ASSESSMENT OF EMERGING OPPORTUNITIES FOR
REAL-TIME, MULTIMODAL DECISION SUPPORT
SYSTEMS IN TRANSPORTATION OPERATIONS
Concept of Operations Final Draft
Contract Number: DTFH61-06-D-00005
Task Order T-10-007
Submitted to:
United States Department of Transportation
ITS Joint Program Office
Research and Innovative Technology Administration
RITA Report Number FHWA-JPO-10-058
Submitted by:
Science Applications International Corporation and Delcan Corporation
May 17, 2011
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Technical Report Documentation Page
1. Report No.
2. Government Accession No.
TBD
4. Title and Subtitle:
Assessment of Emerging Opportunities for Real-Time,
Multimodal Decision Support Systems in Transportation
Operations
Task 4 Concept of Operations
3. Recipient’s Catalog No.
7. Authors:
Dan Lukasik, Bruce Churchill, Jackie Golob, Teresa Malone,
Elliot Hubbard
9. Performing Organization Name and Address
Science Applications International Corporation (SAIC)
8301 Greensboro Drive, Mailstop E-12-3
McLean, VA 22102-3608
8. Performing
Organization Report No.
12. Sponsoring Agency Name and Address
United States Department of Transportation
ITS Joint Program Office
Research and Innovative Technology Administration (RITA)
1200 New Jersey Avenue, SE
Washington, DC 20590-0001
5. Report Date
May 17, 2011
6. Performing
Organization Code
10. Work Unit No.
(TRAIS)
11. Contract or Grant No.
DTFH61-06-D-00005, Task
No. T-10-007
13. Type of Report and
Period Covered
14. Sponsoring Agency
Code
HOIT-1
15. Supplementary Notes
Mr. Dale Thompson (COTM)
16. Abstract
Not Applicable
17. Key Words
18. Distribution Statement
Multimodal, Decision Support Systems, DSS, RealNo restrictions.
time, Transportation Operations
19. Security Classif. (of this 20. Security Classif. (of this
21. No of
22. Price
report)
page)
Pages
N/A
Unclassified
Unclassified
63
Table of Contents
May 17, 2011
Contents
1
Section 1 – Scope ................................................................................................................ 1-1
1.1
Identification ................................................................................................................. 1-1
1.2
Document Overview ..................................................................................................... 1-1
1.3
Real-Time Multimodal DSS Definition ........................................................................ 1-2
1.4
System Overview .......................................................................................................... 1-2
2
Section 2 – Referenced Documents ................................................................................... 2-1
3
Section 3 – Current System or Situation.......................................................................... 3-1
4
5
3.1
Background, Objectives and Scope ............................................................................... 3-1
3.2
Operational Policies and Constraints ............................................................................ 3-2
3.3
Description of the Current System or Situation ............................................................ 3-3
3.3.1
Existing RTMDSS Technologies and Methodologies ............................................ 3-3
3.3.2
DSS Implementation............................................................................................... 3-4
3.3.3
RTMDSS Capabilities ............................................................................................ 3-5
3.4
Classes and Other Involved Personnel .......................................................................... 3-6
3.5
Support Environment .................................................................................................... 3-6
3.6
Conclusions Regarding the Current Situation ............................................................. 3-11
Justification For and Nature of Changes ......................................................................... 4-1
4.1
Justification for Changes ............................................................................................... 4-1
4.2
Description of Desired Changes .................................................................................... 4-2
4.3
Priorities among Changes.............................................................................................. 4-3
4.4
Changes Considered but not Included ........................................................................... 4-4
4.5
System Needs ................................................................................................................ 4-4
Concepts for the Proposed System ................................................................................... 5-1
5.1
Background, Objectives and Scope ............................................................................... 5-1
5.1.1
Background ............................................................................................................ 5-1
5.1.2
Objectives ............................................................................................................... 5-1
5.1.3
Scope ...................................................................................................................... 5-2
5.2
Operational Policies and Constraints ............................................................................ 5-2
5.2.1
Policies ................................................................................................................... 5-2
5.2.2
Constraints ............................................................................................................. 5-2
Real-Time Multimodal DSS Concept of Operations (Final Draft)
i
Table of Contents
5.3
6
7
8
May 17, 2011
Description of the Proposed System ............................................................................. 5-2
5.3.1
GIS-Based Visualization Platform ......................................................................... 5-6
5.3.2
Source(s) of Real-Time Data ................................................................................. 5-6
5.3.3
Source(s) of Historical or Archived Data .............................................................. 5-6
5.3.4
Persistent Data Storage ......................................................................................... 5-6
5.3.5
Business Process Engine........................................................................................ 5-6
5.3.6
Expert Systems or Other Rule-Based Systems ....................................................... 5-7
5.3.7
Traffic Responsive and Other Predictive Algorithms ............................................ 5-7
5.3.8
Faster Than Real-Time Modeling .......................................................................... 5-7
5.3.9
Off-Line Modeling .................................................................................................. 5-8
5.4
Modes of Operation ....................................................................................................... 5-8
5.5
System User Groups and Other Involved Personnel ..................................................... 5-8
5.6
Support Environment .................................................................................................. 5-10
Operational Scenarios ....................................................................................................... 6-1
6.1
Real-Time Multimodal DSS Assumptions.................................................................... 6-1
6.2
“Generic” Scenarios ...................................................................................................... 6-1
6.3
Scenario Analysis .......................................................................................................... 6-2
6.3.1
Scenario #1: Daily Operations ............................................................................. 6-4
6.3.2
Scenario #2: Major Traffic Incident ..................................................................... 6-6
6.3.3
Scenario #3: Major Evacuation ............................................................................ 6-9
6.3.4
Scenario #4: Significant Winter Weather Event ................................................. 6-11
6.3.5
Scenario #5: Special Event ................................................................................. 6-13
Summary of Impacts........................................................................................................ 7-16
7.1
Operational Impacts .................................................................................................... 7-16
7.2
Organizational Impacts ............................................................................................... 7-16
7.3
Procurement/Development Impacts ............................................................................ 7-16
7.3.1
Iterative Development .......................................................................................... 7-16
7.3.2
Communications .................................................................................................. 7-17
7.3.3
Data Management ................................................................................................ 7-18
7.3.4
Web Access........................................................................................................... 7-18
7.3.5
Interoperability .................................................................................................... 7-18
7.3.6
Logistical Support ................................................................................................ 7-18
Analysis of Proposed System............................................................................................. 8-1
Real-Time Multimodal DSS Concept of Operations (Final Draft)
ii
Table of Contents
8.1
Summary of Improvements ........................................................................................... 8-1
8.2
Disadvantages and Limitations ..................................................................................... 8-1
8.2.1
Disadvantages ........................................................................................................ 8-1
8.2.2
Limitations ............................................................................................................. 8-1
8.3
9
May 17, 2011
Alternatives and Trade-Offs Considered....................................................................... 8-2
Appendices .......................................................................................................................... 9-1
Real-Time Multimodal DSS Concept of Operations (Final Draft)
iii
List of Figures and List of Tables
May 17, 2011
List of Figures
Figure 1-1: Conceptual View of an RTMDSS with Inputs, Processing, and Outputs ................. 1-4
Figure 5-1: Notional RTMDSS .................................................................................................... 5-4
Figure 5-2: Response Plan Processing Hierarchy ........................................................................ 5-4
Figure 5-3: Nominal High-Level Architecture for RTMDSS...................................................... 5-5
Figure 5-4: Notional Example of a Freeway Management Business Process ............................. 5-7
Figure 6-1: Generic System Information Flow Diagram ............................................................. 6-3
Figure 6-2: Scenario 1 System Information Flow Diagram......................................................... 6-4
Figure 6-3: Scenario 2 System Information Flow Diagram......................................................... 6-6
Figure 6-4: Scenario 2 Phasing Diagram ..................................................................................... 6-8
Figure 6-5: Scenario 3 System Information Flow Diagram......................................................... 6-9
Figure 6-6: Scenario 3 Phasing Diagram ................................................................................... 6-10
Figure 6-7: Scenario 4 System Information Flow Diagram....................................................... 6-11
Figure 6-8: Scenario 4 Phasing Diagram ................................................................................... 6-12
Figure 6-9: Scenario 5 System Information Flow Diagram...................................................... 6-13
Figure 6-10: Scenario 5 Phasing Diagram ................................................................................. 6-15
Figure 7-1: Notional Data Architecture for a Regional Transportation Network ...................... 7-19
List of Tables
Table 1-1: A Taxonomy of Transportation Modes and Facilities................................................ 1-2
Table 3-1: Generic Support Environment – Agency Roles and Responsibilities ........................ 3-7
Table 4-1: System Needs ............................................................................................................. 4-4
Table 5-1: System User Groups ................................................................................................... 5-8
Table 7-1: Iterative Deployment Functions and Benefits .......................................................... 7-17
Real-Time Multimodal DSS Concept of Operations (Final Draft)
iv
List of Abbreviations
May 17, 2011
List of Abbreviations
AMS
ATMS
CAD
CCTV
CMS
CONOPS
COP
COTS
DMS
DOT
DSS
EMS
ESM
EOC
FHWA
FMS
FOC
GIS
GLIDE
GUI
HAR
HAT
HOV
HP
ICM
ICMS
IOC
ISP
ITS
IVR
LCS
MDSS
MEO
MMS
MPO
NTCIP
Arterial Management System
Advanced Transportation Management System
Computer Aided Dispatch
Closed-Circuit Television
Changeable Message Sign
Concept of Operations
Common Operating Picture
Commercial Off-the-Shelf
Dynamic Message Sign
Department of Transportation
Decision Support System
Emergency Medical Service
Event Scenario Matrix
Emergency Operations Center
Federal Highway Administration
Freeway Management System
Full Operational Capability
Geographical Information System
Green LInk DEtermination
Graphical User Interface
Highway Advisory Radio
Highway Advisory Telephone
High Occupancy Vehicle
Highway Patrol
Integrated Corridor Management
Integrated Corridor Management System
Initial Operational Capability
Information Service Providers
Intelligent Transportation Systems
Interactive Voice Response
Lane Closure System
Maintenance Decision Support System
Medical Examiner’s Office
Modal Management System
Metropolitan Planning organization
National Transportation Communications for ITS Protocol
Real-Time Multimodal DSS Concept of Operations (Final Draft)
v
List of Abbreviations
OBU
OODA
PND
PPP
RMS
RSU
RTMDSS
SMS
SOA
TIMS
TMC
TMS
TMDD
TMT
TOC
TODSS
TRB
TSCS
TSP
USDOT
VDOT
XML
May 17, 2011
On-Board Unit
Observe-Orient-Decide-Act
Personal Navigation Device
Public Private Partnership
Road Management System
Road Side Unit
Real-Time Multimodal Decision Support System
Short Message Service
Service Oriented Architecture
Traffic Incident Management System
Transportation Management Center
Transportation Management System
Transportation Management Data Dictionary
Traffic Management Team
Transportation or Traffic Operations Center
Transit Operations Decision Support System
Transportation Research Board
Traffic Signal Control System
Transit Signal Priority
United States Department of Transportation
Virginia Department of Transportation
Extensible Markup Language
Real-Time Multimodal DSS Concept of Operations (Final Draft)
vi
Referenced Documents
1
Section 1 – Scope
1.1
Identification
May 17, 2011
Through the United States Department of Transportation (USDOT) Connected Vehicle Research
Program, the Intelligent Transportation Systems Joint Program Office (ITS JPO) is engaged in
assessing the potential of the multi-source, active-acquisition data paradigm to enhance current
operational practices and transform future surface transportation systems management. The
Connected Vehicle Research Program is a collaborative initiative spanning the Intelligent
Transportation Systems Joint Program Office (ITS JPO), Federal Highway Administration
(FHWA), the Federal Transit Administration (FTA), and the Federal Motor Carrier Safety
Administration (FMCSA). Program objectives include:



