2. BARRIERS TO AND BENEFITS OF ADUS

            ADUS can be viewed as a progression in three phases with increasingly challenging activities: archiving data, using the archived data, and sharing the archived data. Consequently, the barriers to ADUS can be characterized into three categories: (1) barriers to archiving data, (2) barriers to using archived data, and (3) barriers to sharing archived data. The barriers and benefits of each of these phases can be considerably different from those in other phases, although similarities exist. The decision to archive and use ITS-generated data typically hinges on the trade-off between the costs to remove the barriers and the benefits from using the archived data.

            It is difficult to quantify the costs and benefits of ADUS. In addition to the direct costs, the costs include efforts needed to:

            1.         Articulate and communicate the needs for archived data,

            2.         Save the data,

            3.         Re-format the archived data to a user-friendly format,

            4.         Re-vamp the existing software to accommodate archived data,

            5.         Address data quality issues,

            6.         Make archived data accessible on a timely manner,

            7.         Integrate the archived data with non-ITS data to meet broader data needs,

            8.         Reconcile data incompatibilities among different data sources, and

            9.         Forge mutually beneficial partnerships among data-producing agencies and data users.

2.1 BARRIERS

            The nature of the aforementioned three types of barriers can be characterized as decreasingly technical and increasingly institutional. For example, the barriers to archiving data are typically more technical in nature, while the barriers to sharing archived data are largely institutional in nature. Table 2.1 attempts to summarize barriers pertinent to different phases of ADUS. Only when barriers in all three phases are removed would ADUS reach its greatest potential. Nonetheless, the benefits are still substantial even when barriers in only a single phase are overcome.

Table 2.1 ADUS Barriers at Different Phases

            ADUS barriers include institutional inertia, concerns over privacy, proprietary, liability, data ownership, data liability, data integrity and quality, data compatibility, and other technical and technological issues. They can be categorized into five areas:

            ●          Institutional impediments,

            ●          Data issues,

            ●          Lack of standardization,

            ●          Privacy and liability, and

            ●          Other technological barriers.

2.1.1 Institutional Impediments

            In general, institutional concerns hinder the archiving and sharing of ITS-generated data. As with any “new” idea, there is initial inertia – a resistance to change. If the value of data archiving is not obvious to the data owner, why should additional cost and effort be expended to save the data? On the other hand, although the benefits of the archived data appear obvious in some cases (e.g., for long-term planning and managing incidents and operations), the actual implementation of archiving and using ITS-generated data is hampered by the lack of needed resources and “know-how.” A common reaction from data suppliers and data users is: “...the benefits of using archived data to meet my data gaps are obvious, but it is not at all clear how I can use these data and for what.” This barrier could be overcome by a combination of “technology pushes” and “application pulls.” The technology pushes could occur in the forms of generating and disseminating lessons learned, developing “how-to” guidebooks, and developing easy-to-use tools on calculating the expected range of costs and benefits of different ADUS applications. The application pulls could be in the form of deploying field testings of specific applications.

            Other than the resistance within an agency to change or to adapt to new data systems, the traditional stove-pipe organizational structure also discourages data sharing among agencies. Furthermore, this disinclination to share data among agencies involves a number of non-institutional issues. During one of the field interviews, concern about the quality of one’s own data was raised. It is clear that data should not be shared with other agencies until it is validated and quality-assured. Before ADUS can be widely adopted, this data stewardship/ownership issue needs to be addressed. Another institutional barrier is who should bear the cost of data archiving–data providers such as Transportation Management Centers (TMCs) or data users? In an increasing number of cases, this cost has been borne by the data user.

            The lack of communication or the lack of awareness adds another barrier to sharing and using ITS-generated data. In many cases, potential users of archived data are unaware of the existence of these data collections. For example, officials in City X are aware of the data collected in their own area, but they may not be aware of the data collected in City Y. A transportation engineer in City Z might be unaware of either collection of data. This lack of awareness about the availability of archived data is a very real barrier.

            Proprietary rights to value-added data (as such in the cases of freight and commercial vehicle operations (CVO) applications) can be another barrier to sharing the data. A lack of institutional cooperation among various data users might mean that disparate users duplicate data collection rather than jointly share in the costs of collections, ownership, and use.

            Institutional concerns become a completely different challenge when data sharing risks violating individuals’ privacy rights. Potential solutions to overcoming this barrier is a topic outside the scope of this project and will not be elaborated in this report.

2.1.2 Data Issues

            Although data are powerful, the impacts of mismanaged and misused data are wide-ranging. In the context of ADUS, data issues are multi-faceted and arise usually when using or sharing ITS-generated data. Data quality, format, integrity, compatibility, and consistency often heighten the complexity of using the archived data.

            Furthermore, with ITS-generated data being so temporally extensive (e.g., collected every 30 seconds) but spatially limited (e.g., covering 30 miles of roads), ADUS data sometime need to be integrated with data from traditional sources in order to be useful. Then, data integration becomes an issue. Figure 2.1 shows an example of an integrated data environment. In this system, data goes from the original owners (represented by the small boxes), through regional hubs for data fusion and filtering, to the Gateway for final integration into a regional view of the data. After integration, data flows from the Gateway to many “new” viewers/users as well as back to the Hubs for distribution. The Hubs may edit their own data but they may not revise the regional data which are received from the Gateway.^2.1

Figure 2.1 An Example Overview of an Integrated Data System

        Data quality issues include erroneous data as well as missing data. It was obvious from our previous research that the cost of using ITS-generated data to meet the information needs of traffic Monitoring is significant, particularly in terms of data “preparation.” This data preparation is extensive and includes checking data quality, identifying and correcting questionable data, imputing missing data, and formatting data to a format that can be “plugged” into the existing software. Figure 2.2 demonstrates an example of unacceptable loop detector data.

