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Developing a Data Management Program for Next-Gen ITS: A Primer for Mobility Managers

Posted: April 30, 2013 in Data, Planning, Uncategorized
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There is no denying “big data” and its importance to next-gen ITS applications. The emergence of a vast,data omnipresent data cloud is enabling new knowledge and wisdom to be attained, as well as facilitate new operations models for the mobility manager.  Unfortunately, parochial data systems and data management strategies are quickly becoming obsolete with regards to managing this quickly evolving paradigm.  As a result, the need for operating institutions and mobility managers to understand “big data” and implement new, comprehensive and overarching data management strategies has never been greater.  Next-gen data and information systems will need to be autonomous, contextual, predictive and real-time.  The overall impact is cascading in that now a new strategy is not only desired, but will become an essential function, as the proliferation of meaningful data sources accelerates.  The time for agencies to plan, prepare, implement and transition is now.  The following aggregates a few thoughts into an introductory package for agencies to consider as they get started, in hopes of widening the road to success.

THE NEED

Although all of the values of new “big data” resources are not yet fully understood, the danger of getting bogged down in the data deluge is already being felt.  Before these new values can be leveraged, we must first review, research and retool, predicated on a sound understanding of existing conditions and extensive research and evaluation of likely future conditions and future capabilities. In addition, programmatic and industry changes such as MAP-21 and the Connected Vehicle are changing the operational fabric and are mandating new requirements for mobility managers, and thus, also need to be considered when developing a new data and information management strategy.

WHAT / HOW MODEL

So where to start? – The following insights are framed within the “What/How” solutions model, or “What do we want/need?”, and then,”How do we do it?”  As is the case with all sound planning efforts, an accurate understanding of existing conditions is an essential first-step prior to commencing with future planning efforts.

data2

What

Stakeholders and Champions – The first step is to identify all possible stakeholders (including champions and arbiters), both internal and external to an operating entity.  It’s key to remember that the data paradigm shift will cover all departments, agencies, programs and offices within a city and/or region, therefore coordination with an overarching perspective is essential for success.  Typically non-traditional stakeholders will now play important roles and become key teammates.  The identification of the initial list of stakeholders should include a first draft of a new steering committee or “Data Management Team” (DMT), which should encompass all pertinent agencies and institutions.

What do we have?

Following the formation of DMT, the team should begin to assess existing conditions.  Some key questions to get started include:

  • What are our existing data generators?
  • What systems are required to support these data generators?
  • How do we currently source, transmit and aggregate data from existing data sources?
  • What data and information-based goals and objectives are currently in place?
  • What are our existing processes for measuring and monitoring the path towards prescribed goals?
  • What values are we realizing/not realizing?
  • What standards and formats do we utilize?
  • What policies and regulations currently exist?
  • What quality control processes and procedures are in place?
  • What licensing, warranty and policy factors impact our data and information systems?

These questions will likely uncover significant new understanding as to how an agency currently handles data, and identify opportunities lost or new opportunities for functional improvements. The baseline assessment needs to include identification and mapping of existing supporting systems and infrastructure, including networking and software applications.  The exploration should also begin to drill down and refine existing information such as data attributes. A list of attributes might include:

Data

  • Source
  • Owner
  • Use rights
  • Format
  • Polling rates
  • Current uses (realized)
  • Potential uses (unrealized)
  • Quality
  • Cleansing/conditioning

Data Support Systems and Applications

  • Infrastructure requirements
  • Software dependencies
  • Other OSI reference model considerations

Policies, Guidelines and Contracts

  • Use policies
  • Cost per byte/poll
  • Licensing and Warranties
  • Existing vendor contracts, limitations
  • Storage and Retrieval
  • Performance metrics and monitoring
  • Existing staff requirements

Interim Review – Immediately following initial exploration of existing conditions, the Data Management Team should conduct an interim review of its findings. In addition, the DMT should review any and all existing goals and objectives related to data and information systems. What are we truly trying to accomplish and what are we achieving? What are we not achieving? What are the perceived initial gaps?  The initial review of existing conditions will likely trigger additional exploration needs with regards to existing data and information systems.  The interim review will also likely uncover additional stakeholders, both internal and external to the mobility management ecosystem.

Mapping – Map your findings.  As with all good wayfinding processes, a “you are here” marker is essential.  The goal is to map all exploration activities and contextualize the existing data and information system landscape.  In addition to narrative and graphical mapping, a spreadsheet or database is also helpful for tracking results such as data and information attributes.

