ishas a closed form,

i.e. the choice probability can be expressed analytically. In contrast, the

(unconditional) choiceprobabilityof themixed logitmodelcannotbesolved

analytically. Thisisinsteadobtainedbyintegration oftheconditionalchoice

probability,overallpossiblevaluesoftherandomparameters. Thedimension

ofthisintegralequalsthenumberofrandomparametersorerrorcomponents

inthe model. Forestimation the simulatedmaximumlikelihoodapproachis

most widely used. The simulatedmaximum log-likelihoodfunction is

devel-oped fromthe simulated choice probabilities.

The downside of the mixed logit model is that the estimation times are

relativelylong. Analternativetothe mixedlogitisthe 'Probit'model. This

model also requires approximation of a multi-dimensional integral of each

iterationstep. The run time isstillshorter becausethe more ecient

GHK-estimator(see Train (2003))canbeapplied. However, this modelisalsoless

general since only normally distributed error components can be used, and

has not been used in the work of this thesis.

implemented in large-scale applications. As mentioned, there is an

alterna-tive class of closed form models, GEV models that also can accommodate

exible correlationstructures. McFadden (1978)rst derived the GEV-class

of models, and ve dierent model structures in this class have so far been

developed. Foranoverview of these see Bhat (2002).

Fortrip timing choice the Ordered GEV (OGEV) has received much

at-tentionbecausethismodelshouldbeappliedincaseswherethereisanatural

ordering of the alternatives. The disadvantage of GEV models is, however,

that they, incontrast to the simulation-basedmodels, cannotaccommodate

correlationintheunobserved utility(theerror term)across dierentchoices.

This kind of correlation often arises across choices within the same

indi-vidual in data sets that contains more than one choice per individual (this

often is the case in SP studies). The OGEV model structure was tried in

the departuretime choicemodellingprocess,but wasrejectedbecauseof the

importanceof correlation in the unobserved utilityacross dierentchoices.

For the estimations, the software program ALOGIT was used in paper

I and II and partly in paper III. In paper III the estimation procedure of

the mixed logit model was individually programmed in the programming

languageOx. InpaperIV andVthe software programBIOGEME(Bierlaire

2005) was applied.

4.2 Data

In this thesis several data sources have been used, both SP (stated

prefer-ence) and RP (revealed preference) data. The stated preference method is

based on hypothetical choice situations presented to the respondent and is

widely used in the marketing eld toanalyze consumer behaviour(Louviere

et al. 2000). Revealed preference data exposes the respondent's actual

be-haviour inareal situation. SP dataand RP data both haveadvantages and

limitations. Combining RP and SP data in discrete choice modelling, have

therefore a long tradition (Ben-Akiva and Morikawa 1990). Methodologies

to use the two types of data sources simultaneously in model estimations

are well established, and the widely used logit (see section 4.1) model has

so far been the predominant methodology (Bhat and Castelar 2002). Joint

SP-RP estimation is carried out in the nal paper in this thesis. However,

as discussed more advanced model structures than the MNL model is used

to overcomecertain limitationsof the logitmodelstructure.

The data primarily used in the rst part of the thesis (paper I and II)

was not collected for the present studies in particular, but is a repeated

collectedKOMonayearlybasis(thecollectionisnowcarriedoutwithlonger

intermissions). The collection was rst designed in 1996, and the design is

very typical for that time, when there was a very general interest in the

explicitlinkbetweentravelandothermodesofcommunicationbehaviour. In

thisdataalltransmittedcontactsareincluded,andarecategorizedintomail,

e-mail, phone, cell phone, Internet, fax or video and teleconferences. KOM

further includes traditional socio-economic and geographic data, but also

dataconcerningaccesstotelecommunicationsequipmentandtelecommuting

habits. Thisdatawaspartlyformodellingpurposesandpartlyfordescriptive

purposes. The modelling studies in this thesis are, however, so far the only

ones thathave been carried out using this data.

