Advanced Traffic Information and
Management Systems
State of the Art and Future Challenges
Jaume Barceló, Professor Emeritus
UPC-Barcelona Tech and Linköping University
THE LONG WAY TOWARDS ATIS/ATMS
• THE EUROPEAN WAY
– Projects from various Framework Programs starting in
1989
– CLAIRE, CAPITALS, KITS, TRYS, WAYFLOW, ENTERPRICE,
EUROCOR….
• NATIONAL FUNDED PROJECTS
– Singapore, Madrid “Calle-30”, Toronto-DOT, USA
“Lower Manhattan”…
• USA APPROACH
– FHWA ICM Program: Oakland and San Diego, CA;
Dallas, Houston, and San Antonio, TX; Montgomery
County, MD; Seattle, WA; and Minneapolis, MN.
– FHWA: Guidelines and Methodological Frameworks
ATIS/ATMS-Stockholm/J. Barceló
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AN ORGANIC VIEW ON THE EVOLUTION OF
ATIS/ATMS ARCHITECTURES THROUGH PROJECTS
CLAIRE
KITS
DRIVE-I (1989-91)
TRYS
CAPITALS
DRIVE-II/ DRIVE-III (ATT Program
(1992-1996)
4th Framework Programme
(1997-1999)
ENTERPRICE
ISM (WAYFLOW)
2000-2003
Madrid C-30
2006-2008
ICM/FHWA
Guidelines
In4Mo
2011-2012
ICM
SANDAG
Global Integrated
Architecture
2014
ATIS/ATMS-Stockholm/J. Barceló
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APPROACHES TO
SYSTEM’S ARCHITECTURE
ATIS/ATMS-Stockholm/J. Barceló
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THE KITS MODEL
ATIS/ATMS-Stockholm/J. Barceló
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SCENARIO ANALYSIS IN THE TRAFFIC
MANAGEMENT LOOP (ISM PROJECT)
real
traffic data
strategy
data input
scenario
scenario
evaluation
evaluation
simulation results
adjustment
ALMO
ALMOContent
Content
traffic
trafficpattern
pattern
knowledge
knowledge
base
base
O/D-matrix
net validation and
calibration
ATIS/ATMS-Stockholm/J. Barceló
AIMSUN
6
4th EU Framework Program 1999
Project ENTERPRICE
long-/mid-term
strategy update
Graphical User Interface
D
O
D
O
3
3
D
O
O
D
O
D
O
D
O
O
D
D
D
O
D
Historical
traffic data
Geographic
data
Planning data
(control
strategy)
Interface to External Systems
real-time
TIC data
O
D
D
O
O
Scenario Scenario
Editor Generation
O/D Estimation
Model
Scenario
Simulation
AIMSUN2
microsimulation
Model
Qualitative analysis
)
(Knowledge
Bases)
Analysis Quantitative
& Evaluation analysis
(statistics)
Bus Dati
Software
Software
Data
Bus
Geographic DataBase
(Network Model)
Evaluation DataBase
(Scenarios, results, ...)