Projections and Forecasts

data4The next step will be to begin exploration and research of existing trends and to conduct forecasting of future trends and forecasted conditions.  Predicting the future is always challenging at best.  However, with a sound, comprehensive strategy in place, an organization can best plan and implement strategies that prepare an agency for potential future conditions.  Trends analysis and future conditions forecasting will assist in establishing a pragmatic orientation for the foreseeable future.  These assessments should be conducted in parallel, yet separate paths from the existing conditions exploration and mapping tasks.  (The simultaneous work efforts will assist in finalizing the existing conditions survey task by uncovering additional gaps in the initial existing conditions survey and identify additional existing conditions research required).

Current Trends – Current trends such as cloud-computing, smartphones, mobile apps, private data sourcing, crowdsourcing, and integrated corridor management (ICM) need to be identified and included in new data management strategies. MAP-21 and other Federal requirements will mandate a new minimum acceptance level for the operating entities and also need to be immediately included in planning efforts.  It’s important to look past today’s sheen of certain applications and technologies to truly understand where industries and agencies are headed.

Future Trends – Connected Vehicle, including V2X, or V2I components will directly impact operating agencies and the way they do business in the coming years. Other likely future trends such as the autonomous vehicles, City as a Platform and integration of transportation networks will directly impact the data and information framework.  Additional trends such as system automation and data driven systems will amplify the need for pertinent real-time data.

Research

The “Future-Casting” task should also assign segments of industry to in-house champions (domain expertise), in order to monitor federal regulations, funding streams, the information technology and automobile sectors, university, state and federal research tracks, consumer technology markets, as well as tangential markets and adjacent internal agencies and divisions.

What do we want/need?

Immediately following the initial existing conditions survey and research and forecasting of future trends and conditions, the DMT should revisit original goals and objectives regarding data and information systems, and modify/append accordingly.  At this point, a traditional “User Needs and Preferences” assessment can be conducted, and should follow a traditional Systems Engineering framework. Some of the basic questions to address include:

  • Have we properly identified and defined all of our goals and objectives
  • How do you plan to leverage enriched data environments?
  • How will this foster enhanced wisdom and adaptive genius within our mobility ecosystem?
  • How will me monitor our progress towards achieving our goals and objectives (performance measures)
  • Have we instituted agency changes appropriate and sufficient to meet our goals and objectives?

To this point, you should have a pretty sound understanding of all of the existing data and information systems within the agency/region.    However, it may require additional iterations of the exploration, mapping and wants and needs assessments to truly understand where you are, and where you want to be (goals).

How

Once goals and objectives have been set, we can begin to assess “How” do we get there?  As with most planning efforts, an alternatives analysis and a Long Range Plan and Implementation Plan need to be developed.  A scale vs. value and ROI assessment is conducted at this point as well.  As is always the case with future-proofing, the key is not to plan to design for specific (undefined, and in some cases unknown) technologies, methodologies and strategies, but to identify and anticipate likely future conditions and implement a framework that is agile, flexible and capable of embracing future technologies, strategies and methodologies.

data3The next step is to establish a requirements-based blueprint and roadmap to transition from today to tomorrow. It’s also important to set measurable goals and identify necessary performance metrics in order to track progress towards goals and objectives, and to be able to conduct evaluatory assessments.  This step should also include a traditional gap analysis as well.  The Long Range Plan should also include a Concept of Operations.  This step will also begin to define “rewiring” necessary for executing the new data and information management program, which should also include business rules.  In addition, new data management schema needs to be integrated with the overall (typical) planning processes, including budgeting, long-range plans and regional plans.

Staffing resources and annual operations should also be assessed at this point.  Domain expertise, staffing and skills requirements will need to be addressed.  This should be included in the initial existing conditions exploration.  A new Data Manager position is likely the most appropriate first hire.  This individual may be an MPO, DOT or local agency staff person in charge of overseeing all harmonization of data and information systems across all platforms, jurisdictional and agency boundaries.  A Data Scientists/Analysts will also likely be required.

Additional Challenges and Potential Impediments to Consider

Initial Buy-in and Engagement – As with most new initiatives, getting up from the “comfy couch” can be the biggest challenge to implementing new or improved strategies.  Generating the initial inertia and momentum will require champions at the administrative, technical and arbiter levels, within all stakeholders, departments, agencies and regional staff (MPO).

