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Alliance For Transportation Research (ATR)
As a result of the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) and the enlightened vision of planners in the Department of Transportation (DOT), transportation research has a bright future. With the completion of the Eisenhower National Interstate Highway System comes the challenge of maintaining this remarkable infrastructure and using it in the safest and most economically beneficial manner. ATR brings to the transportation community new capabilities for research and development (R&D) and mechanisms for rapidly integrating the results of research into the transportation market place.
ATR Composition and Organization
ATR is a partnership formed in October 1991 among New Mexico's major educational institutions: the University of New Mexico and New Mexico State University; two Department of Energy (DOE) national laboratories: Los Alamos National Laboratory and Sandia National Laboratories; and the New Mexico State Highway and Transportation Department (NMSHTD). These partners realize that external interactions are important to energize the enterprise. The president, David Albright, and his administrative staff are sponsored by the NMSHTD. Each of the participating national laboratories sponsors a vice president. Functionally, the staff reports to an executive committee that comprises high-level representation from each of the partners.
ATR is fortunate to have the active participation of advisors from both the Federal Highway Administration (FHWA) and DOE. In addition, an Industrial Advisory Board represents the active and growing interests of the private sector, and the National and International Research Council helps ATR keep in touch with global research activities in transportation.
ATR is unique in its approach of bringing together public and private knowledge and capabilities to address problems of national interest. Fundamental to the effectiveness of ATR has been its priority to find ways to contribute significantly rather than to first institutionalize an organization.
ATR Vision and Mission
Simply stated, the vision of ATR is to apply the enviable intellectual and physical resources available in New Mexico to important transportation issues. The governor and the department secretary have empowered the NMSHTD, in conjunction with industry and the other partners, to use the entire state highway system, which spans several climatic zones, as a test bed for research objectives. This collection of resources is proving attractive, both to the industrial participants and to the potential sponsors. Industrial participation is essential to the success of this cooperative research.
The primary work of ATR is to identify worthy, fundable research projects; to match the skills of the partners with the necessary tasks; and to disseminate the results.
National laboratories pursue technology transfer activities with the clear intent of providing the benefits from federal research to boost U.S. industrial competitiveness. Technology transfer became a mission of the national laboratories with the enactment of the National Competitiveness Technology Transfer Act of 1989, which is consistent with the Stevenson-Wydler Act of 1980. These acts establish the primary technology transfer mechanism, the Cooperative Research and Development Agreement (CRADA), and these agreements protect intellectual property for the industrial participants so they can profitably take the research results to market.
Summary of ATR Achievements
In its first year, ATR has stimulated the interest of the national laboratories in important transportation issues. The centerpiece of participation by the national laboratories has been their capabilities in enterprise and engineering modeling. Enterprise modeling is systems oriented and involves energy, environmental, and economic simulations based on a collection of validated models. Engineering models use the vast computing power of the laboratories to compute the properties of complicated physical systems under realistic conditions. These modeling and simulation capabilities allow planners to realistically investigate the consequences of decisions without expensive physical implementation. In addition, the laboratories have demonstrated interest in applying their resources in sensors, data processing, and data fusion to the crucial issues concerning the reliable acquisition of information from the roadway.
ATR has also attracted the attention of industry. Currently, about a dozen agreements exist, or are pending, between ATR and the industrial firms, both large and small, who see a commercial advantage in such a partnership. Among the companies represented in such agreements are JHK Associates, Barton-Aschmann, Hughes, Rockwell, Santa Fe Industries, General Atomic, IRD, and IBM. Some of these companies have set up offices in New Mexico to associate themselves more closely with the available resources.
ATR has illuminated the need for a proactive position regarding transportation and air quality. In Barcelona -- during the Olympic Games -- and in Mexico City, ATR partners demonstrated that the means exist to physically characterize air pollution in urban areas and to examine feasible remedial measures. This demonstration has led to an ATR proposal for a National Center for Transportation and Air Quality.
To link more effectively with the transportation research community, ATR has also reached out to other organizations. A program for research associates allows specific complementary capabilities to join with ATR in seeking solutions to transportation problems. ATR recently established associate relationships with Princeton University, Georgia Institute of Technology, the University of Florida, the University of Minnesota, and the New Mexico Institute of Mining and Technology. In addition, ATR has established an agreement with the FHWA's Turner-Fairbank Highway Research Center (TFHRC) to exchange staff for specific, mutually beneficial purposes. At this writing, three one-week exchanges have taken place.