Enable systematic data capture from vehicles, mobile devices, and infrastructure;
Develop data environments that enable the integration of data from multiple sources for
use in transportation management and performance measurement; and
Reduce costs of data management and eliminate technical and institutional barriers to the
capture, management, and sharing of data.
One foundational element of the Connected Vehicle Research program is the investigation of
how to transform the transportation management and operations archetype by utilizing
Connected Vehicle data (i.e., real-time user data captured from personal mobile devices and
vehicle-to-vehicle and vehicle-to-infrastructure communications). One such initiative is the
deployment of real-time, multimodal Decision Support Systems, the topic of this Concept of
Operations document.
The USDOT is undertaking this project to assess the emerging opportunities for Real-time
Multimodal Decision Support Systems (RTMDSS) in transportation operations. One of the first
tasks of this project has been to conduct a scan of the current Decision Support System (DSS)
practice and capabilities. That task has been completed and a RTMDSS Stakeholder Working
Group has been formed to help identify user needs and expectations and help communicate those
needs to a wider audience of potential developers and supporters of the RTMDSS concept. The
current task, and the subject of this document, concerns a Concept of Operations (ConOps)
developed with input from stakeholders obtained during an initial RTMDSS ConOps webinar
and a two-day face-to-face ConOps meeting held on March 23-24, 2011. During this meeting it
was agreed that the ConOps should be developed to represent a year 2020-2025 timeframe.
Once finalized, a set of functional systems and data requirements will be added to the study, and
the first phase of the project will be completed with a Gap Analysis Assessment Report with
recommended RTMDSS research activities.
1.2
Document Overview
This document is organized and will be presented following the guidelines of IEEE 1362-1998 IEEE Guide for Information Technology - System Definition - Concept of Operations (ConOps)
Document. The sections are as follows:
Section 1 -
Scope
Section 2 -
Referenced Documents
Real-Time Multimodal DSS Concept of Operations (Final Draft)
1-1
Referenced Documents
May 17, 2011
Section 3 -
Current System or Situation
Section 4 -
Justification for and Nature of Changes
Section 5 -
Concept for Proposed System
Section 6 -
Operational Scenarios
Section 7 -
Summary of Impacts
Section 8 -
Analysis of Proposed System
Section 9 -
Appendices
1.3 Real-Time Multimodal DSS Definition
It is necessary to take as a starting point an agreed definition of Real-Time Multimodal DSS.
With support from the various descriptions of transportation DSS in the current situation the
following represents a working definition of Real-Time Multimodal DSS:
“Real-Time, Multimodal Decision Support Systems (RTMDSS) are information systems
that support multimodal, transportation operational decision-making in real time. An
RTMDSS is an interactive, software-intensive system that gathers data from multiple
relevant real-time data sources and knowledge bases. It uses this data, along with models,
processes or analyses to implement context-specific actions and recommendations to assist
managers in the process of collaboratively managing a multimodal transportation network
to increase system efficiency and improve individual mobility, providing safe, reliable, and secure
movement of goods and people.”
RTMDSS actions are most often provided as a function (or functions) within modal management
system software and are intended to influence traffic control devices, communications systems,
traveler information devices and traveler information media (e.g., 511 systems).
1.4
System Overview
We begin with the modes applicable to this project. Table 1-1 lists commonly encountered
modes in surface transportation and the facilities on which they typically operate. These are the
modes applicable to this project.
Table 1-1: A Taxonomy of Transportation Modes and Facilities.
Mode
Facility
Privately owned vehicles
Freeways, toll roads, arterials, parking
Commercial vehicles (goods)
Commercial vehicles (passengers)
Freeways, toll roads, arterials
Freeways, toll roads, arterials, airport (ground side roadways)
Buses
Freeways, toll roads, arterials, busways
Public Safety vehicles
All roadways
Light rail
On-street tracks, dedicated rail right-of-way
Commuter rail
Dedicated rail right-of-way – used/owned
Heavy Rail
Dedicated rail right-of-way – used/owned; generally covers a
Real-Time Multimodal DSS Concept of Operations (Final Draft)
1-2
Referenced Documents
Mode
May 17, 2011
Facility
larger geographic area than Commuter Rail
Freight rail
Dedicated rail right-of-way - owned
Inter-City rail (Amtrak)
Dedicated rail right-of-way - used
Ferry
Waterway
The RTMDSS is envisioned to consist of the following high level components and features:

Existing agency single-mode management systems, including:
o Freeway Management Systems
o Arterial Management Systems
o Bus Transit Management Systems
o Rail Transit Management Systems
o Parking Management Systems
o Commercial Vehicle Systems
o Airport (Ground) Management Systems
o Ferry Systems

External information and data sources that are necessary for the DSS process. Sources
include weather data, special event data (e.g., major sports events impacting congestion),
external congestion data, emergency/evacuation information, news/data feeds, etc.

A common network interface that extracts information from both legacy and new modal
management systems, facilitates communications among such systems, and provides
useful outputs to other external systems (e.g., end users and user systems).

A centralized DSS, seen as an interactive, software-based system that extracts useful
information from a combination of modal data sources and knowledge bases (operational
rules) and converts these into actions and/or recommendations that influence
performance of the transportation network. Its purpose is to detect network anomalies
and to produce recommended and/or automated decisions, based on accepted operational
rules, targeted to system managers. Note the question of whether all RTMDSS
recommendations must be implemented by an operator is subject to the requirements of
the system(s) to be developed.
Figure 1-1 presents a conceptual view of the flow of RTMDSS inputs and outputs with respect to
its users.
Real-Time Multimodal DSS Concept of Operations (Final Draft)
1-3
Referenced Documents
May 17, 2011
Figure 1-1: Conceptual View of an RTMDSS with Inputs, Processing, and Outputs
Note the difference between “active” RTMDSS outputs, which directly influence traffic control
systems and devices, and “passive” outputs, which provide information to transportation system
users and managers in human-understandable form (e.g., e-mail, text messages, web pages,
integrated voice response, in-vehicle signing). System “users” are divided into commercial and
fleet users vs. private users. “Managers” can be divided into single mode managers (freeway,
arterial, transit, etc.) and multimodal managers. The latter are not yet commonly found but will
become increasingly needed as RTMDSS systems mature.
A number of technologies and methodologies can be used in the transformation process that
takes data from input systems and processes it through modeling, expert systems, or algorithmic
methods to achieve a transformation into actions or recommendations that can be accepted,
modified, or rejected. One of the defining characteristics of a RTMDSS is visualization (that is,
how the RTMDSS solution sets are presented to users and managers), making human factor
design issues particularly important. As the Connected Vehicle becomes more prevalent,
vehicle-human factors will impact both safety and driver performance.
System Managers, represented in red in the figure, can use RTMDSS output to monitor system
performance (passive) which in turn leads to operational (tactical) and policy (strategic)
adjustments to the regional transportation system. Depending on the mode, some System
Managers (e.g., city traffic engineers and transit dispatch supervisors) have direct control
responsibilities and can accept, modify, or reject control (active) outputs from the RTMDSS.
Regional policy will dictate whether control outputs are executed automatically, without human
intervention. Legal and liability issues are the major barriers to this mode of operation.
Real-Time Multimodal DSS Concept of Operations (Final Draft)
1-4
Referenced Documents
2
May 17, 2011
Section 2 – Referenced Documents
The following are supporting references:
1. Transit Operations Decision Support Systems (TODSS) Core Requirements Prototype
Development Case Study, 2010 FTA-IL-26-7009-2009.2
2. Transit Operations Decision Support Systems (TODSS) Core Requirements Evaluation
and Update Recommendations, 2010 FTA-IL-26-7009-2009.1
3. iFlorida Model Deployment Final Evaluation Report, FHWA-HOP-08-050, 2009
4. VII Data Characteristics Task: Data Needs Assessment, Dr. Emily Parkany, P.E., and
Phil Tarnoff, Federal Highway Administration Contract No.: DTFH61-05-D-00002
Project No.:04050031-01, Mitretek Systems 2006
5. VII Data Characteristics for Traffic Management and Traveler Information Strawman
Measures Explanation Memo, Dr. Emily Parkany, P.E, and. Phil Tarnoff, for Mitretek
Systems
6. Assessment of Emerging Opportunities for Real-Time, Multimodal Decision Support
Systems in Transportation Operations. Concept Definition and Current Practice Report.
Science Applications International Corporation
Real-Time Multimodal DSS Concept of Operations (Final Draft)
2-1
Current System or Situation
3
Section 3 – Current System or Situation
3.1
Background, Objectives and Scope
May 17, 2011
In the world of transportation systems operations, emerging infrastructure-based sensor
technologies and in-vehicle technologies are providing new data streams to support
transportation operations decision-making. Increasingly complex and capable system control
technologies and traveler systems present transportation managers with a broader range of
potential actions to impact system performance. In many cases, this includes a new capability to
act with increasing precision with a shorter response time. At the same time, there is an
emerging recognition that in complex systems individual mode control decisions cannot be made
independently. Any single modal decision may impact broader system performance positively or
negatively. This leads to the consideration of how concurrent decisions may be made across
modal, facility, institutional and jurisdictional boundaries to optimize performance across the
entire multimodal transportation system.
Given the above, the USDOT has initiated this foundational study to investigate answers to the
following questions:

Do current management practices take full advantage of new transportation operations
control technologies, tools, and data sources (e.g., Connected Vehicle)?
 What emerging opportunities exist for the new generation of automated or semiautomated decision support tools and Decision Support Systems (DSS)?
 Is there strong motivation for a federal role in facilitating the development of new forms
of decision support tools to capitalize on these identified opportunities?
The first task of the project involved a scan of both national and international sources. This
included academia, USDOT Programs such as Transit Operations Decision Support Systems
(TODSS), Integrated Corridor Management (ICM), Connected Vehicle, Dynamic Mobility
Applications, and research conducted by the Transportation Research Board (TRB) and other
associations. The scan was used to develop the concept definition which identifies the situations
and scenarios in which a multi-facility DSS tool could be expected to improve system
performance. The scan also includes lessons learned to date. Literature research and telephone
interviews with selected experienced agency staff contributed to this assessment.1
A stakeholder group has been established which encompasses multimodal transportation
professionals, academics and vendors of software that support Decision Support Systems. A list
of the participants is included in Appendix A. The stakeholder group contributes experience and
understanding and collaborates on the project through a series of webinars and workshops.
The Concept of Operations (ConOps) is being used to document the intended purpose, goals and
objectives and expected capabilities of a Real-Time Multimodal Decision Support System. The
purpose of the ConOps is:

To ensure that stakeholder needs and expectations are captured early;
1
Assessment of Emerging Opportunities for Real-Time, Multimodal Decision Support Systems in Transportation
Operations. Concept Definition and Current Practice Report. Science Applications International Corporation, Report
No. FHWA
Real-Time Multimodal DSS Concept of Operations (Final Draft)
3-1
Current System or Situation
May 17, 2011
 To ensure that the implementation is linked to agency(ies) mission(s), goals, and
objectives;
 To identify existing operational environment and operations;
 To identify where the RTMDSS could enhance existing operations, plans, or functions;
 To illustrate the future operational environment(s) with the RTMDSS (i.e., all of the
functional parts that will be needed to operate); and
 To establish a list of high level operational requirements.
When systems engineering is applied to project development and implementation, the ConOps is
normally grounded in a specific environment. In this case, due to the foundational nature of this
study, the ConOps is being developed as a generic application. This could be viewed as a family
of hypothetical systems. However, to lend reality to the application(s) and fully engage
stakeholder discussion and input, the ConOps will be developed around five scenarios that reflect
real world conditions but are not based on specific locations. The scenarios will be used to
illustrate the potential variants of the concept.
3.2
Operational Policies and Constraints
The deployment of decision support systems, multi-modal or otherwise, may be limited by many
factors. The following are examples that have been encountered:








Limitations in data provided by one or more modes of transportation – typically arterial
data is a limiting factor but lack of full integration of field element systems also
contributes to the problems, as does partial data for transit systems
Communications network limitations i.e. interfaces are lacking between mode specific
systems
Lack of standardization of data flows- implementation of standards such as TMDD and
others support the way forward but have yet to be fully deployed
Shortages in properly-trained personnel
Data gaps caused by underfunded system maintenance
Reluctance of certain modal operators to trust the control results of DSS and inability to
fine tune to suit their preferences and experience
Inherent limitations of certain transportation modes, e.g. city TMC’s not monitored on a
continuous basis, legacy signal systems which cannot be monitored or manipulated
automatically, rail and transit information not fully and accurately available in real-time,
public safety systems using different protocols.
Lack of stakeholder organization and awareness – the Integrated Corridor Management
Pilot programs expose the need and extent of cooperation needed
Real-Time Multimodal DSS Concept of Operations (Final Draft)
3-2
Current System or Situation
3.3
May 17, 2011
Description of the Current System or Situation
3.3.1 Existing RTMDSS Technologies and Methodologies
The Concept Definition and Current Practice Report identified the following technologies and
methodologies as being in current use for specific real-time traffic management applications. They
include:










Predictive travel time calculations
Adaptive ramp metering using predictive traffic congestion algorithms
Intelligence-based Transit DSS
Online travel information systems
Dynamic emergency vehicle routing
Dynamic route guidance
Emissions management
Accident response strategy assessments
Urban and interurban congestion management
Security threat mitigation and large-scale evacuation management
The DSS technologies and methodologies outlined below are organized as follows:



Tables-based DSS Systems (e.g., Toronto COMPASS, KC Scout, GDOT NaviGAtor).
These technologies and/or methodologies are data tables with predefined response plan
recommendations and require little to no processing, modeling or analysis. Some may
include basic logic to analyze data in the tables, while others are purely lookup tables.
Knowledge-driven DSS, which includes:
o Expert System DSS Systems (e.g., Caltrans ATMS, St Louis Gateway Guide). This DSS
requires an expert system engine to generate recommendations for response plans based
on a set of pre-defined rules.
o Custom Rules-based DSS Systems (e.g., ODOT Transport, PACE TODSS2). Similar
to the Expert System DSS, the Custom Rules DSS uses specific rules to determine
response plans. The difference is that the rules are custom built rather than having an
expert system engine.
o Event Scenario Matrix (e.g., Lake County Passage, Michigan ATMS, New Jersey ATMS).
Planned or unplanned events are identified on the roadway using map coordinates
such as latitude/longitude or another plane coordinate systems and users are able to
respond to the events using the predefined ITS field devices along the roadway.
Model-Driven DSS which incorporate on-line simulation tool integration (e.g., Singapore
– Green LInk DEtermination (GLIDE) Traffic Control System, Madrid, Beijing, Milan).
2
Transit Operations Decision Support Systems (TODSS), Core Requirements Prototype Development Case Study,
2010 FTA-IL-26-7009-2009.1
Real-Time Multimodal DSS Concept of Operations (Final Draft)
3-3
Current System or Situation


May 17, 2011
Data Driven DSS, which is a form of support system that will focus on the provision of
internal and sometimes external data to aid in the decision making. Sometimes this
comes in the form of a data warehouse, e.g. a database designed to store data in such a
way as to allow for its querying and analysis by users.3
Hybrids of the above.
3.3.2 DSS Implementation
The scan of current practice summarized the state of the current practice and concluded that there
are few true “active” real-time multimodal decision support systems in the U.S. that account for
all available modes of travel. The closest systems to this goal are the under development
Integrated Corridor Management (ICM) Systems previously reviewed to be deployed in two
locations: Dallas, Texas and San Diego, California. Even with these latest developments in
thinking, there is no uniform commitment to build a true multimodal decision support system.
There are a few cases which integrate two modes to some degree (e.g., incident management
routing using both freeway and arterial routes). One of the more interesting systems studied was
the Chicago Metro area’s Transit Operations Decision Support System (TODSS) that uses
external feeds of freeway and arterial data to implement operation rules for the Pace Suburban
Bus System4. In some systems surveyed, there was no decision support system, but the elements
to support such a system existed.
Most existing decision support systems identified were associated with freeway management
systems – from a “lessons learned” perspective, these deployments can be extrapolated to future
multimodal systems. A major finding from the scan of current practice is that although Decision
Support Systems are not unheard of in the United States, few, if any, can be called truly real-time
or multi-modal by the definition advanced previously in this document. The purest forms of
DSS in most ITS projects are device control (typically motorist information systems such as
DMS and HAR), operator call-outs, and action recommendations in the freeway management
system environment. In most, if not all, ATMS systems, even basic control mechanisms such as
ramp meter plans and traffic signal control plans are not executed under a DSS umbrella.
Another finding is that many DSS implementations are effective (albeit relatively
unsophisticated) in their operation, but lack a simple interface for operators and administrators to
reconfigure the DSS business rules. Difficulty in maintaining DSS systems and lack of operator
training are common shortcomings in the current practice.
Some systems surveyed did not have automated DSS capabilities, but had procedures in place to
perform manual DSS activities. This is a positive indicator that a future DSS, implemented
using sound system engineering processes, would be successful. Any system that plans to divert
traffic from freeways to arterials conceptually requires a joint freeway/arterial decision support
system. Any system that seeks to optimize transit service needs to include a joint transit,
freeway, and arterial decision support system. The decision support systems surveyed in the
scan of current practice were limited to providing the following services:
3
Assessment of Emerging Opportunities for Real-Time, Multimodal Decision Support Systems in Transportation
Operations. Concept Definition and Current Practice Report. Science Applications International Corporation,
report No FHWA
4
Transit Operations Decision Support Systems (TODSS), Core Evaluation and Update Recommendations, 2010
FTA-IL-26-7009-2009.2
Real-Time Multimodal DSS Concept of Operations (Final Draft)
3-4
Current System or Situation
May 17, 2011
:






Dissemination of en-route traveler information using ITS devices:
o Dynamic Message Sign (DMS) location selection for activation
o DMS message library selection or dynamic message generation
o Highway Advisory Radio (HAR) station selection and message generation
Operator recommended actions (contact service patrols, emergency responders, etc.)
Key personnel notification (E-mail, phone, text message, etc.)
Traveler Information Dissemination (Web and IVR)
Transit route adjustments or action plans to address service interruptions
Treatment plans to mitigate the impact of winter weather on road conditions
The existence and importance of “passive” DSS systems (e.g., 511 traveler information websites,
Interactive Voice Responsive (IVR) systems, and trip planners) was also noted. These systems
provide data to end users and aid in decisions related to route, time of travel, and mode choice.
3.3.3 RTMDSS Capabilities
In the context of vehicle-to-vehicle and vehicle-to-infrastructure communication, Parkany and
Tarnoff, 20065 considered the capabilities of such tools first for freeways and concluded that
they can include:

Automated incident detection, assessment, forecasting, traveler information and audits6.
This capability would be used to influence service patrol operations, the installation of
automated surveillance equipment and traffic control strategies.
 Decision support tools that can provide the capability of rapidly evaluating the impact of
alternative incident response measures including diversion routes. This tool is likely to
include a suite of faster-than-real-time simulation techniques that will permit the
modeling of the impact of the incident response alternatives.
 Provide data needed to automatically modify ramp metering rates and traffic signal
timing in the presence of an incident.
 Software that records Transportation Operations Center (TOC) operator’s actions during
incidents for subsequent evaluation and use in response to similar incidents.
They also considered arterial and freeway management for corridor management applications
where real time measurement of volumes, speeds, and travel times could lead to the selection of
ramp and arterial signal timing plans from a suite of pre-determined plans in order to adjust load
balancing to maximize the efficient use of roadway capacity. To this concept they added
enhanced transit operations to be achieved through provision of transit priority and unimpeded
busways.
The Pioneer Demonstration Projects for Integrated Corridor Management are now moving these
concepts forward to implementation. In the course of doing so, demand and capacity strategies
are being recognized as being mutually supportive. Demand management includes vanpooling,
5
VII Data Characteristics Task: Data Needs Assessment, Dr. Emily Parkany, P.E., and Phil Tarnoff, Federal Highway
Administration Contract No.: DTFH61-05-D-00002 Project No.:04050031-01, Mitretek Systems 2006
6
The audit reference in this context refers to performance measures.
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Current System or Situation
May 17, 2011
mode shift with smart parking, ramp metering, and congestion pricing. Capacity management
includes managed lanes, traffic signal synchronization, transit service changes, and transit signal
priority. RTMDSS tools are needed to support these activities in real time and also for the
assessment of long term strategies where multimodal policy interactions are considered.
However, corridors do not represent the limits of RTMDSS applications. The iFlorida Model
Deployment Final Evaluation Report (2009) covers a full range of findings associated with
among other things, the iFlorida plans for (i) metropolitan operations (ii) statewide operations
and (iii) evacuation operations.7 The report recounts details of the challenges encountered during
this project. The lessons learned are certainly that the demand exists for efficiently operating
multimodal interfaces by multiple stakeholders for the purposes of decision support automated or
otherwise. However, perhaps the clearest finding is the need to fully utilize systems engineering
for the design and implementation of such systems and to implement and fully test systems on an
incremental basis to ensure their reliability and accuracy. Fully operational single mode systems
to support decision making are required as a building block before integrated multimodal
systems can be developed.
3.4
Classes and Other Involved Personnel
Currently, those involved with DSS systems and solutions are the trained operators of freeway
control systems in Traffic Management Centers, centralized arterial control systems, roadway
maintenance systems and central dispatch operations for transit and rail systems. In addition
there is necessary communication with Highway Patrols, first responders, highway maintenance
services and motorist support services. State DOTs, City Public Works, Emergency
Management and preparedness staff, Toll Road operations, parking management and Traveler
Information Service providers are all involved or impacted by the users of DSS. However, each
group of personnel is trained to operate within its own mode or operations-specific environment.
Recognition of the need for cross-training of staff to include the impacts of DSS decisions on
other systems appears to be in its infancy. However the Integrated Corridor Management
Projects will clearly require it.
3.5 Support Environment
Because of the generic nature of this ConOps it is necessary to describe a support environment
for a potential RTMDSS in terms that are recognizable but without a specific geographic
reference. Table 3-1 below gives an example based on assumptions regarding a large scale
environment.
7
iFlorida Model Deployment Final Evaluation Report, FHWA-HOP-08-050, 2009
Real-Time Multimodal DSS Concept of Operations (Final Draft)
3-6
Current System or Situation
May 17, 2011
Table 3-1: Generic Support Environment – Agency Roles and Responsibilities
Stakeholder
Regional/State
Department of
Transportation for
state with mature
ITS systems
Roles and Responsibilities
Coverage
 Level of staffing for operational coverage of network is determined by risk
assessment (decision cycles)
 24 hours x 7-days/week x 365 days per year coverage (in-person or oncall)
 Freeways and interchanges/ramps connecting to other networks, state
highways, and major arterials (automated or manual)
Monitoring
 State CCTV video
 Regional CCTV video
 Occurrence of incidents that affect travel – emergency centers, state
patrols, 911
 Traffic flow conditions including HOV lanes
 Weather and emergency events
 Incidents/events on other agency networks
 Parking management
 Bridges and tunnels (including tunnel security systems)
 Connected Vehicle and traveler data
 Pavement conditions
 Security systems
 Communications
 Traffic signal status
 Field infrastructure (network monitoring) – all devices & systems (TSS,
DMS, CCTV, VDS, HAR, RMS, VSL, LCS)
Coordination
 Coordinate construction and lane closures with regional Districts and
municipalities
 Coordinate regional special events
 Coordinate recurring congestion traffic management
 Coordinate roadside assistance services with responsible party
 Coordinate with emergency responders
 Participate in CDM
 Coordinate with freight dispatchers
 Coordinate with TIM Team configuration management
Information Distribution
 Distribute travel conditions to 511 systems, in-vehicle display, and media
 Distribute travel messages and advisories using DMS, HAR, ISPs, and invehicle display
 Distribute incident/congestion updates to partner agencies
 Distribute transit parking information to parking management systems
Operation & Maintenance
 Perform routine maintenance (field, TMC, SW, Comm)
 Emergency maintenance
 Winter maintenance (plow snow)
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Current System or Situation
May 17, 2011
Stakeholder




