Page 1

    Information Science in Transportation   


By: David Durman  University of Kentucky  ICT‐311‐201  12/7/2018                   

Information Science in Transportation    Information, Data, and Knowledge    Before we can begin to examine what Information Science is, we must first understand the underlying terminology. Specifically, we need to understand the fundamental particles of Information Science, that being information, data, and knowledge. At first blush, these seem like synonyms with little distinction, but we will explore the variant meanings that we will be employing. To start off this explanation, we should first look to how the Impact of Information on Society (Lester & Koehler, 2004) tends to define information; while it isn't explicit in a definition, the best approximation would to be, 'meaningful content concerning the world.' The book also notes that information can take many modes, such as visual, aural, tactile, olfactory, or gustatory forms in nature, sticking to a simple model of the traditional, though inaccurate, model of 5 senses. Additionally, it points out that information can exist within our minds in the form of stored memory. Keeping this approximate definition in mind the book makes the case that information generation and consumption has been increasing escalating with the advent of the Digital Age; although it is somewhat dated in its technological references, it notes the rapid increase in the adoption of smartphones, computer use, and other technological marvels. It does temper this, however, by noting that information production and consumption has always been a factor of human existence, noting older technologies such as clocks, radio, the telephone, television, and even the informationcommunicating practices of ancient cultures, such as the Ta Moko tattooing of the Maori that communicates the story of that person's life. Still, information has exploded with the advent of the digital computer and the Internet; currently 2.5 quintillion bytes of data are generated every day (Marr, 2018) and globally we are slated to have generated a total of 163 zettabytes of information by 2025 (Cave, 2017), the vast majority of which has been generated exclusively in the Digital Age. For reference, the amount of digital data in the world first exceeded a single zettabyte, or 1021 bytes, in 2012 (Wikipedia contributors, 2018). Primarily for individuals, information is exploited to make decisions by resolving uncertainty (see image below.) In other words, the confidence of a person in making a decision is directly related to the amount of information concerning the decision that they have access to so that they can account for all of the variables in the decision. The process involved with this begins with a person recognizing an uncertainty in a decision, being aware that it could be resolved with information, determination of the type of information that would be needed, accessing the information that is needed, and finally possessing the necessary skills to locate and understand the information. In other

words from an engineering standpoint, information gathering is the process of eliminating entropy from the decision making process. From a societal level, information is utilized by that culture as a whole and by its constitute parts. For example, Lester & Koehler referencing McHale, they make the point that information is a necessary prerequisite for any economic activity at all; while it goes with oil as an example, we will go with a simpler and cruder resource to demonstrate how far back in time this fact reaches, namely steel. Iron ore, from which the iron is extracted to make the alloy of steel, is essentially a useless red rock unless a person possesses the necessary knowledge to smelt it, alloy it with carbon, and to work steel, all a very complicated and specific process. In the distant past this information would be transmitted primarily in an oral manner through apprenticeship or similar; now one could teach themselves all of these skills without ever interacting with another person through the use of the Internet. Regardless of the activity, information is a necessary precursor to any economic action, which could be argued to be one of the core functions of a society. Beyond economics, subsections of society utilize information to control and manipulate society as previously mentioned. For example, control of information in the political sphere has always been a key tool in governance. In modern times, however, the rapidity and thoroughness of information dissemination is creating new challenges for politicians, necessitating quick response to developing situations to which the machinery of government has been slow to adapt. Additionally, information has been the currency of entertainment since the dawn of civilization; while modern examples such as video games or movies are obviously information, so in the content of a play or a musical performance. We must also contend with the idea that the quantification of information into discrete values is leading us as a species to a complicated philosophical problem that we are wrestling with, namely that of ownership as it relates to the nature of information transference. Patents on intellectual property are a very new concept in the scope of history; the growth of the Internet is built on the back of commoditizing information. Given this examination of Lester & Koehler’s work, we have a wealth of concepts for defining Information, and from there we can build contextual definitions of Data and Knowledge. Data is the existential facts about the world that we are capable of perceiving either directly or indirectly, such as the wavelength of a photon or the average density of Jupiter. Information, building off of this definition of data and well as the bulk of our discussion in this section, would be the manner in which we collect and distribute data about the world. Finally, knowledge would be the possession of the data in our minds, or the manner in which we exploit disparate aspects of individual datum in order to create new data about the world, such as our inference of the existence of the Higgs boson. Knowledge could also include an application of wisdom to information to produce knowledge, such as a shared understanding.

