Archive for September, 2011

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.