Roles and Responsibilities
Maintain communications in support of the above
Operate/maintain data network
Repair/replace malfunctioning devices
Have long term maintenance/replacement plan in place for all above
systems
Large city –
extensive arterial
network with
central system
control
Coverage
 X –person operational coverage of network
 12-14 hours x 7-days/week x 365 days per year coverage
 Monitoring of significant arterial streets within the city
 Ability to view but not control regional DOT systems
Monitoring
 City CCTV video
 Regional CCTV video
 Occurrence of incidents that affect travel – through 911 and emergency
centers
 Arterial flow traffic conditions
 Signal system health and status
 Weather and emergency events
 Parking availability and use
Coordination
 Coordinate construction and lane closures with construction and
maintenance offices
 Coordinate event management
 Coordinate recurring congestion traffic management (with partners to be
added)
 Coordinate transit signal priority with city and regional rail systems
Information Distribution
 Distribute arterial travel conditions to 511, other outlets and media via
regional network
 Distribute travel messages and advisories using DMS
 Distribute parking availability information via DMS and handheld devices
Maintenance
 Perform routine maintenance
 Maintain communications in support of the above regional network
 Repair signal systems and communication failures
 Repair/replace malfunctioning intersection equipment
Regional/local
transit agency
Coverage
All
 7 days/week x 365 days/year coverage during service hours
 7 days/week x 365 days/year coverage at customer service call centers
during service hours (at a minimum)
Rail
 Light rail, heavy rail, and commuter rail routes and stations
Bus
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Current System or Situation
Stakeholder
May 17, 2011
Roles and Responsibilities
 All bus routes owned and operated by the agency
Monitoring
All
 CCTV video, stations, in-vehicle
 Regional CCTV video available via regional network
 Occurrence of incidents that affect travel – though 911 emergency
centers
 Traffic flow conditions includes regional freeways and arterials to
extent regional network carries information
 Weather and emergency events
 Park & Ride CCTV
 AVL
Bus
 Bus schedule/headway adherence/status
 Bus boarding/alighting real time/not real time
 In bus security real time
 Vehicle emergency status (voice communication with operator)
 Vehicle status
Rail
 Rail schedule/headway adherence/status
 Boarding/alighting real time/not real time
 Train emergency status (voice communication with operator)
Coordination
All
 Coordinate construction, maintenance and service disruptions with
construction and maintenance offices
 Coordinate event management
 Coordinate recurring congestion traffic management with regional
DOT, City DOTs and signal control centers
 Coordinate transit signal priority with city transit systems
Bus
 Coordinate transit signal priority with city signal control
systems/centers
 Coordinate fare transit transfer systems (i.e., smartcards across
systems)
 Coordinate pricing strategies
Rail
 Coordinate rail signal priority with city signal control systems/centers
 Coordinate fare transit transfer systems (i.e., smartcards across
systems)
 Coordinate pricing strategies
Information Distribution
 Distribute transit travel conditions to 511, other outlets and media via
regional network
 Distribute travel messages and advisories using transit station, parking lot
station DMS and PA systems
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Current System or Situation
Stakeholder
May 17, 2011
Roles and Responsibilities
 Provide trip-planning services via website, call center and interactive voice
recognition systems
 Park and Ride parking availability in real time
Maintenance
 Perform routine maintenance
 Repair in-vehicle system and communication failures
 Maintain systems that input data to regional network
Local tollway
system






X CCTV cameras and X DMS
TMC monitors for incidents and posts DMS messages
Uses automated toll tags
Contracts for courtesy patrols
Contracts for law enforcement
Not integrated with other regional systems
Local regional
commuter rail
system


Operated on tracks owned by others
No real time GPS information
Other cities



Older signal systems
No central operations
Low level of staffing and monitoring
Other local transit
operations


Multiple operations
No automated transit dispatch to central system
Law enforcement
(e.g., state highway
patrol)






Patrol roads for public safety and law enforcement
Respond to incidents
Establish initial traffic control
Render aid prior to EMT/fire arrival
Scene security
Establish and/or participate in Incident Command, compliant with National
Incident Management System (NIMS) and National Response Plan
Coordinate with other responding agencies
Investigate incident
Help manage traffic around incident



Fire department







Public Safety
Answering Point
(PSAPs) –
Counties, etc.



Respond to incidents
Establish traffic control by truck placement and light discipline
Render aid and rescue services
Patient care and transportation
Establish and/or participate in Incident Command, compliant with National
Incident Management System (NIMS) and National Response Plan
Coordinate with other responding agencies
Mitigate fire and/or HazMat situations
24 hours x 7-days/week x 365 days per year coverage
911 call-taker is initial point of contact—determine nature and appropriate
agency(ies) to receive call
Dispatch units, including police, fire, EMS, HazMat, DOT, Animal Control
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Current System or Situation
Stakeholder




May 17, 2011
Roles and Responsibilities
Respond to additional requests for equipment
Manage resources
Can upgrade to EOC as needed (based on local design)
Ability to handle overflow calls from adjoining PSAPs due to failure or
overload
3.6 Conclusions Regarding the Current Situation
The answer to the first question posed for this study:
Do current management practices take full advantage of new transportation operations
control technologies, tools, and data sources (e.g., Connected Vehicle)?
Must clearly be No; the current state of the practice has yet to exploit the full potential.
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Justification For and Nature of Changes
4
May 17, 2011
Justification For and Nature of Changes
Section 4 describes the known limitations of existing DSS type systems and considers the
justification for the new or modified Real-Time Multimodal DSS. The section represents the
transition from the system today to the system(s) desired for the future. Under desired changes
we may expect: capability changes; system processing changes; interface changes; personnel
changes; operational environment changes; support changes and other changes. To help guide
future development, discussion of priorities amongst those changes is called for. Usually in such
documents it is also considered appropriate to note any changes considered but not included.
This serves as a record of discussions that took place and the reasons why they were not pursued.
Finally this section will note both assumptions and constraints, e.g., the time period over which
such changes might be needed and introduced. Section 4 is a precursor to the Gap Analysis task
and should be linked to the Gap Analysis Report.
4.1 Justification for Changes
This ConOps is intended to be a generic ConOps, non-specific to any single regional deployment
or group of stakeholders. This section discusses common, all-purpose justification for changes
as seen in the general transportation and ITS marketplace, with particular focus toward known
systems in the US market.
The known rationalizations for implementing Real-Time Multimodal DSS (RTMDSS) are as
follows:
1. In recent years, millions of dollars in technology and program investments have been
spent to help reduce increasing congestion problems in corridor and regions. These range
from improved single-mode management systems (freeway, arterial, transit, etc.),
ridesharing programs, ramp metering systems, to various forms of managed lanes, to
improved transit services, and electronically managed parking availability. Although
these improvements have shown some benefit to the traveling public, they are seen as too
narrow in focus since they only attempt to improve one single mode or operational
practice. Approaches that account for all modes are viewed to have potential for far more
overall improvement. This is a serious topic that requires further discussion.
2. Emerging infrastructure-based sensor technologies and in-vehicle technologies (e.g.,
Connected Vehicle) are providing new data streams to support transportation operations
decision-making. Based on this, true RTMDSS is more viable to explore than ever.
3. Increasingly complex and capable system control technologies and traveler systems now
exist to present transportation managers with a broader range of potential actions to
impact system performance. This includes a new capability to act with increasing
precision with a shorter response time, especially with solutions that help multiple modes
and facilities. At the same time, there is an emerging recognition that in complex
systems individual mode control decisions cannot be made independently.
4. Recent system analysis projects, including road network microsimulation and Corridor
System Management Plans (CSMP), have demonstrated that many single modal
decisions may impact broader system performance positively or negatively. This leads to
the consideration of how concurrent decisions may be made across modal, facility,
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Justification For and Nature of Changes
May 17, 2011
institutional and jurisdictional boundaries to optimize performance across the entire
multimodal transportation system.
4.2 Description of Desired Changes
In order to implement true RTMDSS, the following changes must occur. Note that these
changes represent those that typically would be needed, but not all of these items may
necessarily be required in all deployment locations or scenarios.

Establish Information Sharing System - Independent agency systems will need to
begin freely exchanging information among one another, in real time, following standard
data exchange methodologies (e.g., TMDD and NTCIP Center-To-Center guidelines).
The information exchange should allow for recommendation of decision support
responses to agency systems, potentially including the direct control of field traffic
management assets.

Implementation of a Region-wide Communications Network - To collect sufficient
data and to enable the regional transportation network, a relatively high-bandwidth
communications network is a required. The network may be provided by the Internet, a
common carrier, or agency-owned infrastructure, but is necessary to move data and
imagery between management centers. The Internet is increasingly becoming the
network of choice for many transportation applications.

Create Multimodal Historical Database - Containing road condition data, regional ITS
configuration data, response plan information etc. Most regions have legacy data
repositories in existing freeway, arterial and transit management systems. Integrating
different data sets and formats is a technical challenge that must be overcome to
effectively implement an RTMDSS.

Visualization Platform - In order to effectively evaluate RTMDSS recommendations, a
method of visualizing transportation network conditions, response plans and generated
actions is preferred.

Enable/Create Common Operating Picture (COP) - To effectively implement
RTMDSS, the ability of multiple agencies to see data from individual modal management
systems on a common geo-referenced display (adaptable to large screen walls, desktop
workstations, and mobile devices) is desired. The COP can be tailored to the needs of the
transportation system manager to show only the data layers needed for effective decision
support.

True System Interoperability - Interoperability will be a key element in the regional
integration of Decision Support Systems. Two key standards for both collecting data and
sending control commands are the Transportation Management Data Dictionary, Version
3.0 and J2735. These standards will be implemented in the Dallas and San Diego
Integrated Corridor Management Systems, which are expected to be leaders in the
development of newer generation DSS systems. Also to be taken into account for
interoperability will be the NTCIP device communications standards.

Improved Logistical Support - As with any other ITS technology, DSS implementation
must be carried out using solid system engineering principles with due attention paid to
logistical support requirements. These include proper documentation, operator selection
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Justification For and Nature of Changes
May 17, 2011
and training, an Operations and Maintenance Plan and ongoing configuration
management, both at the system and operational levels.

On-line Modeling Capabilities - To enhance the capability to analyze strategies,
perform complex data calculation in real-time and in certain cases enable predictive
capabilities. On-line modeling tools can be an effective way to perform such functions
and in turn are desired for RTMDSS deployments, although not necessary strictly
required.

Predictive Tools - The new genre of intelligent transportation management looks toward
managing by anticipation rather than reaction, i.e., attempting to predict adverse or
negative conditions and then preventing them from occurring or at least lessening their
effects. For these reasons, implementation of real-time or faster than real-time predictive
tools are desired for RTMDSS to continuously provide anticipated network conditions up
to 60 minutes in advance.

Develop a Workflow Engine - The RTMDSS requires a workflow engine that replicates
modal management workflows. The workflow engine is envisaged as the tracking and
sequencing mechanism for what happens when recommended actions are issued. It will
track recommendations and feedback into the recommendation process, e.g. freeway
ramp signal timings may have been changed but a city has not responded by making
requested arterial timing changes leading to serious queues. The engine will be designed
to follow the sequence of events and present modifications.

Create Centralized Rules-Based DSS Engine - At the nucleus of the RTMDSS is a
rules-based DSS engine that links or uses many of the components outlined above. This
engine is envisioned to be an interactive, software-based system that extracts useful
information from a combination of modal data sources and knowledge bases (operational
rules) and converts these into actions and/or recommendations that influence performance
of the transportation network, based on specific rules entered into the engine database.