Thedataused inpaperIIIwascollected throughaSPsurvey atthe

tele-com company Ericsson in one oce district in Sweden. The SP questions

were supplemented with questions concerning the respondents' preferences

for telecommuting and actual experiencewith this work form. The

employ-eesreceivedthequestionnairetroughthecompanyinternalmail-system. The

aim of this survey was to discover company incentives to promote

telecom-muting and identify tools that the company can use to promote this work

form. The company incentive addressed in the SP survey was the potential

savingsfromreducedrentalcosts thatacompany maygainfromintroducing

exible oces simultaneously with telecommuting.

The survey conducted to obtain data for the estimation of the

depar-ture time model(paper IV and V) was alsospecically designed within this

project. An SP survey, designed to explore the trade o that commuters

make between shifts from the preferred departure time, travel cost, travel

time and travel time uncertainty, was carried out inthe spring 2005.

The population from which the respondents were recruited consisted of

car drivers travelling toward the city centre during the extended morning

peak period (06.00-10.00). The drivers were rst registered by road side

number plate registration, and a survey agency then called them the same

evening. Information about the observed trip (i.e. purpose, departure time,

travel time and preferred departure time) as well as some socioeconomic

information (including income) was collected. An SP survey was mailed to

them the followingday.

The RP data included the same set of respondents. Information

con-cerning actual mean travel times was obtained from the dynamic

assign-ment model, CONTRAM (Leonard et al. 1989), calibrated for a Stockholm

network. From this application we obtained travel times for the extended

morningpeak (6.30- 9.30a.m) with15minutes intervalresolution. Data on

5.1 Paper I, 'A Communication Choice Model'

This paper investigates several models with dierent structures, assuming

substitution between contact- and travel-based postal and bank activities.

The nal modelshows interesting relationshipsbetween socio-economic

sta-tusandcommunicationandtraveldemandforpostandbankactivities.

Well-educated people perform more contacts bytelephone and Internetthan

oth-ers. Women use the telephone more, while men are more inclined to use

the othermodes of communication, includingtravel. Older individuals have

lower accessibility to Internet. The age eect for Internet use is, however,

rather weak. This is surprising, since younger people in general are more

inclined toadopt new technology. Itis possible that older people can better

aord Internet use.

The result from the communication model estimation indicates some

problems with the model specication and model approach, as a means to

simultaneously evaluate contact and travel behaviour. Some of the

prob-lems thatarose were relatedtolimitationsofthe data. Otherproblems were

related to the model approach. The dierent kinds of problems are partly

connected,sincethesizeandthetypeofthedatasamplerestrictedthemodel

approach toa large extent.

Since information about the actual activities is so limited in the data

that was used, only possibilities of modelling substitution at a one-to-one

levelcouldbe investigated, for agiven activity. Othereects (se section2.1)

are even more complex and modelling these requires that it is possible to

link tripsand contactsconcerning the sameactivity. More complex ICT

im-plicationstend alsotobe more dynamicand thereforelong-term. Modelling

these would hencerequire datathat ismore processoriented andfocused on

habits, stretching overa longerperiod.

Scenario-based stated preference (SP) data, or in-dept interviews could

bea feasible way of collecting more process or activity oriented data taking

attitudes and perception better into account. SP data collectionwould also

beless dependentofthetimelagofthebehaviouralresponsetonew

technol-ogy. That is, the adoption rate of the services is likely to be higher than in

RP data,enablingmore sophisticatedmodellinganalysisthaninthe present

analysis. However, a disadvantage is that SP data may cause behavioural

overreactions to new technology based onfuturisticideas.

Theproblemswiththemodelapproachisprimarilylinkedtodenitionof

activities. Whenassumingsubstitutionataone-to-onelevel,asinthismodel,

are in such cases not exactly the same. In reality, substitutable activities

may be very dierent. For instance, downloading a movie orplaying games

via Internetathome maysometimes substitute recreationactivitiesatother

places, such as shopping and visiting friends. Even in cases where the goal

of the activities is similar, like for postal and bank activities, are the actual

activities are dierent, depending on the mode of communication (physical

or non-physical). The broader the activities are dened, the more dicult

it is to identify and specify when substitution actually takes place. This

means that the observed substitution eect will in generalbedependent on

the denition of the activities inthis kind of analysis.