Data use and retention policies – some data may be approved for certain uses, however, additional uses may raise privacy, licensing or ownership issues.  This challenge also gives rise to additional hurdles including operational governance and regulation of the new data and information system.  For example, can private data be sourced to operate public systems (signal systems, etc.) were safety is critical?

Integration and Standardization – what level of data and system integration is optimal, or will achieve the greatest Benefit/Cost ratio for an operating entity? What granularity and resolution (data density) is required for each component of the goals?  Automated monitoring and performance reporting will be a key to success with regards to overall integration and standardization.

Sustainability – A new funding stream (outgoing) is likely required.  However, the potential for additional revenue streams (incoming) is also likely.  Funding needs to be identified for the initial capital outlay, as well as annual operations and maintenance cost for the life-cycle of the system and subsystems.

Security – As the data reservoir expands, and the network to support and manage the data and information systems expands, so will the security concerns.  New policies and data management applications will be essential. Data storage, encryption, access rights, use rights as well as infrastructure and support applications should all be included in the initial security assessment and security planning efforts.

RESOURCES
Transportation Data and Information Systems – LinkedIn Working Group
http://www.linkedin.com/groups?gid=4929972&trk=myg_ugrp_ovr
USDOT Research Data Exchange
http://www.its.dot.gov/assetviewer/
Research, technology, and data drive America’s transportation system – USDOT Transportation Secretary
http://fastlane.dot.gov/2013/03/researchg.html
Real-Time Data Capture and Management
http://www.its.dot.gov/data_capture/data_capture.htm

Innovators and Visionaries

Posted: October 6, 2011 in Uncategorized
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As is the case with many of my industry peers, my personal draw to Intelligent Transportation Systems is complex in nature.  Yet a closer look reveals the primary sources for the attraction can be simply defined.  Innovation and technology.  Our industry is ever-changing, continuously fueled by the innovation continuum and guided by the emergence of new technologies.    At the forefront are the innovators and visionaries – a rare bread, yet critical to the advancement of our work.  Steve Jobs, although not directly related to our industry, will probably be revered as THE great innovator of our generation, and was a constant source of inspiration to a world of technology wonks, including myself.  Whether you’re an Apple fan-boy, or not, I think all would agree that the world of technology and innovation has lost a great visionary.  However I also believe Steve Jobs has inspired a new generation of visionaries.  Steve, thanks for the great technology that is firmly rooted in my day-to-day life, but more importantly, thanks for the inspiration. I’d like to conclude with one of my favorite quotes from Steve Jobs that he made during his famous Stanford Commencement speech.

“Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma — which is living with the results of other people’s thinking. Don’t let the noise of others’ opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition. They somehow already know what you truly want to become. Everything else is secondary.”

Steve Jobs
06/12/2005
http://news.stanford.edu/news/2005/june15/jobs-061505.html

State-of-the-art Traffic Signals, Circa 1937

Posted: October 1, 2011 in Uncategorized
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Although the primary focus of this blog is Next-Generation ITS, I came across this interesting educational film produced by Chevrolet in 1937, that shows the “state-of-the art” of that era.  Some of the traffic management applications are fascinating, and others just plain scary.

Traffic Signal System Operations via Smartphone Technologies

Posted: September 28, 2011 in Uncategorized
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Mobile technologies are supporting the development of new traffic management applications that are drastically changing traditional traffic management system architectures. What once seemed several years out is now showing early signs of first-generation deployments, with beta applications and system field trials already underway. 2011 has seen the emergence of several traffic management applications focused on the use of personal computing devices for sourcing traffic data and feeding new traffic management central software applications. In addition, new applications have also emerged this year that plan and manage traffic flows without directly interfacing with existing signal timing software.

An early entrant is being developed by researchers at Princeton and MIT, which bases signal operations and route planning on visual data collected and processed by vehicle-based smartphones. SignalGuru utilizes dash-mounted smartphones and video analytics to detect and process signal traffic signal indications via Green Light Optimal Speed Advisory (GLOSA) . The new application is considered “passive” in that it does not directly interface or manage signal timing and signal system operations.

Siemens has commenced with testing of a new smartphone-based pilot project installed for 400 signalized intersections in Harris County (Houston), Texas. The system utilizes Bluetooth readers and a new central application to measure traffic flow and traffic densities, then utilizes a central application to modify signal timing parameters. This form of traffic management via smartphone technologies is considered “active” as the application actually interfaces with, modifies and manages the central traffic management software.