Los Alamos National Laboratory and the University of California
We focus on Los Alamos as a specific member of ATR to illustrate, in somewhat more depth, the contribution such an institution can make to transportation research.
The Los Alamos National Laboratory is operated for DOE by the University of California (UC), which actually employs the staff of the laboratory. This relationship has existed since the Manhattan Project, which began in 1943 to construct the first atomic bomb. Since then, the laboratory has adopted a broad charter to address problems important to national security, which includes economic competitiveness and the effective use of energy. As a Federally Funded Research & Development Center (FFRDC), Los Alamos is prohibited from competing with industry and, therefore, from bidding on procurements, unless specifically invited to do so by the sponsor. Oversight of the laboratory is in the hands of DOE, and scientific and technical excellence are assured through the relationship with UC.
Specific Projects
In the following paragraphs, we briefly review some ongoing projects of interest to FHWA. These reviews are presented as progress statements because, in most cases, the funding has only recently been secured.
Policy and planning
Public policy is based on planning to achieve benefits demanded by the public. Decision-oriented planning provides planners with the information they need to make effective decisions. (1) An important and imperfect part of the decision-making process is the assessment of consequences of decisions. Transportation is a complicated system that involves both the details of individual behavior and the macroscopic properties of the economy, the environment, and public safety. Using its modeling and simulation capabilities, Los Alamos has embarked on an ambitious project to simulate the transportation system and subsystems and to use these simulations to examine the consequences of decisions faced by local, regional, and national planners. Such decisions may involve factors such as land use, vehicle or roadway component technologies, restrictions on traffic, or alternative fuels. The goal of the project is to produce a TRansportation ANalysis SIMulation System (TRANSIMS) that will advance the state of the art in simulation technology to the benefit of the transportation policy, planning, and engineering communities.
The approach to the problem is to integrate the process of transportation system design and analysis by developing an integrated suite of models and simulations. We intend to develop techniques that produce multi- and intermodal trip routing plans for individual loads with travel requirements. These "load-oriented" techniques will be developed to interface with emerging activity-based travel demand analytic methods. The trip routing plans will be coupled with a detailed microsimulation of the vehicles attempting to execute their plans in a particular geographic area. The data output from the vehicle microsimulations include the vehicle-byvehicle movement dynamics, the vehicle mechanical state, and the control logic state, both in space and in time. These low-level data can be used, for example, to produce accurate pollution load terms for predicting air quality, to indicate the severity of an accident in terms of momentum differences, and to obtain other forms of "higher-level" information. Large urban regions represent the geographical scale for which the TRANSIMS simulation capability eventually will he applicable.
TRANSIMS is an ambitious project that will take years to complete and will require the cooperative efforts of modelers both inside and outside the transportation community. For the first year's effort, we have begun to design and demonstrate proof of principle of the essential TRANSIMS elements. We are developing prototype route planning and microsimulation software, and we are assessing scaling issues in the TRANSIMS approach.
Neural network computational techniques applied to traffic monitoring and automatic vehicle classification
Los Alamos is executing a proof-of-principle demonstration of the value of neural network computational techniques to the application of traffic volume and classification monitoring. Artificial neural networks are a relatively new computational approach -- motivated by biologic neurological systems -- that uses networks of identical processing elements for learning inductively from data base examples. Such networks are effective in performing tasks such as signal processing; pattern recognition; feature extraction and classification; and the modeling, prediction, and control of complex, nonlinear systems. (2) These networks function in a manner similar to that of a conventional clustering algorithm, but with detailed architectural implementation and training algorithm differences that are motivated by current neural-network computational techniques. (3)
The goal in monitoring traffic is to determine the volume and the types of vehicles that are using existing streets and roadways. (4) Such data are valuable for managing and controlling traffic and for planning the maintenance and design of the highway infrastructure. Accomplishing this goal will require that many low-cost monitoring stations be instrumented for continuous, reliable data collection and reporting.
Included among the widely used traffic monitoring sensors are piezoelectric strip detectors and magnetic inductive-loop detectors. A typical traffic monitoring station includes an array of such detectors, configured in sets (such as piezo-loop-piezo or loop-piezo-loop) that are deployed in one, or several, highway lanes. The process of converting the electrical signals from detector arrays into vehicle classification data is difficult and is subject to the following potential errors:
- Nonreproducible sensor signals that depend on such uncontrolled factors as vehicle and sensor construction and vehicle, sensor, installation, and environmental conditions.
- Intermittent or continuous sensor or electronic failures.
