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Journal of Intelligent Transportation Systems:
Technology, Planning, and Operations
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Perspectives on Future Transportation Research:Impact of Intelligent Transportation System
Technologies on Next-Generation TransportationModelingBin Ran
a, Peter J. Jin
b, David Boyce
c, Tony Z. Qiu
d& Yang Cheng
a
aDepartment of Civil and Environment Engineering , University of WisconsinMadison ,
Madison , Wisconsin , USAb
Department of Civil, Architectural, and Environmental Engineering , The University ofTexas at Austin , Austin , Texas , USAcDepartment of Civil and Environmental Engineering , Northwestern University , Evanston
Illinois , USAdDepartment of Civil and Environmental Engineering , University of Alberta , Edmonton ,
Alberta , Canada
Accepted author version posted online: 12 Jul 2012.Published online: 01 Nov 2012.
To cite this article:Bin Ran , Peter J. Jin , David Boyce , Tony Z. Qiu & Yang Cheng (2012) Perspectives on FutureTransportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation
Modeling, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 16:4, 226-242, DOI:
10.1080/15472450.2012.710158
To link to this article: http://dx.doi.org/10.1080/15472450.2012.710158
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Journal of Intelligent Transportation Systems, 16(4):226242, 2012
Copyright C Taylor and Francis Group, LLC
ISSN: 1547-2450 print / 1547-2442 online
DOI: 10.1080/15472450.2012.710158
Review Article
Perspectives on FutureTransportation Research: Impactof Intell igent Transportation SystemTechnologies on Next-GenerationTransportation Modeling
BIN RAN,1 PETER J. JIN,2 DAVID BOYCE,3 TONY Z. QIU,4
and YANG CHENG1
1Department of Civil and Environment Engineering, University of WisconsinMadison, Madison, Wisconsin, USA2Department of Civil, Architectural, and Environmental Engineering, The University of Texas at Austin, Austin, Texas, USA3Department of Civil and Environmental Engineering, Northwestern University, Evanston, Illinois, USA4Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada
In this paper, we attempt to summarize the impact of technologies, especially intelligent transportation system (ITS) tech-
nologies, on transportation research during the last several decades and provide perspectives on how future transportation
research may be affected by the availability and development of new ITS technologies. The intended audience of the paper
includes young transportation researchers and professionals. Current transportation models are divided into generations
based on their technological and practical background. Based on the trends in the past and the potential technologies to
be implemented in the future, general characteristics of the next generations of transportation models are proposed and
discussed to provide a vision regarding expected future achievements in transportation research. This paper is intended to
be a working document, in the sense that it will be updated periodically.
Keywords: Intelligent Transportation Systems; Next-Generation Transportation Models; Transportation Research
INTRODUCTION
Transportation research deals with a complex real-world
system, the transportation system. It covers the theoretical prin-
ciples and practical techniques that can be implemented and
applied in various aspects, including planning, design, con-
struction, operations, safety, and so on. One special character oftransportation research is that it evolves intensively with tech-
nological innovations. In some sense, the entire transportation
Special thanks to Professor David Noyce, University of Wisconsin at Madi-
son, for an inspiring discussion regarding the traffic safety and control subarea
of transportation research. The authors also thank six anonymous reviewers for
their insightful comments and suggestions.
Address correspondence to Bin Ran, Department of Civil & Environmen-
tal Engineering, University of WisconsinMadison, 1415 Engineering Drive,
Madison, WI 53706, USA. E-mail: [email protected]
system is built upon the interaction between human and tech-
nologies. Technologies not only promote new ways of observ-
ing, monitoring, and managing transportation systems but also
have the ability to change the basic characteristics of the trans-
portation system fundamentally. The fundamental diagram re-
lationship among speed, flow, and density (Greenshields, 1935)in the 1930s was one of the earliest and most representative
transportation models. Since then, transportation research has
advanced significantly both in breadth and in depth with respect
to almost all aspects of the transportation system, especially
with the development of intelligent transportation system (ITS)
technologies since the 1990s.
A critical problem for a novice transportation researcher
nowadays is that it is easier to understand a detailed research
topic than to initiate fundamental thinking about transportation
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PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 22
and to understand how transportation research has evolved and
advanced during the last centuries. The purposes of this paper
are (a) to sort out the motivations and ways of thinking that lie
behind transportation research; (b) to review how technologies
and practical needs affected transportation research in the past
decades; and (c) to explore what to expect in future transporta-
tion research as new ITS technologies become available. Theintended audiences of this paper are the young generations of
researchers, ranging from graduate students to young scientists
and engineers. This paper may not include every detailed aspect
of transportation research, given the limited resources and time.
Moreover, this paper is intended to be a working document to be
updated over the years. An earlier attempt to describe the state
of current research problems and future prospects for innova-
tion was based on a conference held in 1985 and published as a
special issue of Transportation Research(Boyce, 1985).
EVOLUTION OF TRANSPORTATIONMODELS
Transportation models can be classified in many different
ways. In this paper, however, we are more interested in tracking
the evolution of transportation models in response to the trends
in technology advance, methodology concepts, and practical
requirements over a long time horizon. From this perspective,
transportation models can be classified into different genera-
tions. By summarizing what has been achieved in the past gen-
erations, we can offersome projections of what may be expected
in future generations (e.g., the next 30 years) of transportation
models, considering some promising ITS technologies being or
expected to be implemented. If one looks back into the history
of the transportation research, three major waves can be iden-
tified. The first wave began in the 1950s with the construction
and massive use of freeway systems worldwide (U.S. Inter-
state, based on earlier experience with the German autobahn
and American turnpikes), which provided a new perspective
in transportation engineering. Researchers and engineers have
been motivated to study the detailed characteristics of the new
transportation systems and explore methods of operating and
managing the expanding system (Weiner, 2009). Due to the dif-
ficulty and complexity of collecting data at that time, models
during this period were primarily empirical and static. Models
and theories are developed based on either ideal assumptions, or
very limited experimental and survey data. However, they still
serve as basic guidelines that help plan, construct, and operatethe early transportation systems. Transportation models devel-
oped during this time period (1950s to 1980s) are here referred
to as first-generation models.
The second wave was triggered by the rapid development of
information technologies after 1980, as well as the legislation
progress regarding transportation systems, such as ISTEA
(Gage & McDowell, 1995), which is the emergence of the
intelligent transportation system (ITS) technologies. During
this time period, the most critical issue that emerged was the
balance between the limited supply that can be added to the
existing infrastructure and the ever-growing travel demand
Different approaches have been taken, including explor
the additional capacity from the existing infrastructure an
using planning strategies to balance the transportation suppl
and demand (Meyer & Miller, 2000) by promote alternativ
transportation modes. Tackling such issues relies on mor
detailed and dynamic information regarding traveler demanand road conditions. Information technologies, along with th
development in vehicle sensing technologies, allow engineer
and researchers to collect, analyze, model, and predict trans
portation phenomena more rapidly, more efficiently, and mor
accurately than ever before. During this period, dynamic, sta
tistical, and disaggregated transportation models with rigorou
formulations and efficient numerical methods originating from
physics, economics, computer science, and other scientifi
fields, suitable for network or system performance evaluation
were widely developed. We refer to models that have thes
features and that emerged during the 1980s to 2000s a
second-generation models. Most early ITS models lie withi
this generation, even though the scope of ITS has been greatlextended by more advanced technologies and models.