Manyofthe ICTbased activitiesdidfurtherpreviouslynot exist. Hence,

ICT implies in most cases that activities are substituted, rather than that

the mode of communication is substituted. As the technology develops,

ac-tivities will continuously be substituted. This process is very dicult to

forecast, since it is not only driven by technology development. Attitudes

and perceptions play alsoa criticalrole for new activity adoption.

Now, all transport modelling approaches is dependent on a pre-dened

set of activities. Thefactthat the actualactivities changeasaresultofICT

development, implies that the possibilities of modelling substitution eects

are limited,irrespective of data source and modelling approach.

5.2 Paper II, 'Telecommuting and work travel demand

modelling in Sweden'

The most commonly discussed substitution eect is telecommuting. At

present, telecommuting explains a very small part of the variation in work

trip frequency between dierent industrialsectors. Only 124 employees out

of 7576 actually telecommute full days athome. Inthis perspective itis not

urgenttoincludetheeectoftelecommutinginlarge-scaletracforecasting.

Only 38 % of the working force works in industrial sectors in which

telecommutingisobservedatallinKOMandRES(SIKA2001)(RES is

sim-ilar to KOM, but includes only trips and no non-physical contacts). These

industrialsectors are concerned with computers, nance or media and

com-munication, authoritiesdealingwithissues concerninginfrastructureand

en-vironment and universities. Employees with high income and self-employed

persons have a larger propensity to telecommute. These groups have

pre-sumably high freedom in their work situation. The variables explaining the

private situation, i.e., children in the household, living with an employed

groups that have a possibility to actually choose. We can therefore expect

that anegativeattitudefromlabourmanagement,jobsuitabilityand lackof

neededequipmentexplainthelowtelecommutingfrequencytoalargeextent.

Telecommutingdoesnot seem tobeinuenced by orinuence low

acces-sibilitytothe labourmarket. Surprisingly,individualswithlowcar

competi-tion actually have larger propensity totelecommute. This alsopoints tothe

fact that accessibility tothe labour market was not animportant factor for

adopting telecommuting atthe time whenthe data was collected.

In summary, KOM data is useful to get an overview of the spread of

telecommuting. However, the data is too general and broad with respect to

the work force and also with respect to the information on facilitators and

constraints, in order to allow for deeper forecasting studies. The number of

telecommuters in the sample is very small, which makes model estimations

uncertain.

5.3 Paper III, 'Company Incentives and Tools for

Pro-moting Telecommuting'

As mentionedinthe introduction,this paperismoredirectlypolicyoriented

and seeks to nd ways to increase the amount of telecommuting. Since the

ndings in paper II indicated that constraints in the work situation and a

negativeattitudefromlabourmanagementhindertelecommuting,thispaper

aims at identifyingmeans for the company topromote telecommuting.

Thebasicideaisthattelecommutingimpliesthatalargeproportionofthe

company's oce space isunoccupied, providinga potentialto reduce rental

cost. However, to utilize this eciently, exible oces must be introduced

in which the employees donot have their own oce, but use any desk in an

open oce space. Employees' monetaryvaluationof thepresent oceplace,

in comparison to a exible oce is therefore tested, in order to estimate

the potential rental cost savings. The results indicate that employees are

in fact sensitive to the monetary compensation and that company benets

could be obtained from introducing exible oces. It is further indicated

that employees perceive an increase in work eciency from telecommuting,

whichis ultimately protable tothe company.

A majority of the employees in the study further want to telecommute

more that they actually do. This nding is coherent with the indication

that telecommuting is a privilege for certain groups that have a possibility

to actuallychoose, found inpaperII. Consequently we believe thatit would

computer supportand equipment to the employees.

In summary, the result suggests that package solutions, where the

com-panyallowstelecommunicationconditionallyontheemployees'acceptanceof

exible ocescouldprovetobeaneective companypolicy. In reverse, this

kind of package solution might reduce the levels of the monetary

compen-sations for introducing a exible oce. Analysis shows that telecommuters

actually demand less compensation to accept a exible oce than others.