ATIS/ATMS-Stockholm/J. Barceló
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KEY COMMON COMPONENTS
• Real-time traffic data collection
• Traffic data processing
• Traffic mobility patterns: Origin-Destination trip
matrices
• Dynamic Traffic Model (usually a meso or micro
traffic simulation model)
– To estimate current traffic state
– To short term prognose traffic state evolution
• A Decision Support System
– Rule-Based (Knowledge Based System)
– Scenario Analysis and Evaluation (Based on KPI)
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A USE CASE: THE INTERMODAL
STRATEGY MANAGER (ISM)
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STEPS IN THE DECISION MAKING PROCESS:
Which is the appropriate strategy? (ISM Project/WAYFLOW)
Historic Traffic
Data Base
Real TimeTraffic
Data Base
Real Time
Traffic
Measurements
from
Detectors
Problem
Identifier
Problem
Network
Definition
Strategy
Data Base
Problem
network
Apply
strategy
Select strategy
Evaluate Impact
(Simulation)
Make Decision
ATIS/ATMS-Stockholm/J. Barceló
Traffic
Problem
10
MANAGEMENT STRATEGIES
• Strategies are a combination of Policies
• Policies for control over time  Traffic Lights
– Signal timings
• Policies for control over space  VMS
–
–
–
–
–
Blocking lanes
Managing tidal flows
Linear speed control
Ramp metering
Rerouting
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STRATEGY DEFINITION AND OPTIMISATION
• Loading relevant data
• adaptation of OD-matrices for the problem area
historic real
world data
normal route
Strategy
data base
strategies
VMS
Problem area
Geographical
Data model
Result data
detector
OD-matrices
patterns
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FHWA:
Guidelines and Methodological
Frameworks
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AMS FRAMEWORK FOR ICM
ICM
Interface
Trip Assignment
Modal Choice
Trip Distribution
Trip Generation
• Revised Trip Tables
• Refined Travel Times
Regional Travel
Demand
Model
Peak Spreading
• Trip
Table
Network
Resolution
• Network
• Other
Parameters
STRATEGIC LONG TERM
PLANNING LEVEL
Meso- and/or
Micro-simulation
Enhanced
Performance Measures
No
• VMT/VHT/PMT/PHT
Dynamic
Assignment
Yes
Convergence ?
Pivot Point
Modal Choice
• Travel Time/Queues
Throughput/Delay
• Environment
• Safety
Refined Transit
Travel Times
• Refined
Trip
Table
(Smaller
• Zones and Time
Slices)
• Refined Network
Benefit Valuation
OPERATIONAL
SHORT TERM
MANAGEMENT
LEVEL
User Selection of
Strategies
Outputs
• Benefit/Cost Analysis
• Sensitivity Analysis
• Ranking of ICM Alternatives
Cost of Implementing
Strategies
Source: V. Alexiadis, (2008) Integrated Corridor Management Analysis, Modeling, and Simulation
Experimental Plan for the Test Corridor, USDOT Integrated Corridor Management (ICM) Initiative,
FHWA-JPO-08-035, EDL 14415
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INTEGRATED CORRIDOR MANAGEMENT (ICM) AND
ANALYSIS, SIMULATION MODELING (AMS) APPROACH
Source: V. Alexiadis and D. Sallman, Cambridge Systematics; A. Armstrong, SAIC, (2012), Traffic Analysis Toolbox Volume XIII: Integrated
Corridor Management Analysis, Modeling, and Simulation Guide, Report No. FHWA-JPO-12-074
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INTEGRATED MACRO-MESO-MICRO
A CONSISTENT
APPLICATION OF
THE AMS
METHODOLOGY
REQUIRES:
• A consistent
Network Modeling
at all levels for
Off-line and On-line
applications
• A smooth and
consistent
information
exchange between
all levels
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THE METHODOLOGICAL PROCESS
MACROMESOMICRO
MACRO LEVEL: TRANSPORT PLANNING
MODEL OF A REGIONAL OR
METROPOLITAN AREA
IDENTIFICATION OF CRITICAL SUBAREAS
OF INTEREST
Traversal
WINDOWING INTO THE
SELECTED SUBAREA
AUTOMATIC GENERATION OF SUBAREA
MICRO OR MESO MODEL
Graphic
subnetwork
selection
AUTOMATIC GENERATION OF SUBAREA
TRAVERSAL OD (IF REQUIRED)
ADJUSTMENT OF THE SUBAREA
TRAVERSAL OD FROM AVAILABLE DATA
Selected
Subnetwork
INPUT TO MESO OR MICRO MODEL
SUBAREA SIMULATION
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TRAFFIC MANAGEMENT OPERATIONS REQUIRE OD
MATRICES ADJUSTED TO TIME SLICES TO PROPERLY
APPROACH TIME DEPENDENCIES OF THE DEMAND
Within-day time variability of traffic demand gi(t) of i-th OD pair
gi (t)
Traffic demand (number of trips per
hour)
3000
2500
2000
gi
1500
1000
500
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of the day
Adjusted OD
matrix for
time-slice jth
from link flow
counts
𝒈 𝒕 → 𝒈𝟏 𝒕 , … , 𝒈𝒋 𝒕 , … , 𝒈𝒏𝒉 𝒕
𝒋
𝒋
𝒈𝒊 𝒕 ~𝒈𝒊 = 𝜶𝒋 𝒈𝒊
𝒋
→ 𝒈𝒊
𝒋
𝒗𝒂 , 𝒂 ∈ 𝑨𝑨
TRB 2014 paper 14-3793
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USE CASES
• Madrid Calle-30 (Aimsun)
• Integrated CorridorManagement (ICM)
demonstration sites:
– San Diego (SANDAG) (Aimsun)
– Dallas (Texas Model)
– Oakland (DYNUS-T)
• Edmonton YellowheadTrail Case Study
(OPTIMA –VISUM/VISSIM)
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ATDM/AMS system to support simulated
real-time analysis.