The third and current wave has been primarily driven by
rapidly growing wireless communication technologies in th
new century. Reliable connectivity between all elements (hu
man, vehicle, and infrastructure) in transportation systems ca
now be achieved. Such connectivity facilitates not only the real
time data collection of transportation systems but also the ac
tive coordination of vehicles in real-time. Models in this perio
have the characteristics of real-time capability, active contro
and integration among different data sources and different ap
plications. However, these models still assume that the natu
ral characteristics of flow in the transportation system, suc
as human driving, local perception, and so on, will remai
largely unchanged. With the future development of commu
nication technologies along with smart vehicle technologies i
the automobile industry, fully automated and controlled trans
portation systems may become possible. This advance may sta
the next wave in transportation model development, since traf
fic flow can be changed fundamentally to automated, proactive
well-informed, and fully controlled flow, which may be trig
gered by several new technologies that are under developmen
such as cloud computing (Armbrust et al., 2009), Internet o
Things (CASAGRAS Research, 2010), and distributed comput
ing (Attiya et al., 2004). Fourth-generation models in this wav
may be highly integrated, highly reliable, distributed, and system optimized based on the above new characteristics of traffi
flow. There are several key differences between the third- an
fourth-generation models. First of all, the third-generation mod
els deal with the increased automation in driving and travelin
with the development of the connected vehicle technologies
while the fourth-generation models study the potential full
automated traveling in the future. The difficulty in the third
generation models is to describe the impact of the increased
connectivity and control within mixed noninformed, informed
and connected driving and traveling, while the difficulty in th
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228 B. RAN ET AL.
fourth-generation model is to explore system-wide and cus-
tomized solutions to stochastic travel demand by data mining
over themassive amountof data. Thelatter onemay sound trivial
but is, in fact, a very complicated system optimization problem.
Tables 1 and 2 describe the main objectives, key character-
istics, data environment, major applications, and their issues of
each generation of models. The major stages of modeling re-search and some important applications are also plotted on the
timeline of transportation models in Figure 1.
As illustrated in Figure 1, the division of generations is pri-
marily based on the emerging times of those models, though
many models in the first and second generations were still under
development in research or have been intensively used in prac-
tice during later generations. In the rest of this paper, we expand
the summary table into a more detailed discussion regarding
methodology, challenges and opportunities, and theoretical and
technological tools and applications.
EVOLUTION OF METHODOLOGY IN
TRANSPORTATIONRESEARCH
Transportation models generally can be classified into mi-
croscopic, mesoscopic, macroscopic, and metascopic models.
Microscopic models study individual elements of transportation
systems, such as individual vehicle dynamics and individual
traveler behavior. Mesoscopic models analyze transportation
elements in small groups, within which elements are considered
homogeneous. A typical example is vehicle platoon dynamics
and household-level travel behavior. Macroscopic models deal
with aggregated characteristics of transportation elements,
such as aggregated traffic flow dynamics and zonal-level travel
demand analysis.
Major research objects in transportation engineering include
traffic flow, travel behavior, transportation networks, traffic con-
trol and management, freight systems, and other transportation
modes. The study on traffic flow includes its micro-, meso-, and
macroscopic characteristics, human factors, autonomous vehi-
cles, and so on. Common approaches include empirical studies,
and statistical and computer science modeling motivated by
new data collection technologies. Theories and models devel-
oped for similar physical objects, such as fluid and particles,
are sometimes introduced and improved to fit traffic flow char-
acteristics. The research topics of travel behavior include de-
mand analysis, route choice, day-to-day dynamics, and activitychoices. Research methods usually involve survey-based meth-
ods and travel choice models that originated from economics
and logistics. Traffic control and management involves the de-
sign and management of traffic control devices, traveler infor-
mation provision, and more recently vehicular communication
system. Optimization and control methods are usually involved.
Transportation network consists of traffic flow, traveler behav-
ior, and traffic control. Its design and performance evaluation
usually rely on integrated models of both planning and opera-
tions. The study of freight systems involves the performance,
optimization, and management of commodity flow. Other re-
search objects also include several alternative modes such as
public transportation, bicycles, and pedestrians, which are also
important components in transportation systems and can either
be studied along with behavior model or operational models or
together with passenger vehicles as alternative studies.
These basic research objects remain relatively staticthroughout the history of transportation research; however,
models to describe and analyze those objects have evolved from
generation to generation. Meanwhile, technologies play im-
portant roles in studying these research objects. More detailed
data sets can reveal new characters of those objects and lead to
new methodologies and models. For example, from traditional
license-plate matching, to inductive loop detectors, and to
probe vehicle technologies, the methodology on estimating
and managing traffic flow dynamics on both freeway and
arterials has evolved from empirical relationship analysis to
complicated traffic state estimation and advanced traffic control
models. Furthermore, similar to the other engineering fields, the
evolution of transportation models usually involves four majortypes of contributions: (A) the discovery and introduction of
new principles and relationships, (B) the integration of models,
(C) the relaxation of ideal assumptions, and (D) performance
improvement. The first two types of contributions usually
come during the transition period between major generations;
the second two types of contributions occur regularly during
all periods. The term model is not used in the type A
contribution since this type of contribution only refers to truly
fundamental and original models. Typical examples include the
fundamental diagrams of traffic flow, kinematic models, and
gravity models. One should not underestimate the contributions
of the latter four types of contributions, since usually the first
type of contribution only result in very raw and ideal models
and formulations that sometimes take years to evolve into
practically accurate and efficient models that can be applied in
the real world, which is quite important for a practical field like
transportation. A famous example is the development of the cell
transmission model (Daganzo, 1993), which made solving the
traffic dynamics inferred by LWR model (Lighthill et al., 1955)
truly efficient and scalable for traffic operations, even though
it is a category-D contribution. Table 3 summarizes the major
existing and expected contributions and their corresponding
types in different generations and different types of models.
CHALLENGESANDOPPORTUNITIES
Similar to other engineering fields, transportation research
has always been motivated by practical needs and technol-
ogy availability. In this section, we discuss the impact of these
two aspects on transportation models, especially the potential
challenges and opportunities that may lead to next-generation
transportation models. As illustrated in Figure 2, the practical re-
quirements for the next generation of models can emerge early
in an old generation, when limitations of existing models are
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PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 22
Table1
Summaryoffourgenerationsoftransportationmodels.