The compensation to telecommuters could also be designed as company

-nanced technologicalequipmentand relatedservices.

Asmentionednoevidences that telecommutingshould giverise tourban

sprawl, was found in paper II. A joint conclusions from paper II and III

is thus that there is ground for promoting telecommuting from a societal,

individualand companyperspective. Itis,ontheotherhand, notlikelythat

telecommutingwillbealargefactor forthe overallworktripfrequency. This

is, however, dependingnot only onhowtelecommutingispromoted but also

on otherfactors, such as transportcosts.

5.4 Paper IV, 'Departure Time Modelling:

Applicabil-ity and Travel Time Uncertainty. '

Paper IV and V develops a departure time and mode which choice model.

Thismodelexplainshowcardriverstradeotraveltime,traveltime

variabil-ity,monetaryandschedulingcostswhen choosingdeparturetime. Given

ap-propriate inputdata andacalibrated dynamicassignment model,the model

can be applied tocalculate benets fromsuch as infrastructure investments

and policymeasures.

The modelshouldprimarily beused for analysesin relativelyshort-term

perspectives. Typical examples would be evaluation of congestion pricing

schemesandinfrastructureprojectsthatarerealizedwithinarelativelyshort

time. In longer-term perspective, alterations in trac generation and

desti-nation choice are more important dimensions. These are not considered in

themodel. Mode choiceisfurtheronlyconsideredpartially,bymodellingthe

propensity to switch from driving. It does not, however, take into account

that public transport travellers may switch todriving.

Previousstudiesanalysingtraveltimevariabilityusingtheexpected

schedul-ing approach (see 2.2) have started out from the assumption that travellers

intend to arrive at the most preferred arrival time,

P AT

. Travellers are thusassumed tochoose departure timesoastominimizetherisk ofarriving

travel conditions. The departure time shifts considered are therefore

con-siderably larger than those considered in previous studies. Since we assume

thattravellersshiftintendedarrivaltime,expectedschedulingcost shouldbe

computed with respect to the new intended arrivaltime, and not to

P AT

. Now, the intended arrival time is usually not directly observable, which

means thatthe researcher has tomakesome assumptionabout it. A

reason-able assumption seems, however, to be that it is equal to expected arrival

time. For instance, assume that, for a given departure time and traveller,

arrival time usually vary between 8.00 a.m. and 8.30 a.m., and expected

arrival time is 8.10 a.m. It is then reasonable to assume that the traveller

use 8.10a.m. as areference. Arrivalearlierthan 8.10 a.m. isthusperceived

as early arrival, and arrival later than 8.10 a.m. as late arrival. This

as-sumption leads to equivalence of the expected scheduling approach and the

mean-variance approach, if the travel time distribution has certain

proper-ties. The lognormal distribution, given that the standard deviation of the

underlying normal distribution is xed, and the exponential distribution,

both have these properties.

In the present paper the model is estimated using SP data collected

for this particular purpose. Estimated schedulingdisutility, with respect to

travel time, is in linewith earlier studies. We found signicant unobserved

heterogeneity in scheduling costs sensitivity, but no observed heterogeneity

except between the three populationsegments. This points to the fact that

there are variousconditions thatdetermine the drivers schedulingexibility,

inadditiontoworkschedulingandtravelpurpose. Theexplanatoryvariables

availablein this study could,however, not capture these conditions.

Itwas alsofound that temporalconstraints are important both atorigin

and destination, but that they workin dierent directions. Hence, temporal

constraintsatoriginareprimarilyrestrictingearlydepartureand constraints

at destination primarily restricting late arrival. If not taking this into

ac-count,schedulingcostswasconsiderablyunderestimated. Specically,alarge

proportion oftravellers then appear tohave avery lowvaluation scheduling

costs. ThiscouldeasilybeinterpretedasSPartefacts,causedbyrespondents

not taken their restrictions properly into accounts, whereas it is actually an

eect of the less well specied

SDL

variable.