References and Resources
Siemens (Active)
http://www.siemens.com/innovation/apps/pof_microsite/_pof-spring-2011/_html_en/traffic-systems.html
SignalGuru (Passive)
http://www.princeton.edu/~ekoukoum/SignalGuru.html

Update from the Data Liberation Front

Posted: September 25, 2011 in Uncategorized
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The Open Data movement has aggressively proliferated since its inception in 2009, spreading  world-wide in a relatively short period of time.  While the movement has gained tremendous momentum, a significant amount of work remains before the “open data” philosophy moves from the “initiatives” stage, to becoming a common component of an operating agencies DNA.

First generation “Open Data” initiatives have enabled transportation agencies to capitalize on the growing capacity and sophistication of public intelligence (crowdsourcing). By making datasets available to an unlimited pool of resources, small agency staffs can supplement their internal knowledge-base, and expand their potential for innovation of transportation solutions.  The following graphic illustrates the growth of “open data” initiatives (From Visual.ly)

“Open Data” Initiatives have also proven to provide an excellent vehicle for expanding public outreach and enhancing public engagement.  By providing an open-access platform, all citizens are capable of participating in day-to-day government activity.  Open Data also allows public agencies to receive valuable input on the condition of existing data sets as well as insight for potentially enhancing and improving existing datasets and data management protocols.   Open Data also allows institutional organizations to receive input with regards to new data needs within the transportation community.

Work to Be Done

As previously noted, we’ve come a long way in a relatively short period of time with regards to data liberation.  However, significant work remains.  Public agencies need to continue the democratization process, freeing up more and more data sets.  The continued proliferation of open data will also require the further advancement of policy and guidance for doing so.

Maintenance of products generated through crowdsourcing efforts will also require significant attention.  Early open data contests are showing some applications falling in disrepair due to lack of maintenance.  Public agencies will need to include plans for providing the ability to maintain applications and tools over the long-haul.

Public agencies will also need to continue to pivot towards disseminating real-time data streams via APIs.  Public transit agencies have provided significant leadership in this particular open data environment, with assistance from third party entities such as Google.  Long-term, however, work remains in order to facilitate a rich, robust real-time data environment.

The current data deluge will require a shift within public agencies with regards to existing data management strategies.  The aggregation of new data sources, such as smartphones, RFID and new wireless sensor technologies combined with emerging data-rich environments such as the connected vehicle will place a significant data management load on operating entities.  New data resources and corresponding “big data” curation and management needs will require public agencies to implement “data managers”, either in-house, or through supporting third party contracts, in order to establish proper data frameworks.

References and Resources
Data.Gov
OpenDataCommons.Org
OpenDataFoundation.Org
OpenDataKit.Org

“Feedback Loops” and Next-Gen Intelligent Transportation Systems

Posted: September 18, 2011 in Uncategorized
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The recent emergence of new mobile computing technologies has enabled ITS practitioners to develop a wide array of next-gen transportation solutions.  The new technologies have also instituted an increased focus on the use of traditional “Feedback Loops” as a mechanism for implementing behavior management and process improvement strategies.

Feedback loops (a subset of cybernetics) consist of four distinct stages: the evidence stage, relevance stage, consequence stage and action stage.  The first stage (evidence) measures, captures and stores behavior data.  The second stage (relevance) relays compelling, emotionally resonant information (not raw data) to the individual.  The third stage (consequence) presents one or more viable alternative paths ahead that confront the individual. Finally, the fourth stage (action) represents a concise moment where an individual can modify a behavior or make a choice regarding a potential path.  The resulting action is then measured (evidence) as the loop continuously repeats.

(Graphic from Wired)

Feedback loops have been utilized by ITS practitioners for a number of years, most notably for traveler information systems.  Feedback loops are implemented to provide travelers with information about their actions (travel), and then provide travelers with viable alternative actions, allowing the travelers to alter their actions.  An early example of the use of feedback loops in ITS can be easily illustrated in overarching operations of travel time information systems.  Traffic data is collected, travel times calculated and decision-quality, actionable travel time information is disseminated to travelers (typically through the use of Dynamic Message Signs) at locations that allow travelers to evaluate their existing travel conditions and modify their trip as required.