Firstgeneration(1950s1980s)
Second
generation(1980s2000s)
Thirdgeneration(2000snear
futuredecades)
Fourthgeneration(2000sdistant
futuredecades)
Technologicalbackground
Massiveconstructionof
transportationinfrastructures
Earlyintelligenttransportationsystem
(ITS)te
chnologies
Wirelesscommunication
technologies
Cloudcomp
uting,Internetof
Things,sup
ercomputers
Objectives
Operateearlytransportation
systems
Createp
otentialsupplyfromexisting
infrastructure
Accommodatebothhumanand
automa
teddriving
Real-timecontrolandmanagement
oftransportationsystems
Balance
supplyanddemand
Actives
upplyanddemand
management
Proactiveco
ntrolandmanagement
Keycharacteristics
Empiricalmodels
Staticmodels
Descriptivemodels
Dynamicmodel
Statisticalmodels
Partialm
acroscopiccontrol
Independentmodels
Behavio
ralmodels
Actuatedcontrol
Richdataenvironment
Partialm
acroscopic/microscopic
control
Interactionwithvehicular
network
Transitionbetweenhumanand
automa
tedtraveling
Massivedataenvironment
Automatedenvironment
Fullyintegratedmodels
Feedback-controlmodels
Systemoptimal
Dataandcontrol
environment
Verylimiteddata
Staticdata
Empiricaldata
Basiccontrol
Sampledandarchiveddata
Automatedtrafficmanagement
Indirect
andunidirectional
commu
nication
Macroscopicdynamiccontrol
Localizedperception
Lowma
rketpenetration
Detailedreal-timeandarchived
data
Directa
ndbidirectional
commu
nication
Highmarketpenetration
High-resolutionreal-timeand
archiveddata
Userspecificcontrol
Fullornear-fullmarketpenetration
Issues
Lackofdynamicdata
Lackofdynamictheories
Suitablefordesignand
planning,butnotreliablefor
operations
Planningmodelslackaclear
relationshiptotrafficflow
theory
Norepresentationof
interactionatintersections
Limited
coverage(spatial/temporalor
both)
Limited
accuracy
Limited
resolution
Heavyd
ataprocessing
Comple
xdatafusionand
integration
Stronginteractions(V2V,
V2I,
andI2I)
Userinterface
Needto
accommodatethe
transitionfromautonomous
vehicle
tofullycontrolled
vehicles
Privacy
andsystemsecurity
(Hubau
xetal.,
2004)
Datamining
onmassivedata
Integrationwithexisting
information
andcontrolsystems
Systemrelia
bilityandrobustness
User-oriente
dservices
Stochasticd
emandmanagement
Privacyand
systemsecurity
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230 B. RAN ET AL.
Table2
Applicationsinorexpected
ineachgenerationoftransportationmodels.
Firstgeneration
(1950s1980s)
Secondgeneration(1980s2000s)
Thirdgen
eration(2000sfuture)
Fourthgeneration(2000sfuture)
Applicationareas(expand)
operations,p
lanning,
control,
behavior,design,
safety
HighwayCapacityManual
(1
950,1965,1985)
TrafficFlowMonograph
(1
964,1975)
A
PolicyonGeometric
D
esign(AASHTO,1
984,
1990,1
994)
TrafficFlowFundamentals
(M
ay1990)
Lo
ng-rangeforecasts
Rampcontrol
Se
quentialmodels
En
tropymodels(Wilson,
1970
Aggregatedzone-based
m
odels
Ea
rlygravitymodels(1955)
Diversioncurves)
Basicfundamentaldiagrams
Statisticalfeaturesoftraffic
statevariables
Webstersmodels(1958)
Kinematicwavemodels
(1
955)
Ea
rlyCar-followingmodels
(1
950s)
Ag
gregatetrip-generation
m
ethods
BPRgravityandtraffic
assignmentmodels(1960s)
(B
rokke,1969)
TR
ANPLAN(1960s,Chang
etal.,
1988)
UTPS(1970s,Dial,1976)
HighwayCapacityManual(2000,2010)
TrafficFlowM
onograph(2001)
TravelTimeD
ataCollectionHandbook
(2001)
HighwaySafe
tyManual(2010)
APolicyonG
eometricDesign
(AASHTO,2
001,2004,2
011)
MUTCD(196
1,1
971,
1978,
1988,2000,
2003,2009)
High-ordercontinuummodels
Kineticmodels
Trafficstateestimationmodels
Trafficcontrolmodels
Incidentdetection,duration,andimpact
models(Payn
eetal.
1978,Khattaketal.
1995)
TRIPS(1970s
)
Multinomiallogitandnestedlogitmodels
Household-basedtripgeneration
Dynamictraffi
cassignment
Stochastictrafficassignment
DYNASMART(1992)
VISSIM/VISU
M(1992),M
ITSIM(1996),
PARAMICS(1997),A
IMSUN(1997),
CORSIM(19
98)
FREFLOW(1
979),METANET(1992),
KRONOS(19
84)
SATURN(1986),
TRANSIMS(1995)
CUBE,
EMME/2,
TransCAD,
VISUM
(Florian1999
,2008,S
lavin,2004)
SCOOT(1980
s),S
CATS(1982),
SIDRA(Akce
lik)(1983)
Futureversionsofpreviousdocuments
(e.g.,
HCM,H
SM,
TrafficFlow
Monograph
etc.)
OperationsModels
VariationalModels
ActiveTrafficControlModels
RHODES
(MirchandaniandLucas,
2001)
PlanningMod
els
IBM:SmartPlanet(2008)
Activity-b
asedmodels
DYNAST
DYNAMEQ(2001)
AMOS(1
998)
SACSIM
(2008)
Vovsha(2
004)
URBANS
IM(1998)
Behavioralmodels
SafetyModels:
Driversim
ulators
Dynamictraffi
ccontrol
Planningmodels,demandmodels,
supplymodels
Behavioralmodels
Microscopictrafficcontrol
Note.
Theyearinparenthesesindicate
stheyearoftheoccurrenceofamodelordocument.
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PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 23
Figure 1 Timeline of major stages and applications of four generations of transportation models (HCM: Highway Capacity Manual, TFT: Traffic Flow Theor
Monograph, HSM: Highway Safety Manual).
Figure 2 Practical requirements over each generation of transportation models.
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232 B. RAN ET AL.
Table3
Evolutionofmethodologyfordifferentmodels.
Firstgeneration
Secondgeneration
Thirdgeneration
Four
thgeneration
Microscopic(individual)
C
arfollowing(CategoryA,verbars
e
tal.,
1951;Hermanetal.,
1958)
Discre
techoicemodels(CategoryA,
Ben-A
kivaetal.