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BASIC ARCHITECTURE OF DSS IN
Aimsun ON-LINE (SANDAG)
Real time raw
detection data
Demand OD
matrices data base
Detection pattern
historical
data base
Detection data filtering
and processing
“OD Matcher”
Pattern recognition module
Filtered
detection pattern
Quality
Indicators
Quality Manager
module
Selected
OD matrix
AIMSUN
Micro/Meso
Short term
forecasting
Forecasted
traffic data
Parallel Simulations
AIMSUN ONLINE
Real time
control plan data
Real time
events detection
Traffic management strategies
ATIS/ATMS-Stockholm/J. Barceló
Traffic Management
Operations
21
THE KPI BASED DECISION MAKING PROCESS
22
ATIS/ATMS-Stockholm/J. Barceló
DECISION SUPPORT SYSTEM:
VISUAL COMPARISON OF SCENARIOS
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ATIS/ATMS-Stockholm/J. Barceló
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San Diego:Data Sources for PeMS
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ICM San Diego Subsystems
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Yellowhead Trail ATDM Framework
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CURRENT AND FORTHCOMING
TECHNOLOGICAL SCENARIOS
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ICT TRAFFIC DATA COLLECTION SCENARIO
o
Vehicle n
Reaches RSU k
At time t1
i
Vehicle n
Leaves origin i
At time t0
Vehicle n
Reaches RSU m
At time t2
Vehicle n
Reaches RSU p
At time t3
THE “SMART CITY”
MULTIPLE HETEROGENEOUS DATA
SOURCES (SENSORS)
Vehicle n
Sends AVL message
At time t0+2t
Vehicle n
Sends AVL message
At time t0+t
Data (RSU Id, mobile
device identity, time
stamp ti) sent by GPRS
to a Central Server
Data (RSU Id, mobile
device identity, time
stamp) sent by GPRS to
RSU-IDx
a Central Server
RSU-IDy
Loop detectors /
Magnetometers
On-board unit of equipped vehicle n
captured by RSU-IDx at time t1
V2V exchange
AVL Equipped vehicle sends message
(id, position, speed) at time t
On-board unit of equipped vehicle n recaptured by RSU-IDy at time t2
Average speed
𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆 𝑹𝑺𝑼𝒙 − 𝑹𝑺𝑼𝒚
𝒕𝟐 − 𝒕𝟏
• Point detection with discrete time
resolution
• Inductive loop detectors:
• Flows (veh/hour), occupancies (time %)
• Spot Speeds (km/hour)
• Traffic mix (% light, heavy vehicles)
•Point detection with continuous time
resolution:
• Magnetometers
• Time in/Time out (flow counts, spot
speeds, occupancies, traffic mix)
• Bluetooth/Wi-FI, LPR, TAGs
• Time tag, vehicle/device identification
and downstream re-identification (
sample counts, travel time
measurements)
• Continuous time-space detection
• GPS, Connected Cars
• Time tag, position (X, Y, Z
coordinates) local speed, heading
• Smartphone data (Open question)
ATIS/ATMS-Stockholm/J. Barceló
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BETTER
DATA
BETTER
MODELS
BETTER
INFORMATION
BETTER
SERVICES
ADVANCED (ACTIVE)
TRAFFIC MANAGEMENT &
INFORMATION SYSTEMS
(INTEGRATED CORRIDOR
MANAGEMENT)
ATIS/ATMS-Stockholm/J. Barceló
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TRAFFIC DATA ANALYTICS (I)
Dealing with heterogeneous traffic data:
-Data filtering, completion and fusion
-Processing huge amounts of data (Big Data)
Kernel Smoothing Methods & traffic flow Missing data supply
based models to identify and remove
outliers Network with Multisensor Technologies for Traffic Data Collection
Measures from
Technology 1
(Loop Detectors, Radar,
Magnetometers…..)