(Picture From FHWA/GDOT)

So how is new mobile technology changing the use of feedback loops within the ITS environment? New mobile technologies have instituted the ability to implement next-generation feedback loops that incorporate dynamic, real-time functional capabilities.  New capabilities focus on the use of hyper-local connectivity and unique user needs through dedicated interfaces with each traveler.   Mobile technologies such as smartphones and connected vehicles allow for the use of real-time, context aware data collection and information processing based on real-time location-based conditions.  These new capabilities facilitate highly-tailored feedback loops based on each unique traveler within a system.  New feedback loops can incorporate individual user needs based on unique characteristics such as weather, scheduled appointments, transit alternatives, route preferences, vehicle type, job location, recurring trips, etc.

References and Further reading:
Harnessing the Power of Feedback Loops
http://www.wired.com/magazine/2011/06/ff_feedbackloop/
Changing Behavior Through a Mobile-Enabled Feedback Loop
http://www.mobilebehavior.com/2010/03/29/sxswi-trend-1-changing-behavior-through-a-mobile-enabled-feedback-loop/

Web 3.0: The Semantic Web and Intelligent Transportation Systems

Posted: September 11, 2011 in Uncategorized
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I’ve never been a big believer in trying to shape eras of technology into categorical boxes and branding those technology groupings or time frames with catchy monikers.  The temptation to label any technological period is at best a difficult and risky proposition, which can be limiting in the true definition and potentially lead to significant inaccuracies.  However, when discussing the evolution of the internet, one can easily envision the industry-derived labels that define the major advancements in the maturation of the world wide web.

Web 1.0 generally instituted a one-way communications framework through the use of static web sites and web services, essentially providing a read-only interface between the web and the end-user.  Web 2.0 introduced collaborative, two-way communications technologies and a suite of innovative communications tools built on top of collaborative philosophies.  The next generation of the internet is a subject of great debate, yet many agree that we will see a transformation from a “web of pages” to a web of data, primarily through the use of “semantic web” technologies.

The semantic web, (or Web 3.0) will enable automated data and information exchange between machines (computers) and systems (software applications) through the use of use of ontologies, new data formatting and new meta data structures.  By applying “semantics”, or meaning to linked data sets with new descriptive meta data (or tagging), computers can begin to add meaning to data as it relates to real-world objects, subsequently automating human functions such as data search, data aggregation and data analytics, thus implementing automated two-way, peer-to-peer collaborative communications.

ITS USES

Web 3.0 technologies will enable ITS applications to automate data and information exchange and limit the need for human interaction.  Context-aware and location-based services data linked to individual travelers will automatically interface with regional operating systems and regional traveler information systems, thus eliminating the need for human support on both the operator and end-users part.  Real-time transportation data including vehicle, pedestrian and transit data will be automatically fused and processed with algorithms to establish new, next-gen regional operations platforms.  Semantic technologies will also provide an essential component for support of the connected vehicle platform.  Vehicle-to-vehicle (V2V) and Vehicle-to-Infrastructure (V2I) will utilize semantic technologies for automated, real-time data exchange.  Mobility is becoming more and more reliant on the ability to apply effective social engineering and behavior management strategies, rather than the application of traditional physical technologies.  Semantic technologies will provide a key tool for the integration of social (personal) data with centralized applications and overarching transportation systems.

Semantic web technologies are still very much in their infancies; however the transformation is well underway.  Early signs show that the semantic web will take time to implement, most likely in stages as the web transitions vast existing data sets to include semantic data formats.  The transportation industry, and more specifically the ITS industry is primed for becoming early adopters of web 3.0 technologies.  Research and demonstration projects are already underway within the transportation industry.  It’s just a question of when will the “tipping point” be realized.

Further Reading
Ontology of Transportation Networks
http://rewerse.net/deliverables/m18/a1-d4.pdf
An Ontological Infrastructure for Traveler Information Systems
http://robotica.uv.es/~cicyt/doc_publicos/0306_CON_ISBN.pdf

The Fragmentation of Computing Systems and the Potential Impact on Next-Gen ITS

Posted: September 5, 2011 in Uncategorized
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There is no question that the recent emergence and rapid penetration of mobile computing devices has facilitated evolutionary leaps in innovation, including the provision of next-gen ITS solutions.  However, a by-product of this rapid technology-shift has consequently included the emergence of multiple mobile operating systems (iOS, Android, Blackberry, Windows, etc), and multiple mobile hardware form factors (smartphones, tablets, personal navigation devices and cloud-based computers such as Google’s CR-48).   The rapid shift to mobile computing has also included the emergence of new supporting client-based software applications (apps) that operate on the aforementioned operating systems and hardware devices.  As a result, the quick emergence of the mobile computing platform has fragmented computing and the services reliant on today’s computing platforms.  The following chart partially illustrates the issue.

