From: AAAI Technical Report WS-93-04. Compilation copyright © 1993, AAAI (www.aaai.org). All rights reserved.
AI In AdvancedTraffic Management
Systems
John F. Gilmore and Khalid J. Elibiary
Artificial Intelligence Group
Computer Science and Information Technology Lab
Georgia Tech Research Institute
Georgia Institute of Technology
Atlanta, Georgia 30332
john.gilmore@gtri.gatech.edu
Abstract
Traffic management
systemshavehistorically
beenlimited to addressing
the controlof street signal
lights. Algorithmicsolutions to this problemhave
provedto be very restrictive, while expert system
solutions haveonly shownvalid results with small
signal networks. Noneof these approacheshas
addressedthe needfor management
of the overall
transportationsystemof surfacestreets, interstate
highways,public transportation, and emergency
vehicle response. Georgia Tech has developeda
distributed blackboardsystemdesignedfor advance
traffic management
in large urbanareas. Knowledge
sourcesin the systemaddressproblemsin traffic
control, monitoring,congestionprediction, adaptive
communication, and incident management.The
knowledge
sourcesexploit rule, frame,script, and
neural networkrepresentationsto solve individual
traffic management
problemsthat appear on the
blackboarddata structure. Theresulting traffic
management
decisions are then implementedand
evaluatedthroughsimulation.
1 Introduction
The goal of an AdvancedTraffic Management
System(ATMS)is to efficiently manage
existing
transportation resourcesin responseto dynamic
traffic conditions. An advanced
traffic management
systemmustincorporateall modes
of transportation
if it is to providean effective management
solution
[Sabounghi,
1991]. For example,
the coordinationof
57
automobilescan not be addressedwithout incorporatingthe specialneedsof buses,trucks, taxi cabs
and emergency
vehicles.
A key support systemin the ATMS
architecture
is the AdvancedTraveler Information System
(ATIS).Thegoalof ATISis to provideeverytraveller
with real-time accessto informationon traffic conditions, travel options, special eventupdates,and
route informationfor a given metropolitanarea. In
addition, information on accommodations
and services wouldbe providedto travellers stayingin the
area. Inputs into ATISwouldinclude:
¯ traffic management
decisions
¯ modelpredictions
¯ traffic congestion
reports
¯ police/publicsafety reports
¯ eventschedules,attendance,& status
¯ transit schedules
andstatus
¯ airport schedulesandstatus
¯ ship line schedulesandstatus
¯ commercialtrucking
¯ commercial
traffic reports
¯ weatherreports
¯ emergency
calls
¯ incident reports
¯ traffic spotters
¯ traffic sensors
¯ video cameras
¯ cellular phones
¯ sites of interest
¯ directory of accommodations
¯ directory of service
This information provides a comprehensive
view of
the current state of the transportationenvironment
and forms the core database which ATMSwould
drawuponduring its decision-makingprocess. In
addition to supporting ATMS,
ATISwoulddirectly
supplytravellers with informationon
¯ the bestroutesto take,
¯ the best mode
of transportationavailable,
¯ the existenceandavailability of services,
¯ in-vehicle hazardwarningandroadsigning,
¯ parkingavailability, and
¯ predicted congestionareasby time of day
Utilizing the ATIS information database
described above, ATMSwill managetraffic by
makingand implementingcontrol decisions on all
aspectsof traffic flow. Decisions
will not only optimizetraffic flow, but mustalso addressanyenvironmental impacts. For example, decisions on
routing traffic throughalternate side streets must
considerthe effect automotive
pollution will haveon
that environment.
Specific ATMS
taskswill include:
¯ predictivetraffic flow modeling
¯ traffic congestionmonitoring
¯ coordinated
signalizationof surfacestreet lights
¯ rampmeteringonto congestedhighways
¯ openingof reservedlanesandreversible lanes
¯ road accessmonitoringandcontrol
¯ electronic enforcement
of traffic regulations
¯ incident detectionalgorithms
¯ requestfor police/emergency
vehicles
¯ demandmanagement
¯ automated
toll collection
An ATMS
utilizes three basic componentsto
maintainan efficient flow on roadways:[1] traffic
control, [2] incident management,
[3] andmotorist
information systems.Thesesubsystemsmustwork
in concertin orderto maintaina balanceof traffic
flow. For example,
a traffic control planis of little
value if it cannotrespondto accidents or other
incidentsthat occurandnotify motoriststo avoidthe
problemarea.
ATMS
are currently in the deploymentstage;
however,manycountries havealready developed
andinstalled the basic elementsof the management
systems.Traffic control algorithmsare nowbeyond
the stageof simpletime of daysignal plans as many
of today’s systemshaveadaptivecontrol capabilities. The SCOOT
and TRANSYT
control algorithms
are usedto minimizethe sumof averagequeues,
examine
the numberof times vehicles haveto stop,
anddetermine
the adverseeffects this hason traffic
in the region being controlled [Robertsonet al.,
1991]. TheSCOOT
systemrequires initial system
parametertuning in order assurethat its models
agreewith traffic observations.