1985;Bhat&
Koppelman,
1993;Koppelman&Wen,
2000)
Microscopictrafficsimulationmodels
(CategoryB,C
ORSIM,P
ARAMICS,
VISSIM,
AIMSUN)
Lane-c
hangingmodels(CategoryA,
Gipps
,1986)
Activitymodels(CategoryA,
Vovshaetal.,
2004a,2004b)
[Vehicleinteractionmodels]
(CategoryA)
[Semi-autonomousvehicle
cha
racteristics](CategoryA)
[Microscopiccontrolmodels]
(CategoryB)
Automatedv
ehiclecharacteristics]
(A)
[Automated
vehiclecontrol](A)
Mesoscopic(disaggregated)
Platoonanalysis(CategoryA,
T
reitereretal.,
1973)
Mesos
copictrafficsimulationmodels
(CategoryB,D
YNASMART,
Huetal.,
1992)
Householdmodels(A)
[Platooncharacteristicsof
automatedv
ehicles](A)
Macroscopic(aggregated)
Fundamentaldiagrams(Category
A
,Greenshields,1935)
LWRmodels(CategoryA,
L
ighthill&Whitham,1
956)
In
tersectiondelaymodel
(CategoryA,Webster,1
958)
Z
onalmodels(CategoryA)
G
ravitymodels(CategoryA,
V
oorhees,1955;Sen&Smith,
1
995)
Planningsimulationmodels
(CategoryB,
TRANPLAN,
U
TPS)
Trafficassignmentmodels
(CategoryA,Beckmannetal.,
1
956;Sheffi,1
985;Patriksson,
1
994;Mertz,
1961)
High-ordercontinuummodels(Category
A,Payne,1971;Whitham,1
974)
Celltransmissionmodels(CategoryD,
Daganzo,1
993)
Dynam
ictrafficassignment(CategoryA,
Merchant&Nemhauser,1
978a,1978b;
Ran&
Boyce,
1996;Mahmassanietal.,
1984,
1986)
Macro
scopictrafficcontrolmodels
(CategoryB,Papageorgiou,1983)
Macro
scopictrafficsimulationmodels
(CategoryB,Messmer&Papageorgiou,
1992;
Michalopoulos,1984)
[Ac
tivetrafficanddemand
ma
nagement](CategoryB)
[Integrationmodelswith
micro-andmeso-models]
(CategoryB)
[Co
nnectedvehicledata-based
ma
croscopicmodels]
(CategoryD)
[Macroscopiccontrolover
automatedv
ehicles](B)
Note.
Itemsinsqauarebracketsindica
tetheexpectedcontributions.
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PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 23
identified and theoretical and empirical studies are initiated
for the new generation of models. As the technologies become
ready, practical demand on the new-generation model starts to
increase rapidly, until the modeling research catches up and be-
comes mature for field evaluation and deployment. Then the
practical requirement turns to the technological side; hence its
needs on the research and modeling side will slow down. Mean-while, at the same period, the demand for a newer generation of
models will emerge again. The practical requirement on an old
generation of models will continue to exist but will eventually
fall below the demand on the new generation of models. Like-
wise, practical demand also changes from generation to gener-
ation. In the first generation, the main practical demand is to
obtain basic knowledge and techniques to understand, manage,
and control the transportation system. Practical motivations are
behind the second-generation model. With new ITS technolo-
gies recently, we are on the verge of the rapid development of
the third generation of transportation models and the preparation
period for the fourth-generation models.
Table 4 investigates the detailed aspects of the motivationsbehind each generation of models. Four important aspects, data,
communication, methodology, and technology, are discussed.
Communication is discussed separately from technology be-
cause of its importance with regard to traveler information and
traffic control.
Figure 3 illustrates the advances of data collection techniques
from generation to generation in terms of the detection grid with
respect to space and time with the evolution of ITS technolo-
gies. In the first generation, due to the technology limitations,
only very limited data could be collected at very scattered time
and space points either through labor-intensive data collection
methods or under controlled experimental environments. As
detection technologies advanced into the second generation,
continuous detection grids were established over some road
sections or time intervals, with the addition of partial trajectory
data provided by probe vehicle technologies. In the third gen-
eration, when the connected vehicle technologies become more
sophisticated, the density of the continuous detection grids will
increase and more complete individual trajectory data can be
collected. In the fourth generation, when full penetration can be
achieved over the entire transportation system, a more complete
and dense detection grid can be achieved.
Communication technologies also change significantly from
generation to generation. In the first generation, only very loose
communication existed from infrastructure to vehicles throughcontrol devices. In the second generation, with the emergence
of regional traffic management centers (TMCs), infrastructure
served an intermediate communication layer. Dynamic condi-
tions in transportation systems were collected and processed
through the infrastructure to the TMCs. The TMCs analyzed the
data and implemented control strategies or guidance through the
infrastructure back to the users. A representative system of such
has been evaluated in the ADVANCE project (Boyce, 2002).
In the third generation, transportation systems take advantage
of the connected vehicle technologies (RITA, 2011) to add the
additional bidirectional communication among neighboring ve
hicles, between vehicles and infrastructures, and possibly with
the TMCs. As the entire system becomes more complex and au
tomated, in the future it may be expected that communication i
transportationsystems will have flatter or more distributed struc
tures by technologies such as distributed(Attiya et al., 2004) an
cloud computing (Armbrust et al., 2009). Then each vehicleinfrastructure, or a TMC becomes one node in a large trans
portation cloud. Such trends can potentially reshape the fun
damental characteristics of transportation systems. Users wil
change from being completely unorganized individuals to bein
more coordinated, more actively involved in the perception, op
timization, and feedback of the entire system. Moreover, user
may also be individually served based on their specific need
(Figure 4).
Another interesting phenomenon to be expected is that th
information provided to users evolves from little in the firs
generation, then increases over the second and third generation
but may decrease towards the end of the third generation; in th
fourth generation, users will receive much more precise anconcise information processed, filtered, and optimized by th
infrastructure or TMCs, as illustrated in Figure 5.
For the first-generation models, the major motivation of trans
portation research was how to understand the basic character
istics of transportation systems using limited field data or dat
collected in experimental environment. Empirical models an
models from other fields (e.g., physics and economics) wer
widely introduced into the transportation field by assuming th
similarities between transportation systems and other physica
or economic systems were studied. In the second generation, th
major motivation is the crisis of transportation supply not bein
able to handle the ever-growing demand. With improved fiel
data quality, two major directions can be observed in the trans
portation research. One was to address the discrepancy betwee
field observations and the phenomenon predicted by empirica
and borrowed models in the first generation. The other was to
explore dynamic models so that the state of a transportatio
network can be estimated, predicted, or controlled with respec
to the demand changes. In the third and fourth generation, th
issue of supply falls behind demand is still the main. For third
generation models, based on a much richer data environmen
major motivations may be the capability of processing high
resolution real-time data for real-time route guidance and traffi
control strategies. Meanwhile, it is also necessary to explore th
impact of the increased perception of travelers and the strengthened interaction among entities (vehicle, driver, infrastructure
and other modes) in transportation systems. In the fourth gener
ation, the motivations become the ability to process large-scal
and massive data in real-time and to provide user-specific con
trol and guidance for fully automated traveling. Moreover, a
each component of a transportation system (travelers, passen
ger vehicles, public transportation systems, freight transporta
tion, and parking) has been studied intensively in the previou
generations, integrated models that consider all transportatio
modes, involve all parties (users, planning agencies, operators
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234 B. RAN ET AL.
Table4
Motivationsbehindeachge
nerationoftransportationmodels.