What is Science? As a civil engineering professional, this may seem to be a nonsensical question, but it is important to discuss science as a process at this point in order to understand Information Science. For common ground, science is defined as, “systematic knowledge of the physical or material world gained through observation and experimentation” (Science, 2018). This is certainly not a controversial definition of science, and moreover it is one that is familiar in this field. The key takeaway from this definition, for our purposes, is both the ‘systemic knowledge’ and ‘observation and experimentation’ aspects; in other words, science is a process that is engaged in to produce the best current model of the way in which the world operates.

What is Information Science? Information Science, in turn with our understanding of science, is the study of information as it exists both discretely and esoterically; in other words it is the study of both information itself in its generation, utilization, and destruction, as well as the study of information itself. This is a difficult concept overall; below is an infographic to help contextualize this concept visually.

From this, we can see the interdisciplinary nature of Information Science. It has significant roots in hard sciences in regards to the documentation aspect of it, such as with Library Science, Computer Science, etc. It also contains components of the philosophical disciplines, primarily dealing with the concept of how information develops from data to knowledge, and possibly wisdom, as well as what these discreetly mean.

Situating Information Science in Transportation Civil Engineering This may seem as though it has gone far afield from our purpose, so now we will examine how Information Science relates to Civil Engineering in the Transportation sector. In this regard, there are two primarily distinct manners in which it relates, divided into the past and the future. Transportation primarily dealt with Information Science in the past in regards to the hard science aspects relating to documentation, specifically Library & Computer Science regarding the storage and transmission of information. Tremendous amounts of information documentation is needed in order to construct and operate a transportation system; plans, standard drawings, contracts, reference manuals, etc. are a few of the information tools that are applied on a daily basis in transportation. In the future the documentation aspects of Transportation will certainly remand, although they will grow in such an exponential manner as to almost be a different beast altogether. Not only will all of the needs of the past still exist, but with the advent of Connected and Autonomous Vehicles, the need to store and transmit additional data, both internally and to the public, will become staggering. To put some perspective to it, it is estimated that a single automated vehicle will generate more than 300 terabytes of data per year (Dmitriev, 2017). Multiplying that by the 268.8 million vehicles on the road in the US (Number‌, 2017) and we easily arrive at a truly staggering amount of data. Certainly, not all of this data will need extensive storage, but the coordination and management of such a vast network, as well as the distribution of information to a huge number of stakeholders, is a massive undertaking. Not only will we need to expand our IT solutions tremendously, but we will need to assess and redesign both our understanding of information and the manner in which we employ it; this is where Information Science will become a key interdisciplinary aspect of Transportation in the near future.

Information Science and Humans Information Behavior Firstly, we need to understand how humans interact with information and information science in general. By understanding how we interact with it, we will be able in turn to examine its general behavior while remaining cognizant of our inherent human biases and perceptions. People tend to think of themselves as supremely perceptive of their environment and that they are a very objective observer in that environment, but that is pretty distant