Sensitivityfortraveltimeuncertainty,orreliability,isnormallycomputed

asa ratiobetween sensitivityfor standard deviationand sensitivity tomean

travel time and this ratio is often called reliability ratio. This study shows

also that reliability ratio is dependent on intended arrival time. If intended

arrival time is earlier than

P AT

, travel time uncertainty is not signicant in the two largest segments. Travel time uncertainty also proves to be least

This resultwas expectedsince manyof thesetravellers donot havetoarrive

punctually.

In order to avoid travellers with wrong-signed scheduling cost and cost

sensitivity the highlyexible

Johnson

0

_{s S}

_B

distribution, bounded in the

in-terval[-1,0]was appliedtoallrandomparameters. Theparameter estimates

proved to be insensitive to the assumption about these bounds. Analysis

further showed that the pseudo random draws perform remarkably well in

comparison totheModiedLatin HypercubeSample(MLHS)draws as

pro-posed by Hess etal. (2006),incoherence with theirndings.

Takingcorrelation inthe unobserved heterogeneity acrossthe same

indi-vidual into account proved to be crucial for correctestimation of the

distri-bution of the random parameters. This implies that valuationof scheduling

disutilityisrandomlydistributedinthepopulation,but relativelyconsistent

across allSP choices. Ifneglecting the correlation of the unobserved

hetero-geneity we thus fail to estimate the substitution pattern correctly and the

modelevencollapsestoanestedMNLmodel. Theconsequenceofthisisthat

wecannotapplytheclosed-formOGEVmodeltothisdata,whichwouldhave

beenfasterintheapplication,sinceGEVmodelscannotaccommodate

corre-lation in unobserved heterogeneity. However, model implementation proves

that the run times of the simulation based mixed logit model are relatively

small compared to the run times of the assignment model, and the mixed

logitmodelmay thusbe used for implementation.

Finally,assumingthat departure timespresented inthe SPchoices dier

relatively much from

P DT

, involves a data collection problem. Including travel time uncertainty and departure time shifts jointly as a attributes,

will in general imply that the disutility arising from departure time

shift-ing is larger than the disutility arising from travel time uncertainty. The

consequence is that it more dicult toproduce reasonable trade os, which

increases the risk of gettingpooraccuracy inthe estimates.

5.5 Paper V, 'Joint RP-SP Data in Mixed Logit

Anal-ysis of Trip Timing Decisions'

This paperextends theanalysis ofpaperIV,by usingjointRPandSPdata.

It has provided an opportunity to compare observed and stated behaviour,

whichis very valuable. Noother such comparisonsondeparture time choice

have been published. The present comparison indicates that there are

sys-tematicdierences inRPand SPdata. Thesedierences aremanifestedina

ual. Thesedierences seem,however, tobesmallerforcommuterswith xed

schedule. In order to assess the validity of the model, itis importantto try

to analyse howthese dierences arise.

67%oftherespondentsreportedthattheyactuallydepartedatpreferred

departuretime(

P DT

)intheRPdata,whichexplainsthehighresponsescale in the RP data. This proportion, and the response scale dierence, would

probably have been smaller if the cost attribute had been included in the

RP data, as well as in SP data. It might also have been smaller if we had

asked directlyabout preferredarrivaltime, insteadofdeparture time, inthe

interview.

Inthe RPdatawe have furtheraggregationerrors,since wehavedivided

the extended morning peak into15 minutes intervals, which might have

af-fected estimated trade-os. This would be one reason for the dierence in

scheduling disutility between RP and SP choices within individuals. The

temporal dierences in RP and SP choice situations might also be an

im-portant factor. In RP data observed behaviour is a result of a long-term

adaptation to actual travel conditions. The SP choice is short-term in the

sense that onlyone particular trip is considered. This has probably implied

higher temporalexibility for some travellers and smaller exibility for

oth-ers, in the SP choice. If this explanation is valid, SP data is generally less

trust worthy than RP data, given that long-termbehaviourand preferences

are what we wishto analyseand forecast. In this caselong-termrefers to at

least a couple of months, as opposed to one or a few days. For commuters

with xed schedule we expect that short-term and longer term exibility is

more equal. This would explain why the scheduling sensitivity is more

con-stant across RPand SPchoices within the same individualin this segment.