- Traffic flow anomalies, such as lane-positioning, vehicle transitions, or highly variable speeds.
This project was conceived to address the several operational difficulties that lead to unreliable data and to provide:
- Increased volume and classification accuracy.
- Automatic screening and rejection of anomalies.
- Self-calibration and drift compensation within a designated range.
- Self-detection and diagnosis of sensor and recording-system faults.
- Automatic attention notification.
If these objectives can be met, a substantial improvement in traffic monitoring will be possible.
Los Alamos is designing and fielding experimental sensor test sites, examining typical conventional sensor signals, and instrumenting traffic monitoring test sites. Our initial test site, located near Los Alamos on state Route 4, was designed and constructed with redundant arrays of conventional sensors. The NMSHTD installed the sensors for this site and will install other developmental sensors in the future.
An approach to weigh-in-motion
Because of the severe damage caused to public roads by overweight trucks, federal, state, and local government agencies are concerned with overweight vehicle violations. The preferred method for measuring the weight of a vehicle is to measure it at highway speeds. (This method is most desirable because it places a very low compliance burden on legal truck traffic.) Weigh-in-motion instruments typically require extensive roadway modifications and, thus, tend to be very expensive. During the late 1980s, Los Alamos scientists, working on the instrumentation to verify compliance with arms reduction treaties, used fiber optics interferometry to develop weigh-in-motion sensors that do not require any roadway modifications because they are installed at the side of the road. These sensors are potentially much less expensive than current commercial sensors.
As a moving vehicle drives over the surface of a road, the pavement and the ground beneath it deform slightly, depending, primarily, on the weight of the vehicle. Then, as the vehicle moves on, the deformed pavement and the ground return to their original shapes. Because these deformations have both vertical and lateral components, sensors buried at the side of the road can make weigh-in-motion measurements based on the lateral deformations. Although imperceptible to the human eye and to most instrumentation, these deformations are large relative to the wavelength of light. Thus, fiber optic sensors and optical interferometry, which measure displacements with resolutions in wavelengths of light, enable such roadside measurements.
During the 1980s, the laboratory was faced with the problem of identifying various types of armored vehicles (tanks, etc.) moving in a stream of other vehicles. The idea was to install the fiber optic sensors and optical interferometric instrumentation in the former Warsaw Pact countries to count the armored vehicles as they moved in and out of the depots and maintenance facilities. These counts were to be used to verify compliance with the treaties that had been negotiated with the former Soviet Union. The instrumentation was required to identify armored vehicles by weight as they moved in streams of regular traffic, to avoid any interference with the traffic flow, and to operate unattended. The roadside installation satisfied all of these requirements.
In 1991, Los Alamos tested the instrumentation on various military vehicles at White Sands Missile Range. Under Los Alamos supervision, the NMSHTD installed similar sensors near a busy intersection in Albuquerque as part of a traffic and air quality study.
Nondestructive evaluation of bridges
Because of traffic safety considerations, the Rio Grande bridges on Interstate 40 in Albuquerque are being replaced with wider, prestressed concrete girder bridges. The 30-year-old existing bridges are fracture-critical, two-girder steel designs, typical of many two-girder designs built in the 1960s and 1970s. The fracture-critical c failure of either of the primary plate girders would result in a catastrophic failure of the bridge. The existing spans are scheduled to be razed in the summer of 1993. But before these structures are demolished, New Mexico State University and Los Alamos National Laboratory researchers will apply various state-of-the-art nondestructive test methods to one of girders. This planned destruction will advance the state of the art in nondestructive evaluation (NDE) techniques.
In support of this research effort, Los Alamos will develop detailed numerical models of the bridge and correlate the numerically predicted responses of the bridge with the observed physical responses. The advanced computing facility at Los Alamos will be instrumental in developing these models and analyses.
The numerical models will be used to determine the static and dynamic structural properties of the bridge. Los Alamos will use the results from the preliminary experiments conducted on the bridge to verify the numerical models. The predicted responses will be correlated with the measured results from the actual structure. We anticipate that the numerical models will have to be refined after we measure the experimental responses of the bridge. When benchmarked, these computer models will be used to benchmark other, simpler microcomputer-based numerical models, and the benefits of this research will be readily available to others in the NDE community.
Throughout the research effort, the computer models will be used to determine location of the instrumentation, required sensitivity, and the safety implications of introducing damage into the structure.