As previously noted, mobile computing hardware has also splintered over the past few years.  New hardware platforms such as smartphones and tablets, in addition to traditional (yet diminishing) computing platforms such as the desktop and laptop have implemented multiple computing ecosystems.  In addition, the OS that operates these multiple platforms is also showing signs of additional fragmentation, as illustrated in the following chart.

Early Affects

The splintering of computing systems has caused a significant cost and complexity problem for solutions developers, as well as confusion on the end-users part, thus greatly hindering solutions-providers  the ability to implement rapid deployment of transportation solutions that cover a majority of the consumer (traveler) and operator market.  As is the case with most industries, fragmentation runs the risk of re-instituting significant barriers and system silos for transportation solutions.  In addition, fragmentation is greatly enhancing the potential for security vulnerabilities.

We’ve Been Down This Road

The early days of computing was also fragmented, built from a number of operating systems and computing hardware platforms.  Unix, DOS, Windows and Mac all provided OS to the infant computing industry.  However, over time the industry consolidated most of the primary components, including operating systems and hardware, implementing a period of stability (calm) from around 1995 to 2005.  During this period, Windows OS and PC-based hardware provided for most of all mainstream computing.

The question remains, will the re-fragmentation of computing have a long-term limiting affect on ITS?  Natural attrition and business competition will provide some degree of defragmentation over the coming years, but to what degree remains to be seen.  For example, should the Android OS continue to outpace iOS in growth rate, we could potentially see a quicker, more cohesive return to a defragmented computing model.  In addition, the industry is beginning to show signs of coming together to develop some form of open standards that will aid in the unification of platforms.

Next-Gen Data and Information Frameworks for ITS

Posted: August 27, 2011 in Uncategorized
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The recent rapid proliferation of transportation data resources has implemented a rich array of potential data resources for transportation agencies.  The new data resources coupled with pending requirements associated with Section 1201 of SAFETEA-LU (23 CFR 940) are requiring transportation agencies nationwide to revisit their overall traffic and transportation data and information goals and requirements.  The recent paradigm shift is necessitating the need for transportation agencies to conduct evaluations of existing data and information architectures, and determine future plans for data acquisition, data processing and information dissemination.  New data governance disciplines will enable transportation agencies to prepare for the changing face of transportation data management, optimize the potential value of their data and information and avoid a patchwork of stove-piped data and information subsystems.

Establishing a Data and Information Framework
A successful transportation data and information framework provides for the detection, collection, aggregation and delivery of accurate, pertinent data to head-end processing and dissemination infrastructure. In order to optimize efficiencies and accuracies within the data and information network, the end user/operator should first assess the existing data and information management environment. The existing conditions survey will set a baseline for the development of the next-gen data and information framework, by establishing requirements associated with legacy infrastructure and existing user needs. Once the existing data and information survey is complete, an “existing framework” model can be generated and utilized as the foundation for the next generation framework.

A “data and information framework” identifies all functional limits, including network and system boundaries of all existing data and information management systems that support and interface with the defined comprehensive framework (and the technologies and applications that operate the framework).  A detailed framework also accurately maps all data and information flows within and between transportation systems and devices, defines functional requirements associated with the framework as well as specifies technical requirements associated with the data and information environment, such as formats, protocols, polling rates, processing and storage. The framework should also identify future user needs and future system needs associated with the information framework, thus establishing a comprehensive mapping of all existing and future data and information requirements.

Considering Potential Data Resources

A formal data and information framework enables the end-user/operator to better understand existing system elements and future data needs in their entirety, and ultimately assess and understand functional and technical requirements required for their data and information services. This level of understanding is essential to the end user considering system improvements or more succinctly, looking to implement new data strategies.  One of the first decisions to consider is whether or not an operator needs to buy, build and manage the data and information network in its entirety, procure private data resources to supplement all or a portion of the data needs, or develop a new framework that will satisfy the use of multiple data resources.