Itispossiblethattheproblemwithdierenttemporalperspectivesinthe

RPand SPchoicewould havebeenavoidedif wehadasked explicitlyforthe

long-termchoice. Thereare, however, two reasonsfor presenting the choices

as concerning only the observed trip. First, along-term choice relies on the

fact that the trip is made on a regular basis. Asking about the long-term

choice thus excludes trips that are not made regularly. Second, we tried to

make the choice situationas concrete and realisticas possible. We assumed

that this would help the respondents totake their restrictions properlyinto

account. Also, ten respondents taking part in a pilot survey were called

afterreturning the questionnaire. We asked if their choices wouldhavebeen

dierentif wehad asked about theirlonger-termchoiceand not particularly

about the observed trip. All ten respondents answered that their choices

would not dier. This was also a reason for our decision about the survey

Demand Modelling?

•

One-daycross-sectionaldataingeneralinunsuitableformodellingICT implications relatedto travel.

•

The fact that the actualactivities changes asa result of ICT develop-ment,impliesthat thepossibilitiesofmodellingsubstitutioneects are

limited, irrespective of data source and modellingapproach.

•

Telecommutingexplainsatpresentaverysmallpartofthe variationin work trip frequency. There is no evidence that telecommuting should

giverise tourban sprawl.

•

Telecommuting seems to be a privilege for certain groups that have a possibility to actually choose and a majority of the employees at

the telecom company Ericsson want to telecommute more that they

actually do.

•

Employeesareinfactsensitivetomonetarycompensationandcompany benetscould beobtainedfromintroducingexible ocesin

combina-tion with telecommuting.

•

A departure time and mode switch choice modelis estimated on joint RP and SP data. It explains how car drivers trade o travel time,

travel time variability, monetary and scheduling costs when choosing

departuretime. Givenappropriateinputdataandacalibrateddynamic

assignmentmodel, the modelcanbeappliedtocalculatebenetsfrom

such asinfrastructure investments and policy measures.

•

Weassumethatintendedarrivalisequaltoexpectedarrivaltime. This implies that the expected schedulingapproach and the mean-variance

approachareequivalent,ifthetraveltimedistributionhascertain

prop-erties.

•

Temporal constraints are important both at origin and destination, but constraints at the origin are primarily restricting early departure

and constraintsatthe destinationare primarilyrestrictinglate arrival.

Sensitivity for travel time uncertainty isonly signicant of if intended

arrivaltime is later than

P AT

.

•

Takingcorrelationinunobservedheterogeneityacrossthesame individ-ualintoaccountwascrucialforcorrectestimationofthedistributionof

ityis randomlydistributed inthe population, but relativelyconsistent

across allSP choices.

•

There are systematic dierences in RP and SP data, and these are manifested in a larger response scale in the RP data, and the fact

that scheduling disutility is not constant across RP and SP choices

within each individual. The scale dierence is largely due to the fact

that 67 % of the respondents reported that they actually departed at

preferred departure time. The response dierences accros RP and SP

choices might be due to the temporaldierences in RP and SP choice

This thesis shows that conventional travel demand models are not suitable

for modelling implications from ICT. Activity-based approaches might, at

least fromsometheoreticalpointsofview, bebettersuitedforincorporating

ICT related eects, because of the focus on activity scheduling in time and

space. However, since denition of activities is crucial and ICT seems to

imply that these constantly change, it will be very dicult to include ICT

implications inany explicitmodellingapproach.

Even if it proved to be possible to model implications of ICT in travel

demand models, the problem of forecasting still remains. Perceptions and

attitudes, which are critical as explanatory variables for future ICT-based

serviceadoption,aremuchmorediculttoforecast,thanconventional

socio-economic data. The problem is even more severe if we add the diculty of

forecastingwhattypesoftelecommunication-basedservicesthat willemerge.

ICTandtravelsubstitutionwillpresumablyalsobeverydependent onother

factors in society, such as travel costs and economical growth.