Effectivecontrol can
thus be established with these models, however
periodic re-calibration is required to ensurethe
performanceof the system[Clowes, 1982]. The
SCOOT
systemis currently usedin Chinaand the
United Kingdom.
In Taiwan, the ComputerizedDynamicTraffic
Control Systems (COMDYCS)
were developed
the National Cheng-Kung
University. Thefirst system, COMDYCS-I
sets a signal timing schedule
basedon prevailing traffic patterns and preset
look-up tables. COMDYCS-III
utilizes stepwise
adjustmentof signal timing, along with predefined
The methodsATMS
will employto managetraffic
traffic models,to logically decideon signal timing
are numerous
andinclude signal lights, changeable plans[Chenet al., 1991].
highwaysigns, rampmetering, reversible lanes,
The Los AngelesAutomatedTraffic Surveillance
emergencyvehicle displays, in-vehicle hazard andControl (ATSAC)
systemis a responsivecontrol
warnings,and in-vehicle road signing. Figure 1.
systemusedfor signal light control [Rowe,1991].
summarizes
the ATMS/ATIS
information, data, and By comparingactual surveillance data gathered
fromthe areato availablemodeldata, a signaltiming
controlflows.
58
s
Service
Mode
Advanced
-t-raveller
tntormati°n
System
. sites
. /~ccommodati°ns
¯ serv}ceS
59
plan is selected. Future plans for ATSAC
include
development
of an expert systemwhichwill assist
in identifying nonrecurring congestionandprovide
assistance basedon previous experienceof the
mosteffective methods
of alleviating traffic for different categoriesof nonrecurringevents.
motorists of problems on their current routes
allowingdriversto seekalternateroutes[Caseet al.,
1991].
The two primary tasks of an incident managementsystemsare detectionandnotification. Traffic
incidents are typically reported by motorists or
enforcement agencies. ATMSdetection and
responsesteps, however,are moreautomated.In
1996 Montreal’s Freeway Traffic Management
Systems(FTMS)will be operational. This system
will utilize closed-circuittelevision cameras
over a
35 kilometer corridor of the city’s mostheavily
congested
freewaysfor automaticincident detection
andvariable message
sign control. FTMS
will advise
systems, each focused on a separate area of
expertise but contributing to overall systemgoals
[Nii, 1986aand 1986b]. Themaincomponents
of a
blackboardarchitecture are the central blackboard
data structure, the independent
problemsolvers or
knowledgesources, and the control module.The
central blackboard
data structureis the hierarchical
solution spacefor the problemsolving process.It
maintainsthe global information postedby knowledgesourcesthat is required to formulateproblem
solutions.
Variable message
signs are the mostcommonly
usedformof Motorist InformationSystems
currently
in use. However, more elaborate systems are
TheSydneyCoordinatedAdaptiveTraffic Sys- currentlybeingtested anddeveloped.Japan’sRoad
tem (SCATS),developedin Australian but deployed Automobile CommunicationSystems(RACS)and
in HongKongand Singapore, is one of the most AdvancedMobile Traffic Information and Commuadvancedsystemsin the world. The SCATS
pronication Systems(AMTICS)are vehicle on-board
gramuseslocal computers
to gatherreal-timetraffic
driver information systems. RACSand AMTICS
informationwhichis relayedto a central regional utilize CD-ROM
digital maps,as well as microwave
computer. The central computerthen choosesa
andcellular links to local traffic centers,to display
skeletal signal timing plan basedon the information problemsandalternate routesto drivers on monitors
gathered. However,parameterswithin these plans located inside the vehicle. Similarly, Europe’s
are adjusted dynamicallyby the central computer DRIVE
(DedicatedRoadInfrastructure for Vehicle
basedon the real-timefeedback
at the local level to
Safety in Europe) and PROMETHEUS
(Programme
optimizetraffic flow [Lowrie,1982].
for EuropeanTraffic with Highest Efficiency and
TERMINUS
extends the philosophy of all these UnprecedentedSafety) programs are on-board
driver information systemsthat use networkcomsystemsfurther by applyinga neural netwod~
optito aid motorists[Frenchet al., 1991].
mizationmodelto actual traffic flow data to deter- munication
minesignal light setting that will producethe most 2 ATMSSystemArchitecture
efficient flow. Similarto SCATS
it will utilize real-time
Theapplicationof artificial intelligencetechnoldata gatheredat the local level to determinethe
signal light configuration. However,TERMINUS ogy to AdvancedTraffic ManagementSystems
hasbeena focusat GeorgiaTechfor several
does not utilize a pre-programmed
plan, unique (ATMS)
years. Specifically, an ATMS
blackboardarchitecconfigurationsare determinedin real-time for the
ture (Figure 2.) hasbeendevelopedas the core
current situation. This allows moreadaptability in
research[Gilmore, 1990].