Traditional Means

Until recently, public agencies have traditionally designed, built and operated all data and information system components (hardware and software) required to feed their traffic management and transportation operations needs.  Public data networks can be developed with hyper local understanding of benefits and constraints of specific detection and data collection strategies.  Public data collection systems place complete control of data collection and data management in the public agency domain.  Sharing public agency data with other internal and external agencies has proven to be easily implemented as well.  Also, it is common for detection vendors to develop head-end software and system management software for the operations of detection devices, therefore alleviating the need for the public entity to develop any additional operations and management software to support a data and information network.  However, public data systems require significant upfront capital investment and continuing funds for the operations and maintenance of the infrastructure, including detection devices, communications infrastructure and head-end hardware.

Private Sector Data

The last decade has seen the emergence of “Data as a Service” (DAAS) models where traffic and transportation data is provided by the private sector.  The rapid proliferation of GPS-based probe data, Bluetooth detection data and peer-to-peer crowd sourced data resources are rapidly changing the transportation data landscape through the implementation of a rich network of real-time data sources.  DAAS primarily originated with GPS data cultivated from fleet management applications, however, a recent paradigm shift has seen private data providers shifting to a hybrid solution of fleet-based data and crowd sourced data generated from consumer-based GPS and Bluetooth-enabled mobile devices such as smartphones and personal navigation devices (PNDs).  The emergence of the concept of “probe-based people” has led the way for the explosion in data generation.

Private data sources are ideally suited for consumer-grade traffic and traveler information systems.

Private data sources are most attractive when compared to public data sources when considering the rate of deployment and rate of coverage.  Private data networks are rapidly expanding coverage and data-density of the coverage, outpacing deployment capabilities of traditional public point detection data resources. Private data services also minimize operations and maintenance (O&M) costs typically tied to traditional public agency data collection systems.  Also, because the data is generally delivered via the internet, public agencies do not need to operate and maintain the communications and networking infrastructure generally required to operate public data collection systems.  In addition, private sector data alleviates legal issues associated with privacy, data collection and open record laws.

Although private data services drastically minimize traditional O&M costs associated with public data collection and delivery systems, new head-end hardware and new or modified software will be required.  Unique, dedicated applications and middle-ware are required to ingest process and disseminate data and information culled from private data services, with the degree heavily dependent on the type of use and application of the data.  Although private data has many of the same characteristics and reliance’s as that of other proprietary systems, private data more resembles.  The “black box” nature of private data can be limiting in the end users understanding of the delivered data.  Another present short-coming of GPS-based traffic data is its lack of precision when compared to traditional point-detection systems inability to differentiate between HOT/HOV lanes and normal travel lanes.  Also, GPS-based data is currently unable to provide accurate volume data.  The ability to share, manipulate or enhance private data is highly dependent on contractual terms and vendor specific criteria.

To date, private sector data has been primarily used to provide traveler information to travelers and to provide a resource for performance metrics and evaluation. In order for agencies to realize full cost benefits associated with the use of private data, private data will need to be able to fully replace data will be required to fully operate day to day traffic operations. The next big leap will require private data to fully replace existing data systems required to operate transportation systems. For example, we will need to see private data provide operational data to feed operational algorithms for systems such as ramp metering, traffic signal and incident detection systems. This represents a conceptual shift and approach to the utilization of public data sources. One early move in this direction has seen private data procured to provide the needed data to run automated travel time systems which utilize DMS for travel time dissemination. Legal hurdles may need to be addressed and policy directives may be required to fully integrate private data with traffic management systems and ultimately relieving the need for public agencies to deploy, operate and maintain their own data systems in order to obtain all necessary data. This may also require reformatting of private data streams, middle-ware or modification of existing software in order for existing traffic management applications to correctly ingest private data. IntelliDrive will generate a huge amount of data. Open data represents a transitional state of data management from the once privately held yet public data to truly “open” public data, or “Data as a utility”.

It is likely that no single type of transportation data, public or private will be able to address all transportation data needs. Public data sources are ideal for feeding and operating public transportation and traffic management systems, although there are early signs private data is slowly entering these areas and supporting these systems. How will existing public data systems and private data systems integrate with the impending data-tsunami associated with the rapid adoption of the personal mobile computing platform and new robust data-generating engines such as the data generated by the systems associated with the Connected Vehicle initiative.

