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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

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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

226


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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

intelligent transportation systems vol. 16 no. 4 2012


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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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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



6/18

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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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.



9/18


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



11/18


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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14/18


15/18


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.

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