Firstgeneration
Secondgeneration
Thirdg
eneration
Fourthgeneration
Data
Aggregatedtr
afficflowcharacteristics
Pointdetectordata
Experimental
andtest-trackdata
Survey-based
statictravelbehaviordata
Staticandlon
g-termdemanddata
Archivedhistoricaldata
Segment-based
detectors
Enhanced(e.g.,h
igh-resolution)point
detectordata
Probevehicled
ata
Dynamicbehaviordata(e.g.,
GPS/cell-phon
etravelsurvey)
CCTVsurveillancevideo
Trafficoperatio
nsandmanagementdata
(e.g.,weather,
workzone,
incidents)
Comprehensivecrashdata
High-resolutionpartialvehicle
trajectorydata
Arterialtrafficnetworkdataandsignal
timingdata
Dynamictraveldemanddata
Real-timelocaltrafficcondition
(throughconnectedvehicles)
Dynamicmultimo
daldataVehicular
networkdata
High-resolutionfullvehicletrajectorydata
High-resolutionsens
ornetwork
Real-timetravelerse
rvicerequest
Real-timetransportationservicedata
Communication
Looseconnec
tivity
Unidirectional/indirectconnectivity
Connectivityrelia
bility
Informationreduc
tion
Connectivitysecu
rity
Massiveconnectionprocessing
Informationpriority
andcompression
Informationsecurity
Methodology
Theoriesandmodelsborrowedfrom
otherfields(e.g.physics,economics)
Basictransportationobservations
Stochasticand
dynamiccharacteristics
oftransportation
Dynamiccontrolstrategies
Real-timemodels
Combinedhuman
,assisted,and
automatedtraveling
Integrationoftheoryandmodels
developedforsubproblem(e.g.
combiningthesimulationof
operationsandplanning)
Networkandlarge-scalesolutionsto
existingtheoryandmodels
Real-timelarge-scaleoptimization
User-specificmodels
Technology
Testvehicletechniques
Manuallicenseplatesurveys
Uniformtraffi
ccontroldevices
Inductiveloop
detectors
GPStechnolog
ies
Wirelesslocationtechnologies
Videodetectiontechnologies(e.g.,
Autoscope)
Probevehiclet
echnologies
Trafficcontrol
centertechnologies
GIStechnologies
ITSstandards
Connectedvehicletechnologies
Smartvehicletechnologies
Socialmediaapplications
Massivedataprocessing
Richandeffectiveuserinterface
Cloudcomputing
Distributedcomputing
Supercomputer
Newformsofpublic
transportation
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PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 23
Figure 3 Advances in transportation data collection methods.
and policies), and serve multiple objectives (efficiency, safety,
and sustainability impact) can be expected. When obtaining not
only operational but also demand data becomes more efficient,
these new integrated models will play important roles in future
transportation system.
One critical step of transportation research is the mode
validation and verification. Transportation models are not ap
plicable without proper calibration and validation using fiel
data. Many transportation models in the first and second gener
ation are presented initially with very limited field data suppor
Figure 4 Changes in communication in transportation systems.
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236 B. RAN ET AL.
Figure 5 Information provided to users in different generations.
However, those successful models were later on intensively val-
idated by other researchers and engineers using field data. With
the development in ITS technologies, benchmark field data sets
have been established in many transportation research fields for
the second- and third-generation models. Examples include the
NGSIM data set (FHWA, 2012) for research on traffic flow the-
ory and the transportation testing problem data sets (Bar-Gera
2011) for network modeling. Yet for testing many transportationmodels, direct and comprehensive data sets are not always avail-
able, including examples such as data sets for traffic flow and
network dynamics in arterial network, data sets for traffic di-
version on freeway, and drivers reaction toward route guidance
and dynamic traffic messages. In the future, with the develop-
ment of new ITS technologies, innovative ways of collecting
and using traffic data may be proposed and optimized. The time
duration from the proposal of a model to its field validation
can be significantly shortened. Meanwhile, comprehensive sce-
narios can be selected to verify new models thoroughly. Some
difficulties may rise in processing and filtering the data to fit the
proposed model, developing efficient optimization algorithms
for model calibration, and finding effective ways of interpreting
the results. For example, with a large amount of high-resolution
data, it can be difficult to validate some macroscopic models, as
researchers need to reconstruct the required inputs and ground
truth data. It may also be possible that some old models become
inaccurate, ineffective, or even useless with the new data sets.
APPLICATIONSOF TRANSPORTATIONMODELS
In this section, detailed observations regarding the applica-
tions of modelsin each generation areoffered. This discussion is
one of the first attempts to depict such a detailed picture of ma-jor topics and applications associated with major transportation
models. Admittedly, the detailed classifications and descriptions
may not be highly accurate and are subject to changes over time.
The primary goal is to present the trend of modeling ideas and
ways of thinking from generation to generation in more con-
crete and specific scenarios other than generation descriptions
in the previous sections. The first scenario is based on different
subareas of transportation research (Table 5).
Research on operational models focuses on two major di-
rections, traffic characteristics and traffic control. The first one
is to develop more sophisticated models that can capture the
actual characteristics of real-world transportation system, from
the ideal models such as car following models, intersection de-
lay models, and kinematic wave models to more complicated
higher order models, such as kinetic models that can describe
nonequilibrium traffic states observed in field data. In the third
and fourth generations, the modeling efforts will need to be fo-cused on vehicle-oriented and control-oriented studies as more
detailed vehicular data and smart infrastructure data become
available. An equally important track for this direction is the
study on the performance evaluation models for traffic flow.
Such a track includes the study of single-variable characteristics
such as sample-data-based speed, headway distribution (May,
1990), and fundamental diagram characteristics (e.g. early edi-
tions of Highway Capacity Manual) in the first generation, as
well as more data-centric measures such as traveltime reliability
(Higatani et al., 2009; Uno et al., 2009) and traffic contour maps
(e.g. HCM 2000) in the second generation that require more
advanced modeling and process efforts. It can be expected that
in the future, when new technologies and data sources becomeavailable, more detailed and informative measures of transporta-
tion systems may emerge and become applicable in practice.
The other major research direction is traffic control and man-
agement. It evolved from the static pretimed signal control in
the first generation to corridor or network-wide adaptive control
in the second generation, then to active traffic management and
control in the third generation. With more dynamic and more de-
tailed data available, as well as innovations in new methodology
and technology, the corresponding control models have become
more and more adaptive, real-time, optimal, and concrete. In the
future, with the development in connected vehicle or Internet of
Vehicles technologies, the control technologies targeting indi-
vidual vehicles or travelers may become feasible. Then efficient
microscopic optimal control models will be needed.
Four major trends may be expected for planning models.
First, the specificity of the model output has increased from
generation to generation, from the static 24-hour single-class
output of the first generation to the dynamic in the second gen-
eration and real-time in the fourth generation, resulting in more
operational models. Second, the models have become more
and more disaggregated as more travel details are reflected in
transportation data. Third, the network representation used by
planning models contains more details with respect to vehicle
classes, lane configuration, and demand and supply changes.
Fourth, sustainability can also be enhanced in future transporta-tionplanning. Sustainable solutionshave drawn increasing inter-
ests in recent years (Black et al., 2002; Jeon & Amekudzi, 2005;
Richardson, 1999,2005). With increased connectivity among all
transportation modes, more customized, efficient, flexible, and
compelling (with regard to auto mode) solutions may emerge in
the future.
Design models may become intensively integrated into the
entire life cycle of transportation systems. In safety research,
the trend has been toward more comprehensive data analysis,
and more proactive measures and countermeasures. Ultimately,
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PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 23
however, in the fourth generation, safety models may become
more of a technological issue than data analysis, since more ad-
vanced vehicle and infrastructure control and automated driving
can potentially reshape the entire concept of traffic safety. Envi-
ronmental models can also benefit from the increased availabil-
ity of data and improvement in clean energy technologies.