from the truth (Koch, 2010). At the most basic level we can experience sensory illusions that confuse or mislead us, but it goes well beyond. People are subject to a wide assortment of cognitive dissonances and biases that distort or mislead our ability to consider information objectively (Bohren, 2018). The only measure we can take against these issues is to maintain a skeptical mindset, examining both the manner in which we think as well as what we think. So far, this has been a little far afield and outside of the general context of Transportation; in order to better ground this concept, we will examine an information system that we are all familiar with and utilize on a daily basis. This is meant to be an example of the way to approach our information systems with a critical mindset, becoming active information users rather than passive ones. The information system we will be focusing on is ProjectWise, a program distributed by Bentley Systems. The basic function of this software is to distribute access to files for a business that need to maintain a record of change on them. Structurally, it is set up similar to Windows Explorer with a nested tree file structure; however, in order to access a file you must 'check it out' of the system, meaning that no one else can access the file until you return it to the system. This allows the system to maintain a record of changes and who made the changes within the metadata, which can be reviewed in the primary program. This sort of system is useful when any engineering design is taking place, such as with road plans, so that potentially critical changes can be track. Additionally, this is an important feature for official documentation, which is both required to be tracked by law and is necessitated for formal approvals. The information behavior that the users are engaging in is necessarily both active and directed (Davis & Shaw, 2013), meaning that accessing it requires active pursuit of the information as both the files, and the metadata concerning their history, as they cannot be chance encountered without intentionally accessing the program. It is also directed in that the information seeker would need to also specifically intend to expose themselves to the information, i.e. have a concept of its form and structure, prior to acquiring it, especially the metadata aspects that the user would necessarily have to be aware of prior to access. All of this complexity in record-keeping is to avoid long-standing problems within engineering and road design. Specifically, most project developments are multi-year affairs with numerous points of input across a wide range of people that are subject to change with regularity as new information is discovered or standards are updated. Obviously, this leads to a ripe opportunity for the introduction of errors, necessitating the need to cut down on controllable errors such as the Post Hoc fallacy that this tread represents. As Stockwell points out in referencing Carl Sagan's list of logical fallacies in A Demon-Haunted World: The astronomer Carl Sagan...developed what to my mind is one of the most useful lists one can use to separate truth from fiction... It can be of great help to anyone in recognizing some of the most common traps in our own and others' logic. (Stockwell, 2000, p.154-155)

In summation, this system is good at alleviating the errors that it is aimed at, but it does suffer from drawbacks of its own. It does require that all users be trained in its use, a perennial issue, in order to implement its controlling function over human information seeking behavior, but it does succeed at the task. This assessment is meant to demonstrate how we should start thinking about our information systems and, moreover, objectively assessing the benefits and problems in order to both develop improvements and to best implement a potentially-flawed tool. Thinking about information behavior, we could look to improve the issues with ProjectWise by understanding the root causes behind the problem with user training. In particular, the training on the use of ProjectWise is passive and informal, which from our understanding of the manner in which people interact with information systems, is a poor training method with inconsistent results. Examination of how people best learn to utilize a distributed information system like ProjectWise will show us how to improve it.

Information Literacy Before you are able to engage in this sort of assessment, you will need to understand and develop your information literacy. Information literacy is the ability of a person to assess the quality and content of the information that they consume, account for their own credulity, and apply critical thinking skills to that information (Durman, Bertram, Davidson, and Moore, 2018). It may seem obvious why information literacy is of importance, but it must be emphasized due to the era of the ‘Fake News’ phenomenon that we live in; in many ways, information has become weaponized to control the actions of others, through misinformation or misdirection (Allenby, 2017). As the attempts to manipulate the information we consume have become active to such a broad and complex degree, so too must our processing of that information become active and mature. The field of transportation is rapidly evolving with the development of new technologies, posed to change more in the next 20 years than it has within the past 100. The source of this change is the advent of connected and automated vehicle (CAV) technology, a nascent set of innovations that will have the same impact on the world as the invention of the automobile itself. These technologies are not, however, independent and standalone; indeed they are only possible now through the coming of the information age; because of this, information literacy is a necessity not only for being able to process and understand the technologies themselves, but it is doubly important for building a concrete understanding of the emerging technology itself and the direction in which it is developing, a challenging endeavor in what is essentially the cutting edge of transportation. As Transportation professionals, as well as organization, the education of the public regarding this technology is a staggering proposition. Not only is a wide-scale implementation of an educational program a monstrous task in and of itself, but the technology carries with it a great deal of nuance and variety, further exacerbating the issue. Should we attempt to rely on the market to educate the public? This seems an

unlikely outcome, so action from the public sector is needed. Are their policies we will have to establish, such as an identification system, in order to even facilitate educating the public? The information challenges inherent in this task are monumental and work needs to begin on them in earnest.

Information Science and Organizations Information Management Having taken a look at how individual people interact with information science, we will now turn to how organizations interact with it. This is important as no one person builds or maintains a transportation system; not only do we need to understand how we individually interact with information, but it is also important to know the methodologies and tools used by the organizations, which will in turn make us better at understanding information within the organization and how to communicate it. One of the first things we will need to understand in regards to organizations and their relationship with information, is how they manage it. Be it organizations in the public or private sector, primarily they are going to divide information up into its media components typically; an online business will probably have a very nuanced and categorized management system for all of its online resources, and probably little else; conversely, the National Archive utilizes all forms of media, subdividing its organization into specialties that deal with discrete management systems appropriate for each category.