Create/Train Multimodal Operators – A training program is needed for existing
system operators or new “Multimodal Operators” on how to manage systems across
systems and jurisdictions taking all modes into account.
4.3 Priorities among Changes
The recommended ordered priority for the desired changes outlined above is as follows:
1. Establish Information Sharing System
2. Implementation of a Region-wide Communications Network
3. Create Centralized Rules-Based DSS Engine
4. Create/Train Multimodal Operators
5. Create Multimodal Historical Database
6. Enable/Create Common Operating Picture (COP)
7. Improved Logistical Support
8. True System Interoperability
9. On-line Modeling Capabilities
10. Predictive Tools
11. Develop a Workflow Engine
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Justification For and Nature of Changes
May 17, 2011
12. Visualization Platform
Note: these priorities will be the subject of stakeholder discussions and input.
4.4 Changes Considered but not Included
Not applicable.
4.5 System Needs
The System Needs listed in Table 4-1 were derived based on the foregoing discussion. These
System Needs will become the basis for development of System Requirements for RTMDSS.
Table 4-1: System Needs
System Need
Description
1. Need to access real-time or near real-time
data from multiple sources
Any RTMDSS architecture will require access to
real-time and non real-time data from multiple data
sources
2. Need to access historical (archived) data from
multiple sources
The RTMDSS architecture will also need access to
historical data for trend analysis, both on-line and
off-line modeling and predictive capability
3. Need to access transportation network
configuration data from one or more sources
Transportation network configuration data includes
field device locations, roadway network, bus
routes, and other types of static data
4. Need to provide a display capability with
geographic context for RTMDSS configuration
and control
RTMDSS users will need to visualize current and
predicted network conditions and have access to
RTMDSS user dialogues
5. Need to process real-time and historical input
data to predict future transportation network
conditions
RTMDSS will use real-time and historical data to
predict future conditions using either algorithms
and/or models
6. Need to provide a business process capture
capability to convert input data into actionable
decisions
RTMDSS real-time, historical, and configuration
data will be used to feed a business process that
generates required actions
7. Need to reconfigure the rules that govern
RTMDSS output
RTMDSS rules must be changeable by system
users without undue technical complexity or
outside IT support
8. Need to indirectly control and/or recommend
changes to addressable field devices by system
and jurisdiction
RTMDSS will output recommended and/or indirect
(through modal management systems) changes to
transportation control and motorist notification
devices as part of a response strategy
9. Need to provide a faster than real-time
modeling capability to evaluate alternative
RTMDSS strategies
Pre-stored and dynamic response plans are input
into a faster than real-time model ( a model that
runs fast enough to affect response decisions) for
evaluation and/or selection
10. Need to disseminate RTMDSS output to
multiple system users
RTMDSS results must be communicated to system
users
11. Need to provide persistent data storage
Data storage will be needed for selected real-time,
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Justification For and Nature of Changes
May 17, 2011
historical and configuration data and for RTMDSS
results
12. Need to manage the RTMDSS system
Typical system administration functions are
required to manage and maintain the RTMDSS –
these include security, backups, access control,
communications network monitoring, system
health monitoring, etc.
13. Need to support the RTMDSS system
Support includes documentation, training,
maintenance, and business plans
14. Need to provide a performance
measurement capability
RTMDSS must be able to monitor its performance
and the performance of the transportation network
as a result of DSS recommendations
15. Need to provide a planning function
RTMDSS should provide a capability for system
managers to use the system for a priori planning for
special events, major incidents, work zone
management, etc.
16. Need to implement multi-agency
collaboration platform
A collaboration platform would allow agencies to
communicate in real-time, especially during
management of complex events
Real-Time Multimodal DSS Concept of Operations (Final Draft)
4-5
Concepts for the Proposed System
May 17, 2011
Concepts for the Proposed System
5
Section 5 includes the overview of the family of potential RTMDSS systems with their now
understood background, mission, objectives and scope. In the case of the RTMDSS, part of the
motivation for new systems is likely to include new opportunities for the procurement of real
time data sources and their incorporation into powerful processing systems. This section will
include how the goals for new systems may be achieved, supported by modified strategies,
solutions, tactics, methods and techniques. The scope of the new systems is defined through the
modes of operation, classes of users and interfaces to the operational environment.
5.1 Background, Objectives and Scope
5.1.1 Background
The convergence of sensor, communications, and data processing technologies is providing new
and more capable tools to enhance transportation operations collaboration. These are reflected in
improved situational awareness, more sophisticated system control, and dissemination of richer
traveler information data streams. Transportation system managers across all modes of a
regional network are presented with a broader range of actions and data to impact and monitor
system performance. This includes the capability to act with better information within a shorter
response time. These decisions and resulting actions can no longer be made within the silo of a
single modal management system. A decision for one mode of transportation may impact the
overall system performance of a regional multi-modal transportation network.
There has been a parallel effort by USDOT to emphasize regional operations collaboration. In
short this means that transportation managers must begin to consider broader implications of the
decisions they make to optimize their own operations. The goal is now to establish, measure,
and display performance metrics for an integrated network of freeway, toll facility, managed
lanes, arterial, transit and parking facilities. This was initially embodied in the Integrated
Corridor Management (ICM) environment, but ultimately will go beyond just linear corridors to
include broader areas within a geographic region.
5.1.2 Objectives
The objectives of multi-modal decision support systems include:




The collection and archiving of existing and emerging data sources from all modes of
transportation. The data/information needs to be collected and stored for analyzing the
effectiveness of strategies and responses generated by a DSS.
The transformation of independent data streams into actionable decisions that impact
multiple transportation modes across multiple jurisdictions. The actions need to be
grounded in pre-arranged response plans for incidents and emergencies attached to
contacts, roles and responsibilities.
The ability to impact overall network operations by sending commands/recommendations
to individual systems in individual jurisdictions. The recommended actions seek to avoid
gaps in response as well as contradictory responses, to produce benefit for total network
operations.
While not a central objective, the provision of links to what has been termed passive
RTMDSS will provide for the delivery of a greater quantity and quality of information to
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increasingly sophisticated system users, impacting their driving decisions The links to the
delivery systems and the data that flows to them should be an integral part of the vision
for RTMDSS.
5.1.3 Scope
A multimodal decision support system will impact system operators of all modes of
transportation. Typically these include Transportation Management Center operators, bus and
rail dispatchers, public safety dispatchers, local agency traffic engineers, transit vehicle
operations, private vehicle drivers, fleet and commercial drivers, technical support personnel,
facility maintenance personnel, parking facility operators, toll road operators and 511
information service providers.
5.2 Operational Policies and Constraints
5.2.1 Policies
Over time, operational policies will evolve to exploit the new capabilities of advanced decision
support tools. At initiation, there will likely be resistance to automation of traditional operations.
As confidence in decision support tools and algorithms grows, agencies will be more
comfortable in allowing external influences over their control systems.
Stakeholder organization and commitment to roles and responsibilities is a vital component of
the ConOps for the RTMDSS and should be embedded in the operational scenarios developed.
A structure needs to be developed to map these responsibilities to decision points and then to
actual staffing and services needed. This structure will then be extended to the vision for
financing the components of the system that have yet to be developed, funding training and
staffing the multimodal operations and paying to maintain both the new system elements and
existing system elements at a level that will support the full multi-modal operation.
5.2.2 Constraints
The deployment of multimodal decision support systems will initially be limited by the
following factors:
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Limitations in data provided by one or modes of transportation – typically arterial data
will be a limiting factor
Lack of standardization of data flows in an RTMDSS
Shortages in properly-trained personnel
Reluctance of certain modal operators to trust the control results of an RTMDSS
Inherent limitations of certain transportation modes, e.g. city TMC’s not monitored on a
continuous basis, rail consists not providing accurate and timely train positions, etc.
5.3 Description of the Proposed System
A vision for future RTMDSS implementations includes the following key elements:

The DSS accepts real-time data inputs from multiple modes and transportation network
facilities (see Figure 5-1)
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
Using a variety of transformation tools and algorithms, such as real-time modeling, rulesbased response plans and visualization tools, the DSS generates response plans that
impact multiple modes and systems in multiple jurisdictions (see Figure 5-2)

The response plans are decomposed into system-specific action plans and commands that
are targeted for a single system in a single jurisdiction (see Figure 5-2)

Operators of the agencies review DSS recommended plans

In some systems, real-time or faster than real-time, models show the impact of alternative
strategies on the target network (freeways/toll roads/transit/arterials) in sufficient time for
multimodal system operators to make timely decisions

System operators are specially trained in multimodal operations and have working
knowledge of all transportation modes in the region. In a large metropolitan area, system
operators may be assigned to sub-regions or corridors similar to public safety or transit
dispatchers (Figure 5-1)

The business rules of the DSS are easily modifiable by system operators and/or
administrators

All control commands issued by the DSS adhere to national ITS standards such as
TMDD v3.0. This is a known gap in current operations

Non-response to recommendations results in new computations to take this into account.
Shown in Figure 5-1 is an example of an RTMDSS. For the various modes and transportation
network facilities, there are data sources (data inputs) and control plans. These are color coded
(e.g., items for the freeway are outlined in green, while arterials are outlined in yellow). How the
transmission between the network and the individual network facilities is defined (e.g., via
XML) is not part of this diagram. As stated earlier, data communications will be compliant with
TMDD and other standards.
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Figure 5-1: Notional RTMDSS
Figure 5-2: Response Plan Processing Hierarchy
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The Real-Time Multimodal Decision Support System is actually a family of systems. For
specific geographic areas, system components may include any combination of the following:

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






GIS-based visualization platform (baseline capability)
Source(s) of real-time data (baseline capability)
Source(s) of historical data (baseline capability
Persistent data storage (baseline capability)
Business Process engine (optional capability)
Expert systems or other rule-based engine (baseline capability)
Traffic-responsive algorithms (baseline capability)
Predictive algorithms (baseline capability)
Real-time simulation/tools (optional capability)
Off-line modeling (optional capability in the context of RTMDSS)
A nominal DSS architecture encompassing these components is shown in the figure below.
Figure 5-3: Nominal High-Level Architecture for RTMDSS
These systems are described in the following paragraphs.
Baseline Capability indicates that this component would be required in any RTMDSS
implementation. Optional Capability indicates a component that would enhance RTMDSS
operations but would not necessarily be required.
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5.3.1 GIS-Based Visualization Platform
The essence of providing decision support to transportation managers is giving each of them a
“common operating picture”, or COP. A City Traffic Manager can more easily make arterial
traffic signal timing decisions knowing the “big picture” of what’s going on around his
jurisdiction. Likewise a transit operations manager can make better bus allocation decisions
knowing the most current and complete picture of road conditions on the routes being managed.
No matter what combination of DSS components may be used in a particular region, operator
dialogs are required to manage input data to supplement automatic data feeds, configure
operational rules, review and/or modify recommended response plans and their component
action plans, initialize real-time models, configure predictive algorithms, and similar functions.
5.3.2 Source(s) of Real-Time Data
Any DSS will require real-time data of traffic conditions on freeways and roadways, bus
locations and schedule adherence, passengers and capacity, scheduled and non-scheduled events,
weather, parking availability, responses to events and field device location and status. Each
modal system will have its native or organic database and thus the challenge will be to integrate
these data sources as inputs to the decision support engine.
5.3.3 Source(s) of Historical or Archived Data
Historical or archived data is essential to the operation of an RTMDSS as it establishes traffic
patterns for various operational conditions of a region’s transportation network. In some cases
an archive database may be available for use but in other cases, it may have to be created from
scratch. As with real-time data, the challenge most regions will face is to integrate multiple data
sources.
5.3.4 Persistent Data Storage
RTMDSS persistent data storage will be required to store the following classes of data:

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



Pre-determined response plans
Transportation network metrics
RTMDSS system metrics
Event histories including actions recommended, actions taken and results of the actions
Model results
Selected real-time data
Selected archival data
5.3.5 Business Process Engine
A business process engine is the high-level view of the activities undertaken by a modal operator
as illustrated in Figure 5-4. This example is for a notional freeway management system. This
process depicts a routine monitoring function for a TMC operator that includes publishing of
travel times, operating normal AM and PM peak ramp meters and managing lanes equipped with
appropriate signing and directional control. TMC operators continuously monitor for the onset
of an incident – if this occurs, a sequence of activities is set into motion that may include
overriding of normal field device operation. Routine operations are shown in green; event
detection and verification are shown in blue, and the event response is shown in red.
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5.3.6 Expert Systems or Other Rule-Based Systems
Rule-based systems have traditionally been the core of existing DSS implementations. In Figure
5-4 below, a Rule Base would operate at the “Generate Response” step in the freeway
management process and is shown in red in the figure. In some cases these rule bases have been
implemented in COTS expert systems, and in other cases, by custom development. Expert
systems combine knowledge of the environment (event states, field device placement, network
nodes and links, etc.), known as a Knowledge Base with a Rule Base containing IF-THEN or
similar logical constructs to implement well-defined operational processes. Rule bases work best
for processes that are clearly definable, relatively straightforward in execution and that embody
repeatable processes. For example, DMS signing and where to place messages on the
transportation network is a classic use of expert systems and rule-based systems. Another
appropriate use of a Rules-Based system would be to select a pre-determined signal timing plan
from a set of such plans in response to an incident at a known location.
Figure 5-4: Notional Example of a Freeway Management Business Process
5.3.7 Traffic Responsive and Other Predictive Algorithms
Predictive algorithms differ from rule bases in that they largely rely on mathematical models of
the environment rather than knowledge and rules. Adaptive traffic signals, traffic-responsive
ramp meters, point predictions of detector data( i.e. predicting what volume/occupancy and
speed at a particular location will be) and travel time calculations are examples of the use of
algorithmic data.
5.3.8 Faster Than Real-Time Modeling
Faster than real-time modeling may not be present in every deployed RTMDSS. Modeling is a
highly-specialized skill, and this is especially true for the newer generation of real-time models
that provide more timely information but that require more skill sets to configure and initialize.
A faster than real-time model will analyze both real-time and archived data, fetch pre-stored
response plans or strategies, evaluate these strategies against regional measures of effectiveness,
select one or more optimum strategies and predict network conditions into a selectable future
time interval.
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5.3.9 Off-Line Modeling
Off-Line modeling will be used to develop input data for real-time models, to forecast long-term
traffic patterns, to develop pre-determined response plan strategies and to support regional
transportation planning. Off-Line modeling will also be used to support longer-term decisions
related to management of a corridor or other defined area within a geographic region.
5.4 Modes of Operation
Two types of operations are broadly envisaged, however the underlying principles of
coordination between systems remains the same:
1. Normal operations that involve exchange of information across all involved modes and
are based on everyday occurrences in terms of congestion, incidents, planned
maintenance and events. Such operations may be viewed as routine and with emphasis
on peak period operations but also require agreed cooperation and coordination.
Agreements and management strategies would need to be in place to promote this
everyday level of decision support across all modes and gradual improvement over time
as confidence increased in the reliability of the available tools. Such DSS agreements are
associated with corridor and or local regional activities.
2. Extreme event management (e.g., hurricanes, winter weather events, wildfires, terrorist
attacks) builds upon the normal operations agreements, but calls for more detailed
agreements and action plans. Extreme event management potentially involves a wider set
of partners (across state line activities and statewide agencies) and may or may not have
more planned action time for the implementation of the DSS-proposed recommendations.
Such RTMDSS may be required to operate over far larger areas, with many more
partners and potential system interfaces.
5.5 System User Groups and Other Involved Personnel
RTMDSS users contribute a diverse set of operations and maintenance skills.
summarizes the essential roles of these system users.
Table 5-1
Table 5-1: System User Groups
Position
Position Roles
Broadcast media
Gather information on planned and unplanned corridor events and
inform the public via radio, TV, and satellite
DOT maintenance dispatchers
Dispatch maintenance vehicles and personnel
Highway Patrol/State
Police/Local Police traffic
officers
Incident response and freeway/highway enforcement
Corridor managers
Multi-modal corridor operations management, with a working
knowledge of freeway, arterial, transit, and public safety
operations. Note these would be new roles that for the most part do
not exist today.
District traffic manager(s)
Responsible for maintenance of lane closure systems
Emergency Operations Center
(EOC) watch officer
When the EOC is activated, Intelligence and Planning Section staff
collect data from many sources in the county and use this data to
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Position
EOC planning and
intelligence staff
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Position Roles
develop plans for the next “operational period” (usually 24 hours)
during a major emergency
First Responders (e.g., Fire,
EMT, ME)
Tollway and High Occupancy
Toll lane customer service
center operators
Operate and maintain customer service centers
Roadway Service Patrol
drivers
Perform motorist aid duties on assigned roadway beats
Freight Logistics managers
IT managers
Develop IT hardware and software standards and deploy IT
equipment and applications
IT maintenance staff
Operate and maintain equipment
Parking facility staff
Operate, monitor and maintain parking facilities including park and
ride
Public (end users)
Populate the transportation network
Public safety call takers
Receive calls for assistance from the motoring public via mobile or
landline phones and initiate incident records within a Computer
Aided Dispatch (CAD) system
Public safety dispatchers
Dispatch and track public safety units using CAD systems and
update incident records based on officer field reports
Rail Operations managers
Roadway equipment
maintenance staff
Monitor, troubleshoot, and maintain roadway ITS devices
TMC operators
Operate and monitor freeway and tollway central control systems
Traffic engineers
Maintain traffic signal and ramp metering systems, develop signal
and ramp meter timing plans, and tune system traffic algorithms
Traffic Management Teams
(TMTs)
Transit
Supervisors/dispatchers
Maintain communications with transit drivers and monitor transit
vehicle schedule adherence, breakdowns, on-board incidents, and
safety issues
Transit operators
Operate regional bus vehicles and monitor roadway conditions on
their assigned routes
Transit maintenance
Monitor vehicle health
Transit call centers
Transportation planners
Analyze all modes of transportation operations, run modeling
software, and develop long-term corridor operations strategies
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5.6 Support Environment
The major support impacts resulting from RTMDSS deployment will include:

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Regional communications network (increasing dependency on inter-agency
communications)
Configuration management (changes in one mode’s system impacting other modes) –
includes hardware, communications, software, firmware
Increased investment in maintenance and spares support
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Operational Scenarios
The operational scenarios identified are as follows:
1. The Daily Operations Scenario involves exchange of information across all involved
modes and is based on everyday occurrences in terms of congestion, incidents, planned
maintenance and events. Such operations may be viewed as routine, with emphasis on
peak period operations, but also require agreed cooperation and coordination. This is the
base scenario for all RTMDSS development.
2. Extreme Event Scenarios involve an entirely different scale of response. The following
examples are described in this section:
a. Major traffic incident with impacts to freeways, arterials, transit, and parking
b. Significant immediate evacuation due to a terrorist threat
c. Winter weather event covering wide area
d. Planned major events impacting the downtown area
The scenarios were developed with a year 2020-2025 timeframe and are meant to reflect the
kinds of activities and interactions expected to occur in that time.
6.1 Real-Time Multimodal DSS Assumptions
1. The DSS accepts real-time data inputs from multiple modes of transportation
2. Using a variety of visualization platforms and transformation tools and algorithms, such
as real-time modeling, rules-based response plans and predictive algorithms, the DSS
generates response plans that impact multiple modes and systems in multiple jurisdictions
3. The response plans are decomposed into system-specific action plans and commands that
are targeted for a single system in a single jurisdiction
4. Operators review DSS recommended plans and accept, reject or modify prior to
execution
5. A variety of real-time tools (e.g. simulation, predictive algorithms, rule-based business
processes) determine the impact of alternative strategies on the target network
(freeways/toll roads/transit/arterials) in sufficient time for multimodal system operators to
make timely decisions
6. Visualization of corridor conditions, performance metrics and management tools is an
essential RTMDSS component
7. DSS “system operators” may be used – these would receive training in multimodal
operations and have working knowledge of all transportation modes in the region. In a
large metropolitan area, system operators may be assigned to sub-regions or corridors
similar to public safety or transit dispatchers. Single mode system operators continue to
manage their systems as is currently done.
8. The business rules of the DSS are easily modifiable by end users (system operators
and/or administrators).
9. Selected control commands issued by the DSS adhere to national ITS standards such as
TMDD v3.0. It is desirable that TMDD should eventually cover all control commands.
6.2 “Generic” Scenarios
The purpose of the ConOps is to provide an RTMDSS system definition that can be implemented
in a variety of locations. Thus, the scenarios to be developed will be generic and not locationspecific.
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The generic scenarios should capture the following key aspects:
1. Long-term (10-year plus) time scale for implementation
2. Differences in size and scope
3. Differences in combinations of transportation infrastructure and relative demand of
modal traffic
4. Differences in potential weather impacts
5. Differences in corridor versus regional/statewide needs
6. Differences in organizational/institutional arrangements
6.3 Scenario Analysis
The scenarios are diagrammed using a system information flow diagram format to help indicate
at a high level the systems involved and the flow of data between systems. Each individual
scenario diagram is based on a single, generic system information flow diagram, depicted in
Figure 6-1 on the following page. The generic system information flow diagram is meant to
encapsulate all the critical systems, subsystems, and components with which the RTMDSS
interacts. Individual scenarios feature subsets of this master generic diagram.
Individual scenario diagrams can also be used with a sequencing indicator both to highlight the
key decision points at which a DSS system would add the greatest value and to incorporate timephased event responses.
The information flow diagram is comprised of three major components:
1. The RTMDSS, depicted as a purple box and containing the various components
discussed in detail in Section 5.3;
2. Modal management systems with which the RTMDSS interacts and communicates,
depicted as blue boxes with critical subcomponents listed within; and
3. External entities that do not exchange but only receive (or provide) data, depicted as red
ovals.
Note: the Connected Vehicle box appears in a contrasting color to indicate its critical role
providing the rich traveler and vehicle datasets required of the RTMDSS and connected
management systems.
The scenario diagrams and content were developed with direct RTMDSS Stakeholder input
during the RTMDSS ConOps face-to-face meeting held in Scottsdale, Arizona on May 23-24,
2011. (A full list of stakeholder meeting participants is provided in Appendix A.)
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Figure 6-1: Generic System Information Flow Diagram
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6.3.1 Scenario #1: Daily Operations
Incidents of typical severity occur throughout the transportation network during the peak
evening period. Road congestion and transit delays are typical for the time and day.
Figure 6-2: Scenario 1 System Information Flow Diagram
The RTMDSS receives input from various connected subsystems:
1. Regional and microclimate weather data is passed to the RTMDSS.
2. Local and regional transit CAD/AVL systems regularly monitor bus and rail
arrival/departure information and report transit travel times, schedule adherence, and
vehicle capacities to the RTMDSS.
3. Sensors at park-and-ride facilities and downtown garages monitor the available
parking capacity and reports capacity to the RTMDSS.
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4. Freight and Logistics Management Systems report freight vehicle locations,
schedules, and routing plans.
5. Maintenance and Construction Operations report real-time lane closure and
construction information to the RTMDSS.
6. The Freeway Management System continually reports current freeway volumes and
travel times to the RTMDSS.
7. Incidents in the CAD systems of participating law enforcement agencies, freeway
operators, and transit dispatchers are automatically shared with the RTMDSS.
Utilizing faster than real-time modeling on archived and real-time multimodal data, the
RTMDSS generates recommendations to or indirectly controls various connected subsystems:
8. Transit fleet management and detour routing
9. Freeway ramp metering rates
10. Dynamic message sign content
11. Roadway signal timing and coordination
12. Variable speed limit signs
13. Reversible lane selection
14. HOT lane pricing
15. Dispatch of Traffic Management Team vehicles.
16. Interface to 511 and other ISPs to provide travel time, incident, HOT lane rates,
parking availability, and other traveler information
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6.3.2 Scenario #2: Major Traffic Incident
On a weekday morning a tanker truck overturns on a major highway, leaking fuel and blocking
all lanes of traffic. Fatalities have occurred and the highway will be closed for several hours
while the medical examiner investigates and debris is cleared.
Figure 6-3: Scenario 2 System Information Flow Diagram
Action 1. From 9-1-1 cell phone calls, Highway Patrol is alerted to a possible incident. The
Call Taker creates a new incident and transfers the incident to a dispatcher for Highway Patrol
response. Paramedic units are notified via telephone. The RTMDSS is updated with this
information.
Action 1’. Roadway sensors simultaneously register an incident using freeway incident
detection algorithms.
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Action 2. The RTMDSS acquires the incident data from the Highway Patrol CAD system and
confirms the automatic incident event against the 9-1-1 manual incident entry.
Action 3. Due to the fact that fatalities have occurred, the Emergency Management System
notifies the County Medical Examiner’s Office.
Action 4. Based on analysis of the impact of the incident on the roadway network, the RTMDSS
generates DMS content and pushes requests to Freeway Management Systems to display the new
messages. The RTMDSS continually updates DMS content based on changing roadway network
conditions.
Action 5. The RTMDSS disseminates incident data to 511 and other ISPs, including end users’
in-vehicle navigation and portable personal connected devices. The RTMDSS continually
updates information based on changing transportation network conditions.
Action 6. The Emergency Management System disseminates filtered incident information
directly to media agencies.
Action 7. The RTMDSS disseminates incident data to Freight Logistics Management Systems
to alert them to reroute shipments around the incident area. The RTMDSS continually updates
information based on changing roadway network conditions.
Action 8. The RTMDSS produces stationing recommendations to prevent secondary accidents
at the end of the queue and provides these to the Freeway Management System to deliver to the
Traffic Management Team (TMT). The RTMDSS continually monitors roadway network
conditions and makes updated stationing recommendations as needed.
Action 9. Based on faster than real-time modeling of the incident and its effects on the regional
transportation network, the RTMDSS sends pre-agreed regional timing plan requests to the
Arterial Management System (AMS) for implementation by affected cities within the region.
The RTMDSS continually monitors roadway network conditions and makes updated signal
timing plan recommendations as needed.
Action 10. The RTMDSS recommends transit service adjustments to the Transit Management
System based on faster than real-time modeling. The RTMDSS continually monitors roadway
network conditions and makes updated transit service adjustment recommendations as needed.
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Figure 6-4: Scenario 2 Phasing Diagram
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6.3.3 Scenario #3: Major Evacuation
An emergency evacuation for downtown has been called due to a confirmed building bomb
threat during typical weekday operations. System operators, administrators, and other
stakeholders from freeway, toll road, arterial, public transportation, airport, port, public safety,
commercial vehicle, maintenance and parking modal management systems were involved in
evacuation planning prior to this emergency. As a result, the role of the RTMDSS in such
evacuations has been designed into the overall metropolitan network evacuation scenario.
Figure 6-5: Scenario 3 System Information Flow Diagram
Action 1. The regional Emergency Operations Center (EOC) is activated and representatives
from freeway, toll road, arterial, public transportation, airport, port, and public safety (all
informed of the RTMDSS capabilities and procedures) join others in response to the evacuation.
Action 2. The RTMDSS stakeholder representatives at the EOC have access to the RTMDSS
data hub. These representatives serve as a conduit informing others at the EOC of information
shared through the RTMDSS and entering in any critical information.
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Action 3. The RTMDSS runs faster-than-real-time simulations based on existing conditions to
continually evaluate evacuation alternatives. These analyses are made available to the EOC.
Action 4. The RTMDSS sends flush (outbound) signal timing plan requests to Arterial
Management Systems. The RTMDSS continually monitors roadway network conditions and
makes updated signal timing plan recommendations as needed.
Action 5. The RTMDSS sends outbound reversible HOT lane setup requests to freeway and toll
road operators.
Action 6. The RTMDSS initiates contra-flow freeway evacuation routing plans: alerts law
enforcement to direct overflow outbound traffic onto inbound lanes and to provide traffic control
for the contra-flow traffic.
Action 7. The RTMDSS generates appropriate HAR and DMS evacuation messages and
provides these to Freeway Management Systems. The RTMDSS continually updates DMS
content based on changing roadway network conditions.
Action 8.
The RTMDSS generates transit fleet management and detour routing
recommendations and sends these to Transit Management Systems to implement. The RTMDSS
continually monitors roadway network conditions and makes updated transit service adjustment
recommendations as needed.
Action 9. The RTMDSS disseminates evacuation information to Freight Logistics Management
Systems to alert them not to send vehicles into evacuation area.
Action 10. The RTMDSS disseminates relevant evacuation data to 511 and other ISPs,
including end users’ in-vehicle navigation and portable personal connected devices. The
RTMDSS continually updates information based on changing transportation network conditions.
Figure 6-6: Scenario 3 Phasing Diagram
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6.3.4 Scenario #4: Significant Winter Weather Event
An unexpected snow storm begins on a weekday morning and covers the entire region. Snow is
expected to continue to fall throughout the day. By noon many businesses have decided to close
early for the day and many commuters begin to return home from the downtown area. Many
incidents have occurred throughout the region due to the poor driving conditions, and
congestion is heavy throughout the road network.
Figure 6-7: Scenario 4 System Information Flow Diagram
Action 1. The RTMDSS receives inclement weather information.
Action 2. The RTMDSS generates Highway Advisory Radio (HAR) messages warning travelers
of anticipated worsening weather and passes these to the Freeway Management System. The
RTMDSS continually updates HAR message content based on changing roadway network
conditions.
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Action 3. The RTMDSS recommends that freeway and toll road operators activate the
reversible HOT lane to be outbound earlier than normal, as the peak outbound commute begins
at approximately 11:00am. Dynamic lane pricing adjusts as needed.
Action 4.
The RTMDSS generates transit fleet management and detour routing
recommendations and sends these to Transit Management Systems to implement. The RTMDSS
continually monitors roadway network conditions and makes updated transit service adjustment
recommendations as needed.
Action 5. Representatives from arterial signals groups have been activated and observe the
cameras with an interest on the signal progression. (As a result of the storm, many travelers are
opting not to drive the freeways, but instead to take non-mainstream arterials with the hopes of
avoiding major congestion.)
Action 6. The RTMDSS disseminates weather and congestion information to freight logistics
management systems to alert them to avoid sending vehicles into the road network.
Action 7. RTMDSS recommends that off-peak signal timings with reactive control be left in
place to accommodate the many turning movements not typically observed during peak (flush)
movement. The RTMDSS continually monitors roadway network conditions and makes updated
signal timing plan recommendations as needed.
Action 8. Due to the increased number of major incidents and overall network delays, the
RTMDSS recommends that transit signal priority be de-activated (because the slow travel has
congested the roads so even if signals green phases are extended for a transit vehicle, there is still
congestion ahead of the signal).
Action 9. The RTMDSS disseminates relevant data to 511 and other ISPs, including end users’
in-vehicle navigation and portable personal connected devices. The RTMDSS continually
updates information based on changing transportation network conditions.
Figure 6-8: Scenario 4 Phasing Diagram
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6.3.5 Scenario #5: Special Event
A weeknight baseball game is being played at the downtown stadium. A majority of the
estimated 40,000 attendees will arrive between 5:30 and 6:45pm, overlapping with the evening
peak period commute time. Due to the advanced notice of the event, multimodal system
managers are able to activate advanced plans—making available additional transit and parking
options and coordinating among agencies.
Figure 6-9: Scenario 5 System Information Flow Diagram
1. Pre-event actions
Action 1-1. RTMDSS activates evening baseball game event plan and sends out
coordination recommendations to agencies, including:

Recommend to transit agencies to plan to provide temporary shuttle service from
park-and-ride lots before and after the game.
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Summary of Impacts

May 17, 2011
Recommend to transit and commuter rail agencies to increase frequencies and
capacities of utilized transit and rail lines before and after the game.
Action 1-2. Transit agencies implement service adjustments and update schedule and
detour information in the Transit Management System and provide this data to the
RTMDSS.
Action 1-3. The RTMDSS disseminates special event and anticipated congestion
information to Freight Logistics Management Systems to alert them to reroute vehicles
around the event-impacted areas or otherwise modify their shipping schedules. The
RTMDSS continually updates information based on changing roadway network
conditions.
2. Event actions
Action 2-1. Transit Management Systems activate special event fare collection programs
(e.g., free or reduced fares for ball game ticket holders) for riders traveling to the
stadium.
Action 2-2. The RTMDSS continually pushes event reports to the in-vehicle navigation
and portable personal connected devices of ball game attendees and commuters (e.g.,
location and capacities of park-and-ride lots, congested roads, alternate routes).
Action 2-3. The RTMDSS sends flush (inbound) signal timing plan requests to Arterial
Management Systems for ballpark-connecting arterials.
Action 2-4. RTMDSS makes signal timing plan recommendations to Arterial
Management Systems based on traffic patterns change as the start of the game
approaches. The RTMDSS continually monitors roadway network conditions and makes
updated signal timing plan recommendations as needed.
Action 2-5. The RTMDSS generates appropriate HAR and DMS event messages (e.g.,
parking guidance) and provides these to Freeway Management Systems. The RTMDSS
continually updates HAR and DMS content based on changing roadway network
conditions.
Action 2-6. Event Managers monitor progress of the game and provide the RTMDSS
with anticipated traffic patterns from the stadium. (Due to the closeness of the game, it is
expected that most attendees will stay until its conclusion.)
Action 2-7. Based on input from Event Managers, the RTMDSS recommends to the
Transit Management System to make increased capacities available between 9:00 and
10:00pm, the expected conclusion of the game.
3. Post-event actions
Action 3-1. Transit Management Systems reactivate special event fare collection
programs (e.g., free or reduced fares for ball game ticket holders) at conclusion of event
for riders departing the stadium.
Action 3-2. Based on input from Event Managers, the RTMDSS recommends
appropriate signal timing plans (e.g., outbound flush plans) to Arterial Management
Systems for ballpark-connecting arterials. The RTMDSS continually monitors roadway
network conditions and makes updated signal timing plan recommendations as needed.
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Figure 6-10: Scenario 5 Phasing Diagram
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Summary of Impacts
7
May 17, 2011
Summary of Impacts
Section 7 reviews the potential operational impacts of the family of systems for users,
developers, support and maintenance organizations and the sponsoring institutions. Impacts
during development are also considered.
7.1 Operational Impacts
Modal system managers will have new information and tools at their disposal, necessitating a
different approach to regional collaboration. Transit managers will have more information on
freeway and arterial conditions, including video imagery. Arterial managers will have more
information on the impact of transit priority and rail at-grade intersections. Public safety
dispatchers will have greater visibility into traffic conditions that impact response to incidents
and to the real-time locations of public transportation vehicles. Freeway managers will be able
to more effectively see the impact of traffic diversions for major incidents. All modes will have
a common operating picture of transportation conditions in the region through a GIS-based
visualization platform. Interactions between freeway and arterial management will become more
sophisticated when ramp metering can be adjusted to respond to changing arterial conditions in
the vicinity of on- and off-ramps and arterial traffic systems can respond to planned or unplanned
freeway diversions.
Regions should begin to develop concepts for different operational paradigms, e.g. using
multimodal-qualified “Transportation Coordinators” who will be more focused on operating
multimodal corridors or sub-regions rather than a single mode across the entire region. This
concept will be evolutionary and is not a prerequisite for initial operational capability of an
RTMDSS deployment.
7.2 Organizational Impacts
Jurisdictions working under a true RTMDSS may have to alter their operational procedures to
meet new transportation network measures of effectiveness. At the very least, multimodal
planning will be a necessity and will involve system operators and administrators from freeway,
toll road, arterial, public transportation, airport, port, public safety, commercial vehicle,
maintenance and parking modal management systems as appropriate for the region. Many
regions find it relatively easy to collaborate on transportation planning issues but ongoing
operational collaboration is more difficult to achieve. Each transportation mode has different
restrictions on how their systems can be controlled – these restrictions must be addressed through
multi-jurisdictional agreements that cover financial, legal, liability, privacy and other challenging
issues.
7.3 Procurement/Development Impacts
Several technical issues will have to be undertaken to make true RTMDSS a success. These
include iterative development, communications, data management, web access, interoperability
and logistical support. Each of these will be discussed in turn.
7.3.1 Iterative Development
It is unlikely that a regional RTMDSS deployment can be successfully accomplished with a
monolithic “waterfall” systems engineering approach. An alternative approach is to use iterative
development, not only from an improved systems engineering and project management
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Summary of Impacts
May 17, 2011
perspective, but also for the purpose of iteratively gaining stakeholder support and confidence for
the RTMDSS concept. Strategically the iterative steps for deployment might look something
like Table 7-1. However, this is only a notional table and each region would have legitimate
claim to a different approach to iterative deployments. For purposes of this table, we assume that
a DSS deployment would consist of the following elements:






Database containing road condition data, regional ITS configuration data and response
plans
A method of visualizing transportation network conditions, response plans and generated
actions; optionally a means of operator review and control of response plans
Modeling, both off-line (for response plan development) and real-time (for plan
comparison and optimization)
Real-time predictive tools that continuously provide anticipated network conditions up to
60 minutes in advance
A workflow engine that replicates modal management workflows
A business rules engine that develops collaborative modal actions in response to network
anomalies
Table 7-1: Iterative Deployment Functions and Benefits
Deployment Iteration
Functional Capability
Incremental Benefit

Foundational Work

Database development
visualization platform

Modeling Calibration

Off-line modeling interface to
real-time modeling
Predictive Tool(s) and
Workflow
Development



Real-time modeling
Predictions to +30 min
Workflow capture






Predictions to +60 min
Recommended Action Plans

Automated Action Plans
Initial Operational
Capability (IOC)
Full Operational
Capability (FOC)


Establish use of standards
(e.g., TMDD)
Provide initial operator view
of ICMS
Establish modeling baseline
Calibrate models
Generate response plans
Build operator confidence in
real-time tools
Operator feedback on
workflows

Build operator confidence in
predictions
Build operator confidence in
Action Plans

Full operator confidence
7.3.2 Communications
To collect sufficient data and to affect different elements of the regional transportation network,
a relatively high-bandwidth communications network is a prerequisite. The network, which must
be able to accommodate the exchange of data and imagery between management centers, may be
Internet-based, common carrier-based, or agency-owned infrastructure. The Internet is
increasingly becoming the network of choice for many transportation applications.
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Summary of Impacts
May 17, 2011
7.3.3 Data Management
Most regions have legacy data repositories in existing freeway, arterial and transit management
systems. Integrating different data sets and formats is a technical challenge that must be
overcome to effectively implement a multi-modal DSS. Figure 7-1 shows a typical regional data
architecture in which data from multiple systems, including extra-regional archived data systems
(e.g. in California, the PeMS ITS data archive) are used to drive an RTMDSS.
7.3.4 Web Access
Increasingly ATMS and other modal management systems are turning to Service Oriented
Architectures (SOAs), allowing greater deployment flexibility, improved system scaling, lowerrisk and faster development and greater ease of adding partner agencies to regional systems.
7.3.5 Interoperability
Interoperability will be a key element in the regional integration of Decision Support Systems. A
key standard for both collecting data and sending control commands is the Transportation
Management Data Dictionary, Version 3.0. This standard will be implemented in the Dallas and
San Diego Integrated Corridor Management Systems, which are expected to be leaders in the
development of newer generation DSS systems.
7.3.6 Logistical Support
As with any other ITS technology, DSS implementation must be carried out using solid system
engineering principles with due attention paid to logistical support requirements. These include
proper documentation, operator selection and training, an Operations and Maintenance Plan and
ongoing configuration management, both at the system and operational levels.
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Summary of Impacts
May 17, 2011
Figure 7-1: Notional Data Architecture for a Regional Transportation Network
Real-Time Multimodal DSS Concept of Operations (Final Draft)
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Analysis of Proposed System
May 17, 2011
Analysis of Proposed System
8
Section 8 presents analysis of the benefits, limitations, advantages, disadvantages, alternatives
and trade-offs considered for the proposed family of systems.
8.1 Summary of Improvements
The Real-Time Multimodal Decision Support System provides the following operational
improvements to current practice:







Integration of multimodal real-time inputs
Creation or exploitation of existing archival data sources
The use of real-time and historical data to generate predictions for future network
operations
Real-time evaluation of pre-determined and ad hoc response strategies
Creation of multimodal and multi-jurisdictional response plans
Use of current center-to-field standards both for collecting data and sending response
commands to affected systems
A multi-jurisdictional common operating picture (COP)
8.2 Disadvantages and Limitations
RTMDSS deployments are major investments in time and cost. As such it is incumbent on the
stakeholders to understand and be prepared to mitigate the almost certain-to-occur disadvantages
and limitations in a sophisticated multimodal deployment.
8.2.1 Disadvantages
The disadvantages of RTMDSS incorporate institutional, operational and technical issues.
Institutional issues include the need for formal agreements to share information and to facilitate
shared governance. In the past, such agreements have proven more difficult to obtain than
generally anticipated during project initiation. Operational issues include the need to change
facility and asset management philosophies and to reach consensus on how operations on one
part of the network may impact other parts of the network and the need for optimizing overall
network operations rather than only one mode or facility of the network. Other operational issues
would include the need to broaden training and certification of RTMDSS “operators” to include
at least minimum practical knowledge of all modal operations represented in a regional network.
Technical issues include the need to provide additional communications capability and the ability
to “virtualize” operations if funding is not available for a dedicated RTMDSS facility.
8.2.2 Limitations
An RTMDSS would have to be implemented in stages as discussed in Section 7.1. Initial
operations may be marginal in demonstrating the cost/benefit advantage of RTMDSS. Without a
regional project champion, or champions, an RTMDSS system may have difficulty in sustaining
momentum in the early stages of deployment. Certain jurisdictions may have legal and
institutional impediments to shared device control, for example. Real-time modeling may not
accurately model the real world in time sufficient to affect the decision loop of a major incident.
Full automation of control algorithms and responses may not be feasible due to institutional
limitations. Prediction algorithms may not achieve the desired look-ahead interval. In general,
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Analysis of Proposed System
May 17, 2011
these are not uncommon issues in any ITS project, but in an RTMDSS deployment the stakes are
higher and the rewards are higher.
8.3 Alternatives and Trade-Offs Considered
A variety of configurations are feasible to implement an RTMDSS. For example, these would
include embedded rule-based expert systems, COTS expert systems, predictive tools using
algorithms, predictive tools using modeling, real-time modeling versus off-line modeling,
centralized control versus virtual control, and automated response plans versus semi-automated
response plans. As this Concept of Operations is transitioned from a non-region specific
document to a regional project ConOps, general architectural alternatives would be formulated
and evaluated against criteria established by that region. A more detailed architectural trade
study would be required during the High-Level Design phase of a specific regional RTMDSS
project.
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Appendices
9
May 17, 2011
Appendices
Appendix A: Current list of Stakeholders
Name
Khaled Abdelghany
Thomas Bauer
Organization
Southern Methodist University
Mygistics
E-mail Address
khaled@engr.smu.edu
tbauer@mygistics.com
John Benda
Former Illinois Tollway
jbenda@getipass.com
Jennifer Brown
FHWA - Arizona Division
jennifer.brown@dot.gov
Matt Burt
Battelle
burtm@battelle.org
Steve Callas
Trimet Portland
callasc@trimet.org
Nancy Chinlund
Caltrans Research and Innovation
nancy_chinlund@dot.ca.gov
Mark Demidovich
Georgia DOT
mark.demidovich@dot.state.ga.us
Hector Guerrero
LACMTA
guerreroh@metro.net
Barbara Hauser
Maricopa County
barbarahauser@mail.maricopa.gov
Bill Hiller
Trapeze
bill.hiller@trapezeits.com
Dave Huft
South Dakota DOT
dave.huft@state.sd.us
Samuel Johnson
SANDAG
sjo@sandag.org
Reza Karimvand
ADOT
rkarimvand@azdot.gov
Brian Kary
Minnesota DOT
brian.kary@dot.state.mn.us
David Kobayashi
Santa Clara County Transportation Authority
kobayashi_d@vta.org
Jingtao Ma
PTV/Mygistics
jma@mygistics.com
Roberto Macias
Texas Transportation Institute
r-macias@tamu.edu
Hani Mahmassani
Northwestern University
masmah@northwestern.edu
Pitu Mirchandani
Arizona State University
pitu@asu.edu
John O’Laughlin
Delcan
j.olaughlin@delcan.com
Koorosh Olyai
Dallas Area Rapid Transit
olyai@dart.org
Larry Orcutt
Caltrans Research & Innovation
larry.orcutt@dot.ca.gov
Zoubir Ouadah
City of Poway, CA
zouadah@poway.org
Tom Phillips
Virginia DOT
thomas.phillips@vdot.virginia.gov
Paul Porell
City of Scottsdale, AZ
pporell@scottsdaleaz.gov
Jay Primus
San Francisco MTA
jay.primus@sfmta.com
Frank Quon
Caltrans District 7
frank.quon@dot.ca.gov
Faisal Saleem
Maricopa County DOT
faisalsaleem@mail.maricopa.gov
Don Spencer
KC Scout/MoDOT - KDOT
donald.spencer@modot.mo.gov
Sonja Sun
Caltrans
z_sonja_sun@dot.ca.gov
Robert Tam
San Mateo County Transit
tamr@samtrans.com
Peter Thompson
SANDAG
pth@sandag.org
Alex Torday
TSS-Transportation Simulation Systems
torday@aimsun.com
Gerry Tumbali
Regional Transportation Authority, Chicago
tumbalig@rtachicago.org
Radiah Victor
MTC
rvictor@mtc.ca.gov
Jerry Wood
Consultant to Gateway Cities COG
jerry@jrwoodconsultant.com
Jim Wright
AASHTO
jwright@aashto.org
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Appendices
May 17, 2011
Name
Organization
E-mail Address
Albert Yee
Metropolitan Transportation Commission (MTC)
ayee@mtc.ca.gov
Dale Thompson
USDOT - FHWA
dale.thompson@dot.gov
Steve Mortensen
USDOT - FTA
steven.mortensen@dot.gov
Ben McKeever
USDOT - FHWA
ben.mckeever@dot.gov
Edward Fok
USDOT - FHWA/Resource Center
edward.fok@dot.gov
Robert Sheehan
USDOT - FHWA/Operations HQ
robert.sheehan@dot.gov
John Halkias
USDOT - FHWA
john.halkias@dot.gov
Brian Cronin
USDOT - ITS/JPO
brian.cronin@dot.gov
Yehuda Gross
USDOT - ITS/JPO
yehuda.gross@dot.gov
Richard Glassco
Noblis
rglassco@noblis.org
Karl Wunderlich
Noblis
kwunderl@noblis.org
Robert Haas
SAIC
robert.p.haas@saic.com
Mark Carter
SAIC
mark.r.carter@saic.com
Dan Lukasik
Delcan
d.lukasik@delcan.com
Bruce Churchill
Delcan
b.churchill@delcan.com
Elliot Hubbard
Delcan
e.hubbard@delcan.com
Rita Brohman
Delcan
r.brohman@delcan.com
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