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Airport operators need good capacity estimates for many purposes, including to bolster applications for funding support for capacity improvements. Based on a recognized need to enhance models for estimating the operating capacity of airports serving a user base of smaller aircraft, such as small general aviation airports, this paper describes research conducted to leverage automated dependent surveillance-broadcast (ADS-B) data to develop aircraft performance characteristics for use as inputs to future small airport capacity models. The research addresses this challenge by constructing home-built ADS-B data collection units using a Raspberry Pi-based hardware platform and an Amazon Web Services-based cloud architecture housing the study’s PostGreSQL database, and python-based codebase. The work was focused at three participating airports: The Ohio State University Airport in Columbus, OH (KOSU); College Park, MD (KCGS); and Republic Airport in Farmingdale, NY (KFRG). The hardware units were deployed at three study airports, collecting more than 90 million individual ADS-B messages transmitted by aircraft operating within the vicinity of the study airport. Models were developed to clean, process, and assess the data, and leveraged to determine aircraft performance characteristics within these environments. Initial work focusing on assessing the fleet mix, approach speeds, aircraft separation in arrival streams, and runway occupancy times was performed. Findings from this research revealed a very high accuracy and richness of collected and processed data, and performance metrics that were reasonable, valid, and applicable for use as inputs to future airport capacity models.

En route inefficiency is measured in terms of extra distance flown by an aircraft, above a benchmark distance that relates to the theoretical shortest distance route (great circle route). In this paper, we have explored causal relations among en route inefficiency with multiple identified sources: convective weather, wind, miles-in-trail (MIT) restrictions, airspace flow programs (AFPs) and special activity airspace (SAA). We propose two mechanisms – strategic route choice and tactical reroute – to ascribe flight en route inefficiency to these factors. In our framework, we first propose an efficient trajectory clustering algorithm to identify nominal routes that reveal both air traffic flow patterns and air route structures in the national airspace system. Second, we develop a tree-based searching algorithm that matches different causal factors to the identified nominal routes in a 4-dimensional manner. Third, we employ a discrete choice modeling framework to quantitatively understand the flight route choice process, and establish a linear model to understand the tactical reroute process. Finally, we estimate the contributions of the identified causal factors to flight inefficiency through counterfactual analysis. Numerical experiments based on 8 representative airport pairs suggest strong negative impact of convection and headwind on route choice process, while MIT, AFP and SAA only demonstrate negative effects on some pairs. We have also shown that the contributions of convective weather, wind, traffic management initiatives, and SAA are up to 7.1%, 14.0%, 4.4%, 21.9%, respectively.

This paper presents results of a simulation of strategies to incorporate business-driven airline preferences in metering operations. Traffic flow systems that balance demand versus capacity at busy airports assign Controlled Times of Arrival (CTAs) to incoming flights. We evaluate a strategy to assign these CTAs based on user-provided priority lists. The user-provided priority is used to drive a slot swapping algorithm that looks for opportunities to rearrange the order of flights in the CTA queue in a way that decreases delay cost. We quantify potential savings by comparing the queue after swapping with the default first-come-first-served rule. Simulations under a variety of realistic scenarios show that our proposed algorithm could reduce delay costs between 1.3% and 17% relative to a baseline where no swapping is performed. Opportunistic swapping, however, presents challenges when handling equity between carriers. Operational considerations and potential solutions to establish equity are discussed.

Ground delay programs allow flights originating beyond a specified distance to become exempt from any delay imposed by the program. This exemption leads to a biased allocation that favors longer flights over shorter ones and alters an otherwise fair allocation. This paper presents two algorithms to reduce this exemption bias through speed control. The first algorithm attempts to assign the maximum possible delay achievable through speed control to the exempt flights. The second algorithm begins by prescribing the maximum possible delay to exempt flights but works to improve on this allocation by acting to fill holes in the schedule with speed-controlled exempt flights whenever possible. Both algorithms demonstrate considerable delay transfer relative to distance-based ration-by-schedule; however, the second one also reveals some ability to improve throughput.

This paper illustrates a continuum approximation to queuing problems at a single airport, adapted from the well-known diffusion approximation, as encapsulated in the Kolmogorov forward equation of stochastic processes or the Fokker–Planck equation of physics. The continuum model is derived using special artifacts of the airport problem context, and a numerical solution scheme based on the finite element method is presented. The results are compared against known stationary results from the M/M/1 process, as well as against airport scenarios generated from real demand and supply data. In both cases, a Monte Carlo simulation is used to provide ground truth results against which to compare the diffusion model, and is shown that the results between the Monte Carlo and diffusion models match quite closely.