the system,because
eventhe mostthoughtdetailed ourAI traffic management
planscannotaccountfor uniquesituations that occur
Blackboardsare distributed problemsolving
in everydaytraffic.
architecturescapableof supportingparallel expert
E
O
¢’n
61
Knowledgesources are the actual problem
solving agentsthat are activatedby blackboard
data
structure conditions. Eachknowledge
sourcehas a
specific areaof applicationexpertise(e.g., traffic
signal coordination, incident management)
that it
contributes to the overall resolution of the data
structure solution space.Thedesignof a knowledge
sourceis broadandmayconsistof heuristics, rules,
algorithms, or anycomputerized
form of expertise.
dayof the week,affectedtraffic classification(e.g.,
rush hour, sporting events), andpredictedcongestion probabilities to determine
the potential disruption on traffic flow shouldthe hypothesis
be valid.
Onceactivated, the traffic signal knowledge
sourceexamines
the current street signal configuration on the blackboarddata structure anddeterminesthe next array of signal light settings. The
knowledge
sourceutilizes a neural networkmodel
described
in [Gilmoreet al., 1992].
Theblackboardcontrol mechanism
is comprised
of problem
solvingheuristics, rather thanapplication
2.2 CongestionPrediction Knowledge
heuristics. Control is taskedwith monitoringthe
status of the blackboard
to ascertainif a knowledge Source
sourcecancontributeto the problemsolution space,
Thecongestionprediction knowledgesourceis
and if so, whenthe knowledgesource should be
taskedwith examining
current traffic flow informaactivated/deactivated.
tion to infer whetherthe present conditions are
TheATMS
blackboardarchitecture supports an
indicative of pending traffic congestion. The
infinite numberof knowledge
sourcesaddressinga
knowledgesource is activated whenany one of
variety of traffic coordinationtasks as discussed
several congestionmeasures(e.g., traffic queue
below.
increase, incident responseimpact) is presenton
2.1 Traffic Signal Knowledge
Source
the blackboard.Theknowledge
source mayalso be
activatedby the signal durationclock. In this mode
Thetraffic signal knowledge
sourceis concerned
of operation, an assessment
of potential traffic
with determining
the signallight settingsresulting in
congestion
is made
beforethe nextset of signal light
an efficient (versusoptimal)flow of traffic at any
settingsis generated
by the traffic signal knowledge
given moment
in time. Activation of this knowledge
sourcemaytake two forms. First, a predetermined source.
durationtime maybe employed
to control the length
of timea light mayremainin its currentstate (e.g.,
greenor the yellowred transitionstate) ortheinterval
betweenassessments
of the signal settings. The
expiration of the time interval clock maintainedon
the blackboard
data structure will trigger the activation of the traffic signal knowledge
source.
Second,the posting of any form of any actual
traffic incidents to the blackboarddata structure
requiring traffic flow adaptationwill trigger the
knowledgesource. Third, the posting of traffic
congestion
predictionsthat mayaffect traffic flow will
trigger the knowledgesource whenthey exceeda
dynamically adapting confidence measure.This
measure
weighsexisting informationon time of day,
62
Congestionis predicted through the use of a
neural network modelemployingback propagation
[Gilmoreet al., 1993].Trainedusingtraffic flow data
on actual evolving congestion, the knowledge
sourcepredicts the type of congestion,the affected
streets andinterstate highways,the amount
of time
until the congestion
will peak,andthe probabilitythat
its assessment
is accurate.Eachtime the knowledge
sourceis activated, it first examineswhethera
predictioncurrently exists onthe blackboard.If so,
the existing prediction is updatedbefore anyadditional analysis occurs. This allows the systemto
weighthemerits of known
predictions in determining
whethercurrenttraffic patternsare actually separate
or related events.
2.3 Ramp Metering
Knowledge Source
Ramp
meteringis the control of traffic on to
interstate highwaysthroughentrancerampsignal
lights orvariable message
signs. Therampmetering
knowledge
sourceis activated whensensorsindicate either the existence of congestion on the
highway,or a queuebuild-up on the entranceramp.
As the entrancerampqueuemayeventually create
surfacestreet congestion,effective rampmetering
is also a congestionalleviating measure.
areascongested
dueto accidents. This is particularly valuableif the accidentoccurs in the only
available lane (e.g., the northbound
lane on a three
lane roadin whichthe reversible lane is currently
southbound).
Thetraffic control knowledge
sourceis activated
whenan incident is postedto the blackboard,when
congestion
is detectedin an areacontainingvariable
message
signs or reversible lane, or whena high
confidencecongestionprediction is hypothesized.