Dividing transportation models by their scope is one of themost important classification scenarios in the field (Table 6). In
this discussion, transportation models, primarily operations and
planning models, have been divided into microscopic, meso-
scopic, macroscopic, and metascopic models for each genera-
tion. In general, one can expect more concrete, dynamic esti-
mation and control models to increase from the first generation
to the fourth generation. In the first and second generation,
microscopic models were primarily descriptive models. How-
ever, in the third and fourth generations, microscopic control
and management models may also be developed. In metas-
copic models, an important trend is that the decision making
has changed from a single objective to multiple objectives as
more data sources are available over the generations.
SUMMARY
With more than eight decades of development, our field has
experienced two major waves of transportation models in the
1950s to the 1990s. Major technology reforms in the automo-
bile industry and information science have respectively inspired
and motivated the previous two generations of transportation
models, along with the ever-increasing practical needs for more
efficientand productive transportation system. We are now at the
verge of the next major waves of transportation research withthe introduction of new ITS technologies including wireless
communication technologies, connected vehicle technologies,
smart vehicle technologies, and distributed and cloud comput-
ing technologies. These new ITS technologies can fundamen-
tally change the characteristics of existing transportation sys-
tem with increased connectivity, automation, and optimization
toward a much more user-oriented, system-optimal, safe, and
sustainable system. All of these technologies open up brand
new territory to be further explored, discovered, and mastered.
The discussion presented in this paper serves as the first step
in inspiring and motivating transportation researchers toward
a future generation of transportation models that may benefit
millions of users of transportation systems.
REFERENCES
AIMSUN. (2011). AIMSUN. TSS (Transport Simulation Systems).
Retrieved from http://www.aimsun.com/site
American Association of State Highway and Transportation Officials.
(1984, 1990, 1994, 2001, 2004). A policy on geometric design of
highways and streets. Washington, DC: AASHTO.
American Association of State Highway and Transportation Official
(2010).Highway safety manual. Washington, DC: AASHTO.
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R. H
Konwinski, A., Lee, G., Patterson, D. A., Rabkin, A., Stoica, I., &
Zaharia,M. (2009).Above the clouds: A Berkeley view of cloud com
puting(No. UCB/EECS-200928). Berkeley: EECS Departmen
University of California.
Attiya, H., & Welch, J. (2004).Distributed computing: Fundamentals
simulations, and advanced topics. Hoboken, NJ: Wiley-Interscienc
Autoscope. (2011). AutoscopeVideo detection solutions to im
prove traffic flow and roadway safety, vol. 2011. Available a
http://www.autoscope.com.
Bar-Gera, H. (2011). Transportation test problems. Retrieved from
http://www.bgu.ac.il/bargera/tntp
Beckmann, M., McGuire, C. B., & Winsten, C. B. (1956). Studies i
the economics of transportation. New Haven, CT: Yale Universit
Press.
Ben-Akiva, M., & Lerman, S. (1985). Discrete choice analysis. Cam
bridge, MA: MIT Press.
Bhat, C. R., & Koppelman, F. S. (1993). A conceptual framewor
of individual activity program generation. Transportation Researc
Part A,27(6), 433446.
Black, J. A., Paez, A., & Suthanaya, P. A. (2002). Sustainable ur
ban transportation: Performance indicators and some analytical ap
proaches. Journal of Urban Planning and Development, 128(4
184209.
Boyce, D. E. (ed.). (1985) Transportation research: The state of the a
and research opportunities.Transportation Research Part A, 19(5/6
349542.
Boyce, D.E. (2002). A Memoir of the ADVANCE project. Journal o
Intelligent Transportation Systems,7(2), 105130.
Bradley, M., Bowman, J. L., & Griesenbeck, B. (2009). SACSIM
An applied activity-based model system with fine-level spatial an
temporal resolution.Journal of Choice Modeling,3(1), 531.
Brokke, G. E. (1969). Urban transportation planning computer sys
tem. In U.S. Department of Transportation (Ed.), Urban plannin
system 360, traffic assignment and peripheral programs (3268
Washington, DC: Federal Highway Administration.
Caliper.(2011). TransCAD transportation planning software overview
Retrieved from http://www.caliper.com/tcovu.htm
CASAGRAS Research. (2010). Final report: RFID and the inclu
sive model for the Internet of Things. EU project number 216803
Retrieved from http://www.grifs-project.eu/data/File/CASAGRA
%20FinalReport%20(2).pdf
Chang, D. M., Stover, V. G., & Dresser, G. B. (1988). Detailed evalu
ation of the TRANPLAN package of microcomputer programs. Co
lege Station, TX: Federal Highway Administration.
Citilabs. (2011). Cube | Citilabs. Retrieved from http://www.citilab
com/products/cubeClarke, K. C. (1986). Advances in geographic information sy
tems. Computers, Environment and Urban Systems, 10(34
175184.
Collier, W. C., & Weiland, R. J. (1994). Smart cars, smart highways
Spectrum, IEEE,31(4), 2733.
Control Data Corporation. (1964). Users manual, T/P transportatio
planning system for the control data 3600 computer, Applicatio
program #7. Minneapolis, MN: Data Centers Division.
Daganzo, C. F. (1993). The cell transmission model: a dynamic rep
resentation of highway traffic consistent with the hydrodynam
theory. Transportation Research Part B,28(4), 269287.
intelligent transportation systems vol. 16 no. 4 2012
-
8/10/2019 Perspectives on Future Transportation Research_Impact of Intelligent Transportation System Technologies on Next
16/18
240 B. RAN ET AL.
Daganzo, C. F. (2005a). A variational formulation of kinematic waves:
basic theory and complex boundary conditions.Transportation Re-
search Part B,39(2), 187196.
Daganzo, C. F. (2005b). A variational formulation of kinematic
waves: Solution methods.Transportation Research Part B,39(10),
934950.
Dial, R.B. (1976)The urban transportationplanningsystem: UTPS phi-
losophy and function.Transportation Research Record,619, 4348.
Dial, R. B., & Bunyan, R. E. (1968) Public transit planning system.
Socio-Economic Planning Sciences,1, 345362.
Federal Highway Administration. (2011a). TRANSIM introduc-
tion, travel model improvement program. Retrieved from
http://tmip.fhwa.dot.gov/content/619
Federal Highway Administration. (2011b).TSIS: Traffic software inte-
grated system. Retrieved from http://mctrans.ce.ufl.edu/featured/tsis
Federal Highway Administration. (2012.Home of the next generation
simulation community. Retrieved from http://ngsim-community.org
Florian, M. (1999) Untangling traffic congestion. ORMS Today,26(2).
Florian, M. (2008) Models and software for urban and regional trans-
portation planning: Contributions of the CRT. INFOR, Information
Systems and Operational Research,46, 2950.
Gage, R. W., & McDowell, B. D. (1995). ISTEA and the tole of MPOs
in the new transportation environment: A midterm assessment. Pub-
lius: The Journal of Federalism,25(3), 133154.
Gerlough, D. L., & Capelle, D. G. (1964). An introduction to traffic
flow theory. Washington, DC: Highway Research Board.
Gerlough, D. L., & Huber, M. J. (1975). Traffic flow theory: A mono-
graph. Washington, DC: Transportation Research Board, National
Research Council.