Data from
Measurement
Point1.1
...
Data from
Measurement
Point 1.n
Data
Collection
Protocols
Measures from
Technology 2 (GPS)
Data from
Collection
Point 2.1
...
Raw Data
Filtering and
Completion
Data from
Collection
Point 2.m
…
Messures from
Technology k
(Bluetooth, LPR, TAG)
Data from
Measurement
Point k.1
...
Data Fusion Methods
of Type II (Kalman,
Bayesian…)
ATIS/ATMS-Stockholm/J. Barceló
Mobile Data from
Smartphones
Data from
Measurement
Point k.p
Fusion Results: state
reconstruction, map of
speeds and their time
evolution,…..
31
IMPROVED CONCEPTUAL ARCHITECTURE FOR AN
ADVANCED TRAFFIC MANAGEMENT
AND INFORMATION SYSTEM
Data
Collection
Protocols
Real-Time
Raw Traffic
Data
Traffic Data
Processing &
Management
Real-Time
Cleansed
Traffic Data
Sensored Urban Nework &
Data from Mobile Sensors
Profile Pattern
Recognition
Process
Data Processing Level I (Data Cleansing,m Missing Data Models,
Profile Generation & Profile Identification
ACTUATORS TO
IMPLEMENT STRATEGIES
· Gate In/Gate Out
· Reroutings
· Speed Control
· Control Changes
· Other
Historical
Database
· Traffic
· profiles
Other data
· Weather
· Calendar
·
·
·
·
DISSEMINATION OF
INFORMATION TO USERS
Variable Message Panels
Internet/Smartphones
Navigation Equipment
Other
Historical
Seed OD
Database
Selected
Profile
ONLINE
ONLINE DECISION
DECISION SUPPORT
SUPPORT SYSTEM
SYSTEM
Impact
Evaluation
Process
SELECTED STRATEGY
DYNAMIC
TRAFFIC
MODELS
Management
Strategies
Data Processing Level III
ATIS/ATMS-Stockholm/J. Barceló
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IN THE WAY TO CREATE “COMPREHENSIVE
SITUATIONAL AWARENESS”
Raw Traffic Data from
Multiple Sensor
Technologies
Historical Traffic &
Profiles Database
Data Processing Level I (Data Cleasing, Missing
Data Models, Profile Generation)
Profile Selection
(Day, Time of the day…)
Calibration of Filter
Parameters
Data Processing
Level II (Data Fusion)
Consistent and
Reliable Traffic Data
Local Traffic State &
Short Term
Forecasting
Multisensor timespace state
reconstructions
Estimation of TimeDependent OD
Matrices
Refinement of
Historic Profiles
Raw Data
Filtering (Per
Sensor)
Yes
Clean, complete
real-time traffic
data
Accept Data?