Further information:
Information Management Strategic Framework
http://www.ato.gov.au/content/downloads/cor48331nat11852.pdf
Data, Information And Knowledge Management Framework
http://www.slideshare.net/alanmcsweeney/data-information-and-knowledge-management-framework-and-the-data-management-book-of-knowledge-dmbok-3366885
Data Governance Institute
http://www.datagovernance.com/
Data Governance Blog
http://datagovernanceblog.com/

Operations without Borders?

Posted: August 22, 2011 in Uncategorized
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It’s no secret that the efficiencies of transportation operations are often hindered by the jurisdictional boundaries applied to them. Most of these operational boundaries were delineated from a non-transportation perspective, many going back a hundred years or more. The logic of the original assignment of transportation boundaries is understood, as they easily aligned with legal and geographic boundaries, yet the lack of long-term, transportation-centric perspective in the designation of these boundaries has in many cases significantly limited the overall operational efficiencies of a region. As result, many regions in the United States have recognized the aforementioned limitations, and commenced with the collaboration and integration of operational entities, whether through the implementation of formal (legal) regional operating entities, or through the development of formal agreements between participating agencies. However, the fusion of system operations and development of operational agreements can be time-consuming, costly, and often hindered by technological barriers.

New technology advancements including GPS-enabled devices, mobile, always-connected computing, ubiquitous broadband wireless communications and cloud-based computing platforms are supporting the removal of many of the motes and silos typically associated with jurisdiction-based transportation operations. These new technologies are facilitating the ability to implement agency-agnostic, regional transportation functions including multimodal, interoperable cross-platform management of travelers from point of origin to point of destination. Payment systems, traveler information, traffic management, incident management, transit and parking management systems have been significantly liberated from most of technical barriers that once constrained their operational limits. These technological and strategic capabilities could lead one to envision the incremental migration towards a future borderless operational environment for larger metropolitan areas, one that is not limited by non-related legal boundaries.

Federal programs such as the Integrated Corridor Management (ICM) initiative are beginning to examine and implement an incremental framework for boundary-independent system operations along key multimodal, multijurisdictional corridors. In late 2008, the USDOT selected Dallas, TX; Minneapolis, MN; and San Diego, CA as test beds for the analysis, modeling and simulation (AMS) of ICM strategies. The ICM AMS Pioneer Sites will develop tools and technical guidance for further integration of transportation systems throughout the United States, removing existing stove-piped systems, thus enabling less constrained, fused operational systems.

From RITA ITSJPO

Considering the bigger operational picture, one can envision a more expansive deployment of integration strategies that amplifies the removal of additional operational silos. For example, GPS-enabled devices provide the technical capability to implement comprehensive, continuous multimodal usage-based data and information across all modes of transportation, from point of origin to point of destination. GPS-enabled devices are also enabling usage-based fees, high-resolution traveler information, including multimodal travel times, across all modes of transportation, from point of origin to point of destination. Theoretically a traveler could navigate multiple jurisdictions and modes, utilizing personal vehicles, buses, light rail, managed lanes and parking facilities, all under a single operational umbrella (system).

One can also envision future traffic management system operations centrally operated through a cloud-based platform and central operations facility. Regional Advanced Traffic Management Systems (ATMS), although complex to initiate and implement, have proven beneficial in a number of jurisdictions. The traffic management community has seen gradual migration from individual, stove-piped jurisdictionally constrained traffic management systems to unified regional operations at numerous locations throughout the Country. However, obviously significant challenges remain. Even though barriers associated with the technical layer are diminishing, and ultimately achievable, the institutional layer will always represent the major challenges to the migration to regional operations. Obviously operations oversight will always be necessary, but to what degree?

From USDOT/FHWA

The application newer, state-the-art technologies provide yet another tool for supporting the emergence of these regional operating entities. As regions continue to coordinate and expand the operational layers, overall efficiencies and economies of scale can be realized.

Further Reading:
Integrated Corridor Management Knowledgebase
http://www.its.dot.gov/icms/knowledgebase.htm
Regional Transportation Operations Collaboration and Coordination
http://ntl.bts.gov/lib/jpodocs/repts_te/13686/13686.pdf
Advancing Regional Transportation Operations: A National Workshop
http://onlinepubs.trb.org/onlinepubs/circulars/ec150.pdf
Regional Concept for Transportation Operations
http://ops.fhwa.dot.gov/publications/rctoprimer/rcto_primer.pdf

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