Rules are usedto determinethe mostappropriate
Ramp
meteringis achievedthroughthe use of a
responseto the existing condition. Theresponses
qualitative reasoningknowledge
basethat constimayrangefrom reversing a lane to coordinatinga
tutes the knowledgesource. Modified to address
series of variable message
signs on a highwayto
the temporalrealities of traffic flow, the ramp
metering knowledgebase posts timing sequences alleviate traffic through secondaryroads. The
mayalso be to take no action at the current
to the blackboard to govern on cominghighway response
time basedon the knowledge
source’sinferencethat
traffic.
another ATMSknowledge source will be more
2.4 Traffic Control Knowledge
Source
effective.
Signallights are the primaryformof traffic flow
control in an ATMS,
but two other control medium
exist. Variable messagesigns are slowly being
incorporated
into the transportationinfra structureof
manylarge metropolitancities. Usually positioned
on interstate highways,variable message
signs
provideinformationto motorists on highwayconditions and traffic problems.Messages
mayprovide
drivers with informationon surfaceconditions(e.g.,
SlowTo 30MPH:
Ice DetectedAhead),alternative
routesto avoid traffic build-ups(e.g., Alternative
StadiumRoute: TakeExit 13 To AvoidDelays), and
updateson traffic conditions(e.g., AccidentAt Williams Street Overpass:PleaseAvoidLeft Lane).
Control of reversible lanes allows an ATMS
to
supportexisting traffic patterns,but also adaptto
dynamictraffic events. Themostcommon
existing
traffic patternsare morningandeveningrush hours.
Reversingthe middlelane on a three lane road so
that it containstraffic flowinginto the city in the
morning
andtraffic leavingthe city in the evening
will
increasethe overall city travel flow. Lanereversal
mayalso be usedto increase traffic flow around
63
2.5 AssessmentMonitoring Knowledge
Source
Assessment
monitoring in an advancetraffic
management
systemwill take two forms. First, key
intersectionswill be monitored
by additional street
sensors and camerasto provide humanoperators
with detailed informationon individual intersection
status. Knowledge-based
computervision systems
will analyze
intersectionimagery
to verify traffic flow
estimates,identify the type of congestionpresent,
andascertainthe existenceof anyunreported
traffic
incidents. Second,an assessment
of the traffic
congestion predictions in various areas of the
transportation networkwill be performed.Unsatisfied predictions will be re-evaluatedandremoved
fromthe blackboard
if theyproveto be invalid.
Theassessment
monitoring knowledge
sourceis
activatedby inconsistencies
in traffic patterns(e.g.,
greensignal without traffic flow) andcongestion
predictions. Utilizing a semanticframerepresentation, the knowledgesource will analyze the
blackboard
data in order to determineincidents and
eventsaffecting traffic flow. Detectedproblems
will
be postedto the blackboardfor resolution by the
appropriate knowledgesource.
2.6 Incident Knowledge
Source
TheIncident Knowledge
Sourceis a rule-based
approachto incident management.
An incident is
an eventthat restricts traffic flow or requiresthe
routing of safetyvehicles.Severaltypesof incidents
mayoccur in a traffic management
system.First,
accidentsinvolvingoneor morevehicleswill clearly
betraffic incidentswith a majorimpactontraffic flow.
Second,road construction involving the narrowing
of a roador highway
will result in restricted traffic
flow. Third,safetyvehicleresponse
(i.e., police,fire,
or emergencyvehicles) to accidents, fires, or
emergencies
will requirespecialvehiclerouting that
will adversely
effecttraffic flow.
The knowledgesource is activated whenan
incident is postedto the blackboard.Rulesfirst
determinethe safety vehicle routing requirements
and post updatedroad priorities to the blackboard
for useby the traffic signalknowledge
source.These
priorities will result in increased
traffic flow for the
streets on whichthe safety vehiclesare traveling.
Traffic flow aroundthe incident areais addressed
next. Rulesare usedto the determinemostefficient
methods
of alleviatingtraffic in the area.Thisresults
in a posting of modified weights and/or energy
functions to the blackboardfor use by the traffic
signal neural network.
3 Summary
The research programdescribed herein is an
ongoingendeavorat GeorgiaTechas part of the
University’s TransportationTechnologiesCenter.
Simulationresults in the areaof intelligent traffic
signal control andcongestionprediction haveindicated the improvements
possible through an integrated blackboard approachtoward the traffic
management
problem. Our workshoppresentation
will describe the ATMS
blackboardarchitecture,
detail the applicationdomains
andtriggers for each
54
traffic knowledge
source, present results of the
current system,and describe the areas of future
development.Future plans include the adaptation
the ATMS
blackboardto addressthe special transportation requirements
Georgiawill face during the
1996Olympicsin Atlanta.
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