No
Missing Data
Model
Outlier
Replacement
Data Processing Level III
Multisensor Bayesian
Fusion Models
Dynamic Traffic
Models
Information for the
Decision Making
Process
ATIS/ATMS-Stockholm/J. Barceló
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TRAFFIC DATA ANALYTICS (II)
Identification of time-dependent mobility
patterns in terms of Origin-Destination (OD)
Matrices Exploiting ICT measurements
Off-line estimation of a good input
OD seed per time interval
Destination
Origin
t ijτp number of trips from Origin i to
Destination j in time period  for
purpose p
Nonlinear bilevel nondifferentiable optimization
problem solved using:
-A special version of Stochastic Perturbation
Stochastic Approximation at the upper level
- A Dynamic User Equilibrium Assignment at the
lower level
k
k
T
g kk 1  Dgkk Pk 1  DPk D  Wk
Initialization
Factors determining
the quality of the
estimation:
1. % technology
penetration
2. Detection layout
3. Input OD seed


Pkk11  I  G k 1Fk 1 Pkk1

G k 1  Pkk1FkT1 Fk 1Pkk1FkT1  R k
KF recursive
dynamics
g kk11  g kk1   d k 1  0

dk 1  G k 1 zk  1  Fk 1g kk 1

Online Ad Hoc Kalman Filter to estimate the
time dependent OD
ATIS/ATMS-Stockholm/J. Barceló
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

Network
Model
Time-dependent
OD matrices
Traffic
Control Data
Traffic
Network
Space State
Initial path calculation
Estimation
and selection
and Short
Estimate the new path sets
according to the computational
MAIN OUTPUTS
Term
calculate paths and
algorithm for equilibrium (MSA,
Forecasting
paths flows at time t
Projection…) adding new paths
- Time dependent flows
or
removing
existing
ones
for
- Time dependent travel times
Based on a
each OD pair and time interval
- Queue dynamics
Perform Dynamic
Dynamic
- Congestion dynamics
Network Loading (meso
Traffic Model
traffic simulation)
Mesoscopic
Estimate path travel
Traffic
times at time t
Simulation
NO
(Projects
YES
DUE
Convergence
criteria
STOP
SIMETRIA,
(Rgap ) satisfied
MITRA, In4Mo)
COMPLETE NETWORK INFORMATION
Alternative paths and
forecasted path travel times
LinkVelocidad
SpeedenMap
Link
Travel
Tiempo
de viajeTimes
de los arcos
los arcos
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CONCEPTUAL ARCHITECTURE OF THE DECISION
SUPPORT SYSTEM FOR ADVANCED TRAFFIC
MANAGEMENT AND INFORMATION
(A)
(A) OFF-LINE
OFF-LINE GENERATION
GENERATION OF
OF CANDIDATE
CANDIDATE TARGET
TARGET OD
OD MATRICES
MATRICES
(B)
(B) ON-LINE
ON-LINE SELECTION
SELECTION OF
OF
TARGET
TARGET OD
OD MATRIX
MATRIX
REAL-TIME
TRAFFIC
DATA
HISTORICAL
TRAFFIC DATABASE
(PROFILES/
BEHAVIORAL
PATTERNS)
TARGET MATRIX
GENERATION FOR
SELECTED PROFILE
INITIALIZATION
HISTORICAL OD
MATRIX
REAL TIME
DATABASE OF
TIME-SLICED
OD SEED
MATRICES
OD MATRIX
ADJUSTMENT
PROCESS
NETWORK
MODEL
SELECTED
TARGET FOR
TIME SLICE k
REAL-TIME OD
KALMAN
FILTER
ESTIMATOR
REAL-TIME
TRAFFIC
DATA FOR
TIME SLICE k
MANAGEMENT
STRATEGIES
DATABASE
ONLINE
EVENT
DETECTION
COMPLETE NETWORK
INFORMATION
PREDICTED
OD MATRIX
FOR TIME
SLICE k+1
DYNAMIC
TRAFFIC
MODEL
DEFINITION OF KEY
PERFORMANCE
INDICATORS
IMPACT
EVALUATION
PROCESS
SELECTION OF
THE OD
TARGET
MATRIX FOR
TIME SLICE k
LinkVelocidad
Speed
Map
en los
arcos
Tiempo
de viaje de
los arcos
Link
Travel
Times
EVALUATION OUTPUT
CURRENT VS.