Information Retrieval A management system would be useless, however, if the people within the organization are unable to retrieve and utilize the information being managed. To contextualize this, we will turn back to ProjectWise, applying information retrieval theory to the operation of the software. ProjectWise would seem to best fit within the context of the theory of Web information behaviors (Detlor, 2009). This follows from the fact that it is an information retrieval system meant to operate in a professional network to execute work-related problem situations by organizing and protecting necessary documentation. The theory hybridizes two models, specifically Choo’s general model of information use and Taylor’s value-added approach, in order to lay out the cyclical function of the information environment. Firstly, the user will need to identify the problem situation, say the need for environmental documentation on a project, as well as the dimensions of the problem, which can include issues such as scope, time frame, and other aspects. In our example, let’s consider the dimensions to primarily be concerned

with the environmental documentation having proper approval authority and being completed with the time frame to let the project out to bidding. Having our problem and its dimensions identified, the user then turns to the information seeking phase; in this phase, the user would navigate through the Windowslike file tree on ProjectWise to the location of the project that they are working on. From there they would examine any existing environmental documents and, if none are present, begin the process of developing the document and planning a timeline for completion within the targeted time frame. In either situation the user would then engage in the next step, information use. Checking out the document, or more specifically the placeholder for the document within ProjectWise, the user would gain sole authorship control of the document, being able to modify it to fit the constraints the particular project and send it out for review by proper authorities and signatures. In this manner, the user will probably go through the information seeking/information use cycle several times over the proceeding months as incremental modifications to the document are achieved, not to mention review for the encroachment of any errors.

Information Processing and Visualization Simply being able to retrieve information, however, is not the end of the story, especially as we move forward into the era of Big Data. Any engineer could pull the raw traffic data collected by modern traffic systems and not only would it be incomprehensible nonsense to that person, for the most part, but it would also probably be of such a volume that they would be unable to process in their lifetime even if it was readable to them. The first thing to understand in regards to this is that humans are very visually oriented creatures; information that would take a long time to convey to someone could be conveyed in a few seconds by illustrating that information; an individual could pour over traffic volume information for a road for days with no real clear idea of the trends in their mind, but could instantly see the trends when this information is converted to a graphical format. This is explained further in an excellent manner by David McCandless in his Ted Talk entitled The beauty of data visualization (McCandless, 2010). To better illustrate this idea, here is a visual representation of the sensor fusion data from an automated vehicle; this information would largely be useless to a human being if it was presented in its raw, multi-sourced format.

Information and Society Information does not exist in a vacuum; we have learn about how information works on an individual and organizational level, but it is important to understand how it interacts with society at large so that these tools can be leveraged appropriately. This is especially true in Transportation: a huge section of the industry, both on the public and private sides, deal with and interact with policy concerning information on a daily basis, which is in turn a byproduct of the relationship between information and society. Let us first explore that relationship so that we can pinpoint how it influences information policy.

What is the role of Information Science in Society? Information has always been the lifeblood of civilization. Stretching back to the days of the Sumerian Empire in 4,000 BCE with their meticulous record keeping of grain, poetry, and music, to the modern world where data is the fuel that drives the world-spanning engines of the economy. Human civilization has never be without a need to create, exchange, and consume information, be it the planting methods for prehistorical agrarian cultures or the massive data stores needed to develop and train machine learning; culture is nothing if it is not the medium that exists for the purpose of manipulating data and in turn a society is the metaphorical hardware on which that medium operates. We will explore the relationship between information and society, both in a generalized manner and in the modern sense with the rise of big data. A great deal of ink has been spilt over the relationship between power as an abstract concept and its relation to information; as pointed out in Fundamentals of Information Studies, “If money is the currency of economics, power is the currency of politics� (Lester & Koehler, Ch. 10, 2007) which begs the question, how is power generated. It seems easy to postulate that power comes from information in various forms; while you can construe a system where that is not directly the case, such as with a feudal kingdom, information is still intrinsic to the exercise of power. In our kingdom example, the exercise of power by a monarch relies on the base level of information that that person is the monarch; if that information isn’t disseminated effectively, that person will find it difficult to wield power. Furthermore, that monarch would need to have information of varying sorts to be able to exercise power in a meaningful way. This relationship between information, power, and societal function continues on into our modern society, albeit magnified a thousand times over. At the simplest level, our assorted governments are elaborated organisms that feed on and create vast amounts of data; even something as simple as how the government pays for itself through taxation is an elaborate environment of information generation, exchange, and