Considering these issues, in-dept interviews and stated preference-based

data, where scenarios are presented to the respondents, are the only

possi-bility lefttoforecastingICTservice adoptionandtravelrelatedimplication.

This method was used in the European Commission 6th framework POET

project (de Jong et al. 2006). However, this kind of data collection is

de-pendent on a realistic description of the scenarios. It is further dependent

on the fact that respondents actually know howthey would respond to new

technology, withoutactually havingexperience of it fromtheir dailylife.

On the other hand, this work shows, inline with other studies, that the

expectation and hope that ICT would reduce travel was exaggerated. It is

clear that individualsand commercial business in general have not used the

opportunities given by telecommunications to reduce physical travel. The

POET study also showed that the eects on travel pattern implied by new

ICTservicearegenerallysmall. Asdiscussedinsection2.1,ICTimplications

arealsopresumablyinuencingtravelpatternsinaverycomplexmannerand

are dicult to distinguish from other factors acting on travel patterns. In

this perspective, and given the diculties of modelling the eects, it does

not seem veryurgenttospend a largeresearch eorttoincludeimplications

from ICT in large-scale trac forecasting.

Theprimaryresearcheort, fromatransportperspective,shouldinstead

focus on the challenge to make use of new service innovations to meet the

goals set up by society. In this perspective, ICT has the potential to

re-duce negativeeects from transportation. The focus shouldprimarily be on

improvedinformationservicesinthepublictransportandcarpoolingsystems

wouldincreasetheattractionofthese,lessresourceconsuming,travelmodes.

Along-termobjectiveofSILVESTER (seesection1),istolaterintegrate

the departure time model with the mode choice model of the Swedish

na-tionaltravelforecastingmodelSAMPERS(BeserandAlgers2001). However,

the demand models of SAMPERS are estimated in another choice context,

as compared to the departure time choice model. In the former frequency,

mode and destinationchoiceare modelledsimultaneouslywhereas the latter

assumes xed frequency and destination choice. Integrating the departure

time choice model directly in SAMPERS, would therefore cause

inconsis-tency. The easiesway toovercomethis problemwould betore-estimate the

mode and destination choice modelof SAMPERS. The inclusive value from

the departure time modelwould then replace time and cost variables in the

car alternative. This is possible since we explicitly have estimated the RP

scale of the utilityfunction of the departure time model.

Anobstaclewhenmodellingeects oftraveltimeuncertainty,andforthe

SP designinparticular, wasthe limitedknowledgeabout the distributionof

travel times, and drivers' perception about this. Little was further known

about travellers' preferences and behavioural response to this attribute.

Perceptions and behavioural response to uncertain travel times is very

diculttoanalyse fromdata collectionperspective. In theRP situationthe

problemisthataverylargenumberofobservationsisneededtogetacorrect

measurement of the travel time variability. Excluding observations in the

cleaning processing must therefore be made extremely careful. The obvious

risk is otherwise that observations of unusually high travel times, which are

the most important ones, are assumed because of expected measurement

errors. Theproblemof measuringvariabilityinthe SPdesignarises fromits

complexity,andthe factthatthis attributeingeneralisdiculttointerpret.

A key issue for further research is thus how travellers perceive travel time

variability and howit inuences their preferences.

ThepresentworkalsoindicatesdierencesinSPandRPdataconcerning

trip timingchoice. Fortracforecastingit wouldtherefore beveryvaluable

to analyse RP data includingthe cost attribute. Such data is presently not

available,but can be obtained fromthe tolls trialin Stockholm. Ideally the

respondents taking part in the present survey would be asked again about

their departure time during this trial. New respondents could also be

re-cruited and asked about their departure time before, during and after the

trial. This approachrelies, however, onrespondent's abilitytorecollectpast

Arnott, R., A. de Palma and R. Lindsey 1998. Recent developments in the

bottleneckmodel. In: K.J. Button and E.T.Verhoefeds. Road Pricing,

TracCongestionandtheEnvironment: Issuesof EciencyandSocial

FeasibilityCheltenham: Edward Elgar, 79-110.