FORECASTED STATES
Alternative paths and
forecasted path travel times
DECISION SUPPORT PORCESS
(C) REAL-TIME ESTIMATION AND PREDICTION OF THE OD MATRIX FOR TIME SLICE k+1, ESTIMATION
OF CURRENT AND PREDICTED STATE, STRATEGY SELECTION AND IMPACT EVALUATION
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CONCLUSIONS AND RECOMMENDATIONS (I)
• The accumulated European and US experiences prove that
the implementation of an ATIS/ATMS project is large and
complex and doesn’t admit shortcuts.
• All the existing architectures share the main components:
–
–
–
–
Real-Time Traffic Data collection, filtering, completion and fusion
Generation of traffic profiles for varied conditions
Off-line and (desirably) On-line OD estimation tools
Simulation models for network state estimation and short term
forecasting computationally performing
– Decision Support Systems with capabilities for scenario analysis and
evaluation
• Common critical issues: To be compliant with the widely
accepted AMS framework, long-term planning models and
real-time operational models should:
– Integrate planning and simulation traffic models with consistent
network representations Off-line and On-line models must share
common components at appropriate levels
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CONCLUSIONS AND RECOMMENDATIONS (II)
•
There are in the market available commercial tools that
partially fulfill the main requirements, but:
– To increase the efficiency of a successful implementation it would be
desirable that some key components (Data Management, OD
estimation…) should be replaced by state of the art components
– To achieve that, the tool must be able of flexible and efficient
integration such components
– Even the most advanced commercial tools must be customized: every
ATIS/ATMS project is site specific 
• Network models must be built and properly calibrated
• Data samples of significant size must be collected
• Algorithms for data analysis and fusion, OD estimation, etc. must be fine
tuned , calibrated, adapted…
ATIS/ATMS-Stockholm/J. Barceló
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RELATED PAPERS
1.
2.
3.
4.
5.
6.
7.
8.
9.
US Department of Transportation, Research and Innovative Administration:
– Analysis Modeling and Simulation (AMS) Analysis Plan Final Report (2013) FHWA-JPO-13-22
– Analysis Modeling and Simulation (AMS) Capabilities Assessment (2013) FHWA-JPO-13-21
Cambridge Systematics, Decision Support Systems for Integrated Corridor Management Needs
Analysis, Report for the FHWA, 2009, www.camsys.com
H.Kirschfink , M.Boero , J.Barcelo (1997), Real-time traffic management supporting intermobility
and strategic control, ITS World Conference, Berlin
M. Boero, H. Kirschfink (1999), Case Studies of Systems; the ENTERPRICE model, Erudit
Workshop
J. Barceló, M. Delgado, G. Funes, D. García and A. Torday, An on-line approach based on
microscopic traffic simulation to assist real time traffic management, 14th World Congress on
Intelligent Transport Systems, Beijing 2007
M.Bullejos , J.Barceló , L.Montero (2014), A DUE based bilevel optimization approach for the
estimation of time sliced OD matrices, International Symposium of Transport Simulation, Ajaccio,
June 2014, Procedia - Social and Behavioral Sciences (2014), available at www.sciencedirect.com
J.Barceló, L.Montero, M.Bullejos, M.P. Linares, O. Serch (2013), Robustness and computational
efficiency of a Kalman Filter estimator of time dependent OD matrices exploiting ICT traffic
measurements. TRR Transportation Research Records: Journal of the Transportation Research
Board, No. 2344, pp. 31-39.
J.Barceló, L.Montero, M.Bullejos, O. Serch and C. Carmona (2013), A Kalman Filter Approach for
the Estimation of Time Dependent OD Matrices Exploiting Bluetooth Traffic Data Collection. JITS
Journal of Intelligent Transportation Systems: Technology, Planning and Operations, 17(2):1-19.
J.Barceló, F. Gilliéron, M.P. Linares, O. Serch, L.Montero (2012), Exploring Link Covering and Node
Covering Formulations of Detection Layout Problem. TRRTransportation Research Records:
Journal of the Transportation Research Board, No. 2308, pp.17-26.
Models for Smart Mobility in Smart Cities
39
THANK YOU VERY MUCH FOR YOUR ATTENTION
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