consumption. Many layers exist above that, such as politicians in the current environment of ‘Fake News’ at least, in part, rely on their ability to manipulate and control the flow of information, as well as which information is considered true or false independent of the fact. This outline barely scratches the surface and this is something as simple as merely our form of governance. Society itself as a whole is vastly more complex, the generation and distribution of social and financial information dwarfing it. This has been further complicated by the rise of Big Data and machine learning. There has always been the tendency to view information, especially information that has been record in some manner, as objective and that tendency has moved on to our relationship with Big Data. Big Data, or the ability to mine huge quantities of data in order to generate new and significant data, benefits in this regard as humans set up the process but cannot generally deconstruct the product and examine its components. This in turn leads us to further believe in the objective nature, but this is fallacious. Skepticism is key to the our new data environment in that we need to be able to critically think about data, both in its generation and application, and employ critical thinking skills to analyze it (O’Neil, 2013). The application of critical thinking to information both individually and collectively as a society is how we must deal with the flood we find ourselves in.

What is Information Policy? Working at the Transportation sector, much of what would be considered information policy has to do with practices amongst the employees and with the public. This is accomplished through widely assorted requirements, such as complex passwords that are changed frequently, badge ID for access, and directives on handling cybersecurity issues such as phishing, to name a few. Additionally, it would cover what information, typically computer files, can or cannot be shared with the public, as well as covering the storage and distribution of that information to proper recipients. In this way, information policy is primarily concerned with controlling the distribution of information, more specifically with protecting information from unauthorized access. Other aspects of information occur as well, such as the generation of information in the form of plans and proposals for construction projects; the stakeholders in this information are primarily the public at large, as well as the government itself. It would be surprising for anyone to think of information policy in any other context within the organization. This describes our experiential, day-to-day relationship with Information Policy, but it also exists as a reality on the institutional and political level. Every aspect of the transportation system is meticulously regulated, both at the federal and state levels, typically as a result of the enactment of law. Interpreting these statutory version of Information Policy is critical to performance and operations. It is crucial that transportation professionals understand the both contexts of Information Policy in order to operate effectively in this sector.

What are some of the most critical policy issues for Transportation? Currently, the most critical Information Policies for Transportation are more related to operational issues than regulatory ones, although this is likely to reverse in the near future. Operational policy deals with the rules surrounding the utilization of information technologies and their accessibility. On the public sector side, the vetting process for new information technologies is lengthy and typically must rely on nonproprietary solutions to promote competition amongst vendors. This means that information technology solutions in the public sector tend to be both outdated and slow to adapt; many organizations in Transportation are extensively taking advantage of machine learning technology, but this has primarily taken place in an ad hoc, disorganized manner due to the mechanisms of information policy within the Transportation sector not being adapted to rapid adoption. This is further complicated by Information Policy decisions typically being made outside of the organization itself, be it through other agency or through the legislature. This adds a very difficult stumbling block in tailoring the Information Policy needs of Transportation to actual policy implementation. Specific policy needs relating to connected and automated vehicles, as we have discussed, is where the difficulties with regulatory Information Policy will appear in the near future. Both new policy regarding best practices and operations will need to be developed, as well as policy regulating the use and distribution of information. Tremendous economic issues surround this area, not to mention issues of privacy and mobility.