LIDAR, a powerful tool for air quality measurements
Recent measurements and demonstrations by Los Alamos National Laboratory, in collaboration with a variety of other organizations, have shown LIDAR to be a powerful tool for measuring air quality associated with vehicle emissions. Experimental campaigns conducted in Mexico City, Mexico; Barcelona, Spain; and Albuquerque have located sources of pollution, tracked the movement of aerosols, and located mixing layer boundaries as a function of time and traffic conditions.
LIDAR is an acronym for LIght Detection And Ranging (or laser radar). When a short pulse of laser light is emitted through the atmosphere, the light interacts with aerosol molecules and is then scattered back to the receiving telescope and detectors, which are usually located with the transmitting laser. The distance from the LIDAR unit can be determined by timing the pulse return. The elevation and azimuth directions of the unit determine the location of the scattering aerosol particles.
Different types of LIDAR systems are used for different purposes. The simple scattering systems (usually in the infrared) that observe light scattered back to the detector at the same wavelength (or color) as the laser will locate the aerosols, but they will not identify the species of the molecules doing the scattering. These systems have a longer range than the others because their infrared wavelengths of light are transmitted through the atmosphere with a minimum of absorption.
Two other types of LIDAR systems use Raman scattering and fluorescence to identify the species of molecules being located. These systems, which often use ultraviolet lasers, have shorter ranges because of atmospheric absorption and, often, can detect very low concentrations of fluorescent chemicals. In some cases, less than two parts per million have been observed.
The most versatile type of LIDAR system is the DIAL, or DIfferential Absorption Lidar system. This system uses two different wavelengths of laser light, one of which is tunable. The fixed wavelength is chosen so that there is very little atmospheric absorption; the tunable wavelength varies across the absorption bands of the molecules being measured. Thus, the DIAL system can identify most molecules. The wavelength requirements, however, make this system more complicated and more expensive than the others.
Several of the simple scattering LIDAR systems Los Alamos has are mobile or portable. One is the size of a large footlocker and weighs about 150 pounds, and some others are mounted in mobile vans. These systems have been used in various cities to locate sources of pollution aerosols and to track the movement of the aerosol clouds. Original data are color coded to show the variable intensities of aerosol concentration. Other data have been taken to measure the location of the mixing layer boundaries. These boundaries show where the more polluted near-surface air is trapped. The correlation of these data with data on traffic movement and traffic congestion levels could provide substantive information on pollution levels and sources and on possible corrective measures.
Los Alamos is developing smaller versions of these systems, as well as other environmental monitoring techniques.
Conclusion
ATR is rapidly organizing state, national, and private resources to address important transportation research issues. The national laboratories' contributions in this effort eliminate much of the risk associated with applying this new technology. Our goal is to assist DOT with improvements in environment, efficiency, and safety through the development and application of new technologies.
The following Los Alamos scientists contributed to this article: Chris Barrett, TRANSIMS; William Mead, automatic vehicle classification; Thomas Kuckerts, weigh-in-motion; Charles Farrar, NDE; and Dennis Gill, LIDAR.
References
(1) Michael D. Meyer and Eric J. Miller. Urban Transportation Planning, A Decision-Oriented Approach, McGraw Hill, New York, 1984.
(2) P.D. Wasserman. Neural Computing, Theory and Practice, Van Nostrand Reinhold, New York, 1989.
(3) R.D. Jones, Y.C. Lee, S. Qian, C.W. Barnes, et al. "Nonlinear Adaptive Networks: A Little Theory, A Few Applications," in Proceedings of the First Los Alamos Conference on Cognitive Modeling in System Control, Santa Fe, New Mexico, June 10-14, 1990.
(4) Traffic Monitoring Guide, Federal Highway Administration, Washington, D.C., June 1985.
(5) W.C. Mead, P.S. Bowling, S.K. Brown, R.D. Jones, et al. "Optimization and Control of a Small-Angle Negative Ion Source Using an On-line Adaptive Controller Based on the Connectionist Normalized Local Spline Network," Nuclear Instruments and Methods, B72 (1992) 271.
Daniel S. Metzger is program director for transportation programs at Los Alamos National Laboratory and leader of the Mechanical and Electronic Engineering Division. A veteran of many field operations ranging from the Nevada desert to Alaska and the Samoan islands, Dr. Metzger has a background in sensor systems and signal processing. He has served in several management positions at Los Alamos for the past 15 years. He earned his doctorate from The Ohio State University. After teaching for a year, he joined the staff at Los Alamos and became acquainted with field operations and instrumentation.
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