Gipps, P. G. (1986). A model for the structure of lane-changing deci-
sions.Transportation Research Part B,20(5), 403414.
Greenshields, B. D. (1935). A study of traffic capacity. Highway Re-
search Board Proceedings,14, 448477.
Gudmundsson, H. (2000). Indicators and performance measures for
transportation, environment and sustainability in North America.
Roskilde, Denmark: National Environmental Research Institute.
Herman, R., Montroll, E. W., Potts, R. B., & Rothery, R. W. (1958).
Traffic dynamics: Analysis of stability in car following.Operations
Research,6(2), 165184.
Higatani, A., Kitazawa, T., Tanabe, J., Suga, Y., Sekhar, R., & Asakura,
Y. (2009). Empirical analysis of travel time reliability measures in
Hanshin expressway network.Journal of Intelligent Transportation
Systems,13(1), 2838.
Highway Research Board. (1965).Highway capacity manual. Wash-
ington, DC: Author.
Hu, T.-Y., Rothery, R. W., & Mahmassani, H. S. (1992).
DynaSmartDynamic network assignment-simulation model for
advanced road telematics. Austin: Center for Transportation Re-
search, University of Texas, Austin.Hubaux, J. P., Capkun, S., & Jun, L. (2004). The security and privacy
of smart vehicles.Security & Privacy, IEEE,2(3), 4955.
IBM. (2011).SmartPlanetInnovative ideas that impact your world.
Retrieved from http://www.smartplanet.com
Int Panis, L., Broekx, S., & Liu, R. (2006). Modelling instantaneous
traffic emission and the influence of traffic speed limits.Science of
the Total Environment,371(13), 270285.
Jeon, C. M., & Amekudzi, A. (2005). Addressing sustainability in
transportation systems: definitions, indicators, and Metrics.Journal
of Infrastructure Systems,11(1), 3150.
Khattak, A. J., Schofer, J. L., & Wang, M. (1995). A simple time se-
quential procedure for predicting freeway incident duration.Journal
of Intelligent Transportation Systems,2(2), 113138.
Koppelman, F. S., & Wen, C-H. (2000). The paired combinatorial
logit model: Properties, estimation and application. Transportation
Research Part B,34(2), 7589.
Kristensson, A., Johansson, C., Westerholm, R., Swietlicki, E., Gid-
hagen, L., Vesely, V. (2004). Real-world traffic emission factors of
gases and particles measured in a road tunnel in Stockholm,Sweden.
Atmospheric Environment,38(5), 657673.
Lighthill, M. J., & Whitham, G. (1955). On kinematic waves. I. Flood
movement in long rivers. Proceedings of the Royal Society of Lon-
don, Series A, Mathematical and Physical Sciences, 229(1178), 281.
Lighthill, M. J., and Whitham, G. B. (1956). On kinematic waves.
II. A theory of traffic flow on long crowded roads. Proceedings of
the Royal Society of London. Series A. Mathematical and Physical
Sciences,229(1178), 317.
Merchant, D. K., & Nemhauser, G. L. (1978a). A model and an algo-
rithm for the dynamic traffic assignment problems.Transportation
Science,12, 183199.
Merchant, D. K., & Nemhauser, G. L. (1978b). Optimality conditions
for a dynamic traffic assignment model.Transportation Science,12,
200207.
Mahmassani, H., & Herman, R. (1984) Dynamic user equilibrium de-
parture time and route choice on idealized traffic arterials. Trans-
portation Science,18, 362384.
Mahmassani, H. S., Chang, G.-L., & Herman, R. (1986) Individual
decisions and collective effects in a simulated traffic system. Trans-
portation Science,20, 258271.
May, A. D. (1990).Traffic flow fundamentals. Upper Saddle River, NJ:
Prentice Hall.
Mertz, W. L. (1961) Review and evaluation of electronic computer
traffic assignment programs.Bulletin 297, Highway Research Board
Bulletin,297, 94105.
Messmer, A., & Papageorgiou, M. (1992). METANET: A macroscopic
simulation program for motorway networks. Traffic Engineering and
Control,31, 466470.
Michalopoulos, P. G. (1984). A dynamic freeway simulation pro-
gram for personal computers. Transportation Research Record, 971,
6879.
Meyer, M. D., & Miller, E. J. (2001). Urban transportation planning:
A decision-oriented approach. New York, NY: McGraw-Hill.
Michalopoulos, P. G. (1991). Vehicle detection video through image
processing: The Autoscope system.IEEE Transactions on Vehicular
Technology,40(1), 2129.
Mirchandani, P. B., & Lucas, D. E. (2001). RHODESITMS Tempe
field test project: Implementation and field testing of RHODES,
A real-time traffic adaptive control system, final report 447 (no.
FHWA-AZ01447). Tucson: ATLAS Research Center, Universityof Arizona, Federal Highway Administration.
MITSIM. (2011). MITSIM. MIT Intelligent Transportation Systems.
Retrieved from http://mit.edu/its/mitsimlab.html
NAVTEQ. (2011). NAVTEQ maps and traffic. Retrieved from http://
www.navteq.com/#highlight
Network, D. B. (2011). Mobile synergetics: Connected vehi-
cles & intelligent transportation systems. Retrieved from http://
mobilesynergetics.com
Office, N. C. (2011). What is GPS? Retrieved from http://www.gps.
gov/systems/gps
intelligent transportation systems vol. 16 no. 4 2012
-
8/10/2019 Perspectives on Future Transportation Research_Impact of Intelligent Transportation System Technologies on Next
17/18
PERSPECTIVES ON FUTURE TRANSPORTATION RESEARCH 24
Papageorgiou, M. (1983).Applications of automatic control conceptsto
traffic flow modeling and control. New York, NY: Springer-Verlag.
Patriksson, M. (1994) The traffic assignment problemModels and
methods. Utecht, The Netherlands: VSP.
Payne, H. J. (1971). Models of freeway traffic and control.Mathemat-
ical Models of Public Systems,1, 5161.
Payne, H. J. (1979). FREFLOW: A macroscopic simulation model of
freeway traffic.Transportation Research Record,722, 6877.
Payne, H. J., & Tignor, S. C. (1978). Freeway incident-detection algo-
rithms based on decision trees with states. Transportation Research
Record,682, 3037.
Pendyala, R. M., Kitamura, R., & Reddy, D. V. G. P. (1998). Ap-
plication of an activity-based travel-demand model incorporating a
rule-based algorithm. Environment and Planning B: Planning and
Design,25(5), 753772.
Pipes, L. A. (1951).A proposed dynamic analyogy of traffic. ITTE Re-
port, Institute of Transportation and Traffic Engineering, Berkeley:
University of California.
PTV-AG. (2011).PTV AG: VISSIMMulti-modal traffic flow model-
ing. Retrieved from http://www.ptvag.com/software/transportation-
planning-traffic-engineering/software-system-solutions/vissim
Qiu, Z., Jin, J., Cheng, P., & Ran, B. (2007). State of the art
and practice: Cellular probe technology applied in advanced
traveler information systems. Paper presented at the Trans-
portation Research Board 86th Annual Meeting. Retrieved from
http://pubsindex.trb.org/orderform.html
Quadstone. (2011).Quadstone Paramics | Cutting edge traffic simula-
tion solutions. Retrieved from http://www.paramics-online.com
Quiroga, C. A., & Bullock, D. (1998). Travel time studies with
global positioning and geographic information systems: An inte-
grated methodology. Transportation Research Part C, 6(1), 101
127.