Conclusions New positions, such as a Chief Knowledge Officer (CKO), will become a necessity as the information aspect of Transportation expands unprecedentedly. The duties outlined for a CKO (Lester & Koehler, Ch. 7, 2007) are generally in keeping with the role the author would pursue, focusing on the Transportation industry generally, and automated vehicles specifically. Ideally, the author would hope to serve as a director for a dedicated division at the state or federal level, although alternatively within a municipality role such as Lexington-Fayette Urban County Government or Louisville Metro, primarily focusing on informing the myriad number of areas outside of transportation that would be affected by this revolutionary technology, such as Labor or Economic Development. Much of the ability to leverage this technology hinges on the ability to implement it in the best manner possible, which is why policy, both informational and otherwise, concerning automated vehicles is crucial. Equity concerns, for example are a major concern in this area; while mobility would be afforded to previously under-served groups such as the elderly, the disabled, or children, there are concerns that if the industry

advances on the current model that the benefits will severely favor the wealthy and could lead to unprecedented levels of congestion. On the flip side of the coin, if local or state policy is handled correctly, shared fleets of electric vehicles could be utilized to vastly reduce congestion, drive down the cost of transportation, and reshape cities into greener, pedestrian-focused locations. Being able to guide this development hinges on many ICTs, including database management for vehicle communications, cybersecurity issues to prevent bad actor involvement in the system, and basic technology management; too many entities attempt to buy a technology solution and then neglect to maintain it. Obviously, this position would not have existed 50 years ago, although there would be analogs in the form of city or state transportation planners. We have the opportunity to revolutionize the way in which we move people and goods, for the better or for the worse.

References Allenby, B. (2017, Summer). The Age of Weaponized Narrative, or, Where Have You Gone, Walter Cronkite? Retrieved from

Bohren, A. (2018, March 7). Cognitive Dissonance: How Does it Influence How We Think? Retrieved from

California Transportation Broken Governor Can Fix It [Digital Image]. (2018). Retrieved from

Cave, A. (2017, April 13). What Will We Do When The World’s Data Hits 163 Zettabytes In 2025. Retrieved from

Davis, C. H., & Shaw, D. (2013). Chapter 3: Information Needs Seeking and Use. Introduction to Information Science and Technology. Medford, NJ: Information Today.

Detlor, B. (2009). Chapter 68: Web Information Behaviors of Organizational Workers in Fisher, K.E., Erdelez, S., McKechnie, L., & Information Today. Theories of Information behavior. Retrieved from

Dmitriev, S. (2017, November 28). Autonomous cars will generate more than 300 TB of

data per year. Retrieved from

Durman, D. (2018). Information Science [Digital Image]. Retrieved from 07

Durman, D., Bertram, E., Davidson T., and Moore, S. (2018). Information Literacy Resources Repository. Retrieved from

Header-1024x438 [Digital Image]. (2017). Retrieved from

KM Pyramid Adaptation [Digital Image]. (2016). Retrieved from

Koch, C. (2010, July 1). Looks Can Deceive: Why Perception and Reality Don’t Always Match Up. Retrieved from

Lester, J., Koehler, W. (2004). The Impact of Information in Society. In Fundamentals of Information Studies: Understanding Information and Its Environment (pp. 1-12). Chicago, IL: Neal-Schuman Publishers

Lester, J., & Koehler, W. (2007). Chapter 7: The Information Professions. In Fundamentals of Information Studies (pp. 149-185). New York, NY: Neal Schuman.

Lester, J., & Koehler, W. C. (2007). Ch 10. Information, Power, and Society. Fundamentals of information studies: Understanding information and its environment. New York: Neal-Schuman. (pp. 241-263)

Marr, B. (2018, May 21). How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. Retrieved from

McCandless, D. (2010). The beauty of data visualization [Video]. Retrieved from ge=en

Number of motor vehicles registered in the United States from 1990 to 2016. (2017, September). Retrieved from

O’Neal, C. (2013). On Being a Data Skeptic [PDF File]. Retrieved from

Science [Def. 2]. In, Retrieved December 7, 2018, from

Stockwell, F. (2000). Chapter 17: The Complexity of Learning. A History of Information Storage and Retrieval. Jefferson, NC: McFarland & Company Inc.

The Scientific Method [Digital Image]. (2016). Retrieved from

Wikipedia contributors. (2018, October 18). Zettabyte Era. In Wikipedia, The Free Encyclopedia. Retrieved 13:36, December 7, 2018, from

Information Science In Transportation  
Information Science In Transportation