Axhausen, K., Pendyla, R., 2002. Microsimulation: workshop report.

em-phIn: H.S. Mahmassani, ed. In Perpetual Motion: Travel Behavior

Re-search Opportunities and Application Challenged.. Oxford: Pergamon,

Elsevier Science, .

Bates,J.,Polak,J., Jones,P.andCook,A.J,2001.Thevaluationofreliability

for personaltravel.Transportation Research Part E, 37(2-3), 191-229.

Ben-Akiva, M. and Lerman, S.R., 1985. Discrete Choice Analysis: Theory

and Application to travel Demand. Cambridge: The MIT Press.

Ben-Akiva,M.andMorikawa,T., 1990.EstimationsofTravelDemand

Mod-elsfromMultiple DataSources. Proceedingsfrom the11

th

International

Symposium on Transportation and Trac Theory, Yokohama, Japan,

Elsevier New York,461-476.

Ben-Akiva, M. and Bolduc, D., 1996. Multinomial Probit with a Logit

Ker-nel and a General Parametric Specication of the Covariance

Struc-ture.[PDF]workingpaper,Departmentd'Economique,UniversiteLaval,

Quebec, Canada.

Ben-Akiva, M., Bowman, J. and Gopinath, D., 1996. Travel demand model

system forthe informationera, Transportation, Vol 23,241-266.

Beser, M. and Algers, S., 2001. SAMPERS - The new Swedish National

Travel Demand Forecasting Tool.emphIn: Lundqvist, L.and Mattsson

L-G.ed.Nationaltransport models,RecentDevelopments andProspects,

Springer.

Bhat,C.,1998a.AnAnalysisofTravelModeandDepartureTimeChoicefor

UrbanShoppingTrips.TransportationResearchPart B,32(6),361-371.

Bhat,C., 1998b.Accomodatingexiblesubstitutionpatternsin

multidimen-sionalchoice modelling: formulationandapplicationtotravelmodeand

revealed and stated preferences: formulation and application to

con-gestion pricing analysis in the San Francisco Bay area. Transportation

Research Part B, 36(7), 593-616.

Bhat,C.R., 2002.RecentMethodologicalAdvancesRelevanttoActivityand

Travel Behavior Analysis. emphIn: H.S. Mahmassani, ed. In Perpetual

Motion: Travel Behavior Research Opportunities and Application

Chal-lenged.. Oxford: Pergamon, Elsevier Science, 381-414.

Bhat, C.R., Sivakumar, A. and Axhausen, K.W., 2003. An analysis of

the impact of information and communication technologies on

non-maintenance shopping activities. Transportation Research B, 37 (10),

857-881.

Brewer, A.M. and Hensher, D.A., 2000. Distributed work and travel

be-haviour: The dynamicsofinteractiveagency choicesbetween employers

and employees, Transportation, 27,117-148.

Bierlaire, M., 2005. BIOGEME Version 1.4. Available from:

http://roso.ep.ch/biogeme

Black, I.G., Towriss, J.G., 1993. Demand Eects of Travel TimeReliability.

Craneld Institute of Technology: Centre for Logisticsand

Transporta-tion.

Brownstone,D.,Bunch,D,Train,K.,2000.Jointmixedlogitofstatedand

re-vealed preferences for alternative-fuelvehicles.Transportation Research

Part B, 34, 315-338.

Brownstone, D., Small, K, 2005. Valuing time and reliability: assessing

theevidencefromroadpricingdemonstrations.TransportationResearch

Part A, 39, 279-293.

Cao, X., Mokhtarian, P.L., 2005. The Intended and Actual Adoption of

Online Purchasing: A Brief Review of Recent Literature Institute of

Transportation Studies, University of California: Davis, Research

Re-portUCD-ITS-RR-05-07.

Castells, M., 1996. Rise of the Network Society . Cambridge, MA, USA.

Blackwell Publishers, Inc.

Cirillo, C., Torino, P. D., Daly, A., and Lindveld, K., 1996. Estimating bias

due to the repeated measurements probleminSPdata. The24th