Ran, B., & Boyce, D. E. (1996). Modeling dynamic transportation
networksAn intelligent transportation system oriented approach.
Berlin, Germany: Springer.
Rebolj, D., & Sturm, P. J. (1999). A GIS based component-oriented
integrated system for estimation, visualization and analysis of road
traffic air pollution.Environmental Modelling and Software,14(6),
531539.
Richardson, B. C. (1999). Toward a policy on a sustainable trans-
portation system. Transportation Research Record: Journal of the
Transportation Research Board,1670(1), 2734.
Richardson, B. C. (2005). Sustainable transport: Analysis frameworks.
Journal of Transport Geography,13(1), 2939.
RITA. (2011). Connected vehicle technologies. Research and Inno-
vative Technology Administration (RITA), U.S. Department of
Transportation. Retrieved from http://www.its.dot.gov/connected
vehicle/connected vehicle.htm
Ryan, J. M. (1979). Urban transportation planning system (UTPS):The community aggregate planning model (CAPM) users guide.
Washington, DC: Federal Highway Administration Urban Planning
Division.
SATURN. (2011). SATURN software. Retrieved from http://www.
saturnsoftware.co.uk
SCATS. (2011). SCATSBase package. Retrieved from http://
www.scats.com.au/product base packg compnts.html
SELNEC Transportation Study. (1973) Computing Procedures. Tech-
nical working paper no. 11. Manchester, UK: Author.
Sen, A. K., & Smith, T. E. (1995). Gravity models of spatial interaction
behavior. New York, NY: Springer.
Sheffi, Y. (1985). Urban transportation networks:Equilibrium analys
with mathematical programming methods. Englewood Cliffs, NJ
Prentice Hall.
SIAS. (2011). SIAS transport planners. Retrieved from http://www
sias.com/ng/sparamicshome/sparamicshome.htm
SIDRA. (2011). SIDRA solutions. Retrieved from http://www
sidrasolutions.com
SIEMENS. (2011). SCOOTAdaptive traffic management solution
Retrieved from http://www.itssiemens.com/en/t nav124.html
Slavin, H. L. (2004) Therole of GIS in land use andtransport planning
InHandbook of transport geography and spatial systems, ed. D. A
Hensher, K. J. Button, K. E. Haynes, and P. Stopher, 329356
Amsterdam, the Netherlands: Elsevier.
Transportation Research Board. (1985, 2000, 2010).Highway capaci
manual. Washington, DC: Author.
Treiterer, J., Nemeth, Z., & Vecellio, R. (1973), Effect of sig
nal spacing on platoon dispersion: Final report. Columbus, OH
U.S. Department of Transportation, Federal Highway Adminis
tration, and the Ohio Department of Transportation. Retrieve
from http://www.tft.pdx.edu/greenshields/docs/Treiterer Platoon
Dispersion.pdf
Uno, N., Kurauchi, F., Tamura, H., & Iida, Y. (2009). Using bus prob
data for analysis of travel time variability. Journal of Intelligen
Transportation Systems,13(1), 215.
UrbanSIM. (2011). UrbanSIM. Retrieved from http://www.urbansim
org/Main/WebHome
U.S. Department of Commerce. (1950). Highway capacity manua
Washington, DC: Bureau of Public Roads.
U.S. Department of Commerce. (1963). Calibratingand testing a grav
itymodel for any size urbanarea. Washington, DC:Bureau of Publi
Roads.
U.S. Department of Commerce. (1964). Traffic assignment manua
Washington, DC: Bureau of Public Roads.
U.S. Department of Transportation. (1969). Urban planning system
360, trip distribution and peripheral programs, and traffic assign
ment and peripheral programs. Washington, DC: Federal Highwa
Administration.
U.S. Department of Transportation. (1961, 1971, 1978, 1988, 2000
2003, 2009). Manual on uniform traffic control devices for street
and highways. Washington, DC: Federal Highway Administration
U.S. Department of Transportation. (1972a). Urban transportatio
planning, General information. Washington, DC: Federal Highwa
Administration. Updated and reissued periodically.
U.S. Department of Transportation. (1972b). U.M.T.A. transportatio
planning systemReference manual. Washington, DC: Urban Mas
Transportation Administration.
U.S. Department of Transportation. (1973). Traffic assignment, meth
ods, applications, products. Washington, DC: Prepared for the Fed
eral Highway Administration by Comsis Corporation.U.S. Department of Transportation. (1974). Urban transportation plan
ning program documentation. Washington, DC: Federal Highwa
Administration.
U.S. Department of Transportation. (1977). User-oriented material
for utps, An introduction to urban travel demand forecasting. Wash
ington, DC: Federal Highway Administration, Urban Mass Trans
portation Administration.
U.S. Department of Transportation. (1984). Microcomputers in trans
portation, software and source book. Washington, DC: Urba
Mass Transportation Administration. Updated annually throug
1987.
intelligent transportation systems vol. 16 no. 4 2012
-
8/10/2019 Perspectives on Future Transportation Research_Impact of Intelligent Transportation System Technologies on Next
18/18
242 B. RAN ET AL.
U.S. Department of Transportation. (1995). Detection technology:
IVHS volume 1: Final report addendum(no. FHWA-RD-96100).
Washington, DC: Federal Highway Administration.
U.S. Department of Transportation. (2011). Intelligent transportation
systems. Retrieved from http://www.its.dot.gov
US Department of Transportation. (2011). Connected vehicle. Re-
trieved from http://www.its.dot.gov/connected vehicle/connected
vehicle.htm
Van Vliet, D. (1982). SATURNA modern assignment model. Traffic
Engineering and Control,23, 578581.
Voorhees, A. M. (1955). A general theory of traffic movement. Pro-
ceedings,Institute of Traffic Engineers, New Haven, CT.
Vovsha, P., & Bradley, M. (2004). Hybrid discrete choice departure-
time and duration model for scheduling travel tours. Transportation
Research Record: Journal of the Transportation Research Board,
1894, 4656.
Vovsha, P., Bradley, M., & Bowman, J. L. (2004, May). Activity-based
travel forecasting models in the United States: Progress since 1995
and prospects for the future. Paper presented at the EIRASS Confer-
ence on Progress in Activity-Based Analysis, Maastricht, the Nether-
lands.
Weiner, E. (2009) Urban transportation planning in the United States
(3rd ed.). Berlin, Germany: Springer.
Whitham, G. B. (1974). Linear and nonlinear waves. New York, NY:
John Wiley and Sons.
Webster, F. V. (1958). Traffic signal settings. Road Research Laboratory
technical paper no. 39. London, UK: HMSO.
Wilson, A. G. (1970). Entropy in urban and regional modelling, Pion,
London.
Wilson, A. G., Hawkins, A. F., Hill, G. J., & Wagon, D. J. (1969)
Calibration and testing of the SELNEC transport model, Regional
Studies,3, 337350.
intelligent transportation systems vol. 16 no. 4 2012