We present a new type of unsupervised learning problem in which we find a small set of representative regions that approximates a larger dataset. These regions may be presented to a practitioner along with additional information in order to help the practitioner explore the data set. An advantage of this approach is that it does not rely on cluster structure of the data. We formally define this problem, and we present axioms that should be satisfied by functions that measure the quality of representatives. We provide a quality function that satisfies all of these axioms. Using this quality function, we formulate two optimization problems for finding representatives. We provide convergence results for a general class of methods, and we show that these results apply to several specific methods, including methods derived from the solution of the optimization problems formulated in this paper. We provide an example of how representative regions may be used to explore a data set.

We discuss a new approach to unsupervised learning and data exploration that involves summarizing a large data set using a small set of ``representative'' elements. These representatives may be presented to a user in order to provide intuition regarding the distribution of observations. Alternatively, these representatives can be used as cases for more detailed analysis. We call the problem of selecting the representatives the Unsupervised Prototype Reduction problem. We discuss the KC-UPR method for this problem and compare it to other existing methods that may be applied to this problem. We propose a new type of distance measure that allows for more interpretable presentation of results from the KC-UPR method. We demonstrate how solutions from the Unsupervised Prototype Reduction problem may be used to provide decision support for the planning of air traffic management initiatives, and we produce computational results that compare the effectiveness of several methods in this application. We also provide an example of how the KC-UPR method can be used for data exploration, using data from air traffic management initiatives at Newark Liberty International Airport.

During bad weather and under other capacity-reducing restrictions, FAA uses various initiatives to manage air traffic flow to alleviate problems associated with imbalanced demand and capacity. A recently introduced alternative concept to airspace flow programs is the collaborative trajectory options program, in which aircraft operators are allowed to submit sets of alternative trajectory options for their flights, with accompanying cost estimates. It is not clear that these sets of alternative trajectory options can be generated or evaluated quickly enough to respond to flow programs that arise unexpectedly or that the program is intended to be folded into a formal resource allocation mechanism. This research proposes (a) a meaningful, yet simple, way for carriers to express some preference structure for their flights that are specifically affected by flow programs and (b) a resource allocation mechanism that will improve system efficiency and simultaneously take these airline preferences into account. The results are compared with the events that could occur if an airspace flow program were run by using a ration-by-schedule approach, with or without the opportunity for carriers to engage in swaps among their own flights.

In this paper, a geometric model for predicting locations and times where sun glare has the potential to affect drivers on roadways was developed. Existing or prospective highway alignments can be considered. The method includes appropriate geometric models of the roadway alignment, astronomical computations for the relative position of the sun in the sky, and corrections for atmospheric refraction. Novel graphical tools were introduced to indicate the results of the model, which include sets of time and location where sun glare should be expected. The hypothesis that sun glare has an effect on congestion was tested by comparing real speed data from locations and times at which the geometric model indicates the presence of sun glare with those at which it does not. Sun glare is shown to have a statistically significant impact on mean traffic speeds.

Congestion in the air traffic system, both recurrent and non-recurrent, is typically handled by rationing access rights to individual resources such as airports or important parts of the airspace. Under the planning paradigm employed in the US, this rationing process occurs independently at each resource. The stochastic integer programming model proposed in this paper brings coordination to this process while considering capacity uncertainty. Results of a case study suggest that the model is tractable, and generates capacity allocations that improve efficiency and enable greater responsiveness to changing capacity conditions.

For decisions about air traffic flow management (ATFM), an accurate characterization of resource capacities at a lead time useful for planning is paramount. Unfortunately, this description is difficult to develop because of the complex nature of the airspace system and the unpredictable nature of the weather phenomena that often influence capacities. Capacity disruptions may be characterized simply by onset, duration, and severity; each of these parameters has a different effect on planning. Having a better understanding of the sensitivity of the air traffic system to uncertainty in each of these parameters can enhance decision making and improve model building. To help characterize the sensitivity of ATFM models to uncertainty in various capacity parameters, this research applies a modified Monte Carlo framework to a simplified model of capacity to identify output effects. In addition to the variations induced in resource capacities, randomness is included on demand profiles to avoid dependencies on a single demand scenario