Figure 2.2 An Example of Unacceptable ITS-Generated Loop Detector Data

 2.1.3 Lack of Standardization

            The lack of standardization has hindered progress in many areas. A few examples of such barriers were observed in our previous study. Although an efficient and compact way to store data, the 16-bit data-storage protocol can present technical challenges to users. This is because the 16-bit binary data might be recorded differently on different computers (e.g., little-endian vs. big-endian, which indicates which of the 2 bytes comes first).

            Another example is the changing of sensor identification numbers (sensor ID). Although this type of change is documented by the data collection agency, the information is not integrated with the data file. The implication of not having this type of information integrated with the traffic data is that it is almost impossible to develop a traffic profile over time.

            From the perspective of standardization, our previous project on traffic monitoring data¹⁰ is rather straightforward in that it only focuses on augmenting or replacing traditional traffic monitoring data with ITS-generated traffic data. When ADUS advances to the point of integrating different information from multiple sources, then it will become important to develop standards to realize the full benefits of archiving and sharing ITS-generated data.

            ADUS has 14 different stakeholder groups. These stakeholders assert that the lack of consistent standards is one of the greatest barriers to successful ADUS implementation. Recognizing the need to develop standards, an effort is underway^2.3. Nine guiding principles have been established to develop standards related to archived ITS data^2.4.

2.1.4 Privacy and Liability Issues

            In the context of ADUS, it is important to distinguish between the concerns about archiving and using personal information that might violate the 1974 Privacy Act, from the concerns about using and sharing proprietary information. The Privacy Act of 1974 concerns the privacy of individuals. As stated in an overview of the act provided by the Justice Department,

Broadly stated, the purpose of the Privacy Act is to balance the government's need to maintain information about individuals with the rights of individuals to be protected against unwarranted invasions of their privacy stemming from federal agencies' collection, maintenance, use, and disclosure of personal information about them^2.5

Therefore, the term “privacy” pertains to individual or personal rights. On the other hand, “proprietary” rights relate to a company or profit-making organization having exclusive and legal rights to a process or product. These terms will be used in this report according to these definitions.

            The issue of privacy presents a legal barrier to certain types of data that may be collected and archived for needed analyses. Generally, the data used for traffic monitoring and transit operations such as loop detector data do not present a privacy concern. But the data needed for safety and CVO analyses may contain privacy related data which should be eliminated or protected to allow for needed trend analyses and findings.

            As with all laws, privacy laws are subject to interpretation. It is, however, widely accepted that surveillance cameras in public areas do not violate the Fourth Amendment (unreasonable search and seizure) because people in public places do not have a reasonable expectation of privacy. In fact, video surveillance has been used by city police departments on streets throughout the country, as well as in airports, museums, stores, banks, and ATMs just to name a few. Video surveillance in public areas is largely accepted as a way of life and in those instances where law suits have been brought claiming a violation of civil liberties, the courts have generally ruled in support of the use of public surveillance cameras. An example is the ruling in the Supreme Court case of United States vs. Knotts 368 U.S. 276, 281-82 (1983):

A person traveling in an automobile on public thoroughfares has no reasonable expectation of privacy in his movements from one place to another. When [an individual] traveled over the public streets he voluntarily conveyed to anyone who wanted to look the fact that he was traveling over particular roads in a particular direction, and the fact of his final destination when he exited from public roads onto private property^2.6.

If audio is recorded along with the video image, it becomes much more problematic, legally speaking. Title 1 of the Electronic Communications Privacy Act of 1986 limits the abilities of law enforcement to execute wire taps and to intercept other types of communications. If audio is recorded as a component of the video surveillance, then government agencies may be required to obtain a court order before the equipment may be installed^2.7.

            That said, the issue here is not whether surveillance recording devices can be installed in public areas. Rather, it is the issue of whether ITS-generated data that are recorded from these devices can be used for purposes other than the originally intended use. Some agencies have a far more open culture than others. Each state has its own public records laws, and some are more open than others. Ohio, for example, has a very open public records law that says that the public has the right to any information gathered with public funds for the price of a disk, stamp, or whatever it costs to physically duplicate that data^2.8.

            The most challenging barrier to overcome might be a closed-off, private culture within an agency. Some agencies who are reluctant to share information are concerned that they might provide someone with too much information about an individual, which might in turn lead to litigation. Even if the data collected appear to be harmless, when fused with other publicly available records, these data might result in information about a person which could be maliciously used. Part of the public sector’s concern on privacy and liability issues stem from the standpoint that litigation is an unwanted and unnecessary hassle.

2.2 BENEFITS

            In general, the benefits of using ITS-generated data are measured with respect to the value added by the ITS-generated data. Specifically, the benefits of ADUS can be categorized as providing:

            1.         More detailed temporal data (i.e., collected in very short-intervals), thereby increasing the robustness of the estimates,

            2.         Alternative data to the existing data, thereby reducing the costs of data collection,

            3.         Data with greater geographic coverage, thereby increasing the geographic representativeness of the estimates,

            4.         Data that were too costly to collect in the past, thereby meeting unmet data gaps, and

            5.         Data that are on electronic media, thereby expediting data analysis and information dissemination.

            Although the costs of ADUS are high at this early stage of deployment, it is anticipated that these costs will decrease while the benefits of using ITS-generated data will increase with increasing numbers of demonstration projects, sharing of best practices and lessons learned, making data “preparation” software and tools accessible and easy to use, and quantifying and demonstrating the benefits of ADUS.

            Should there be barriers specific to each ADUS application (e.g., Operations and Maintenance, Highway Safety), they are discussed in detail in the appropriate chapters.

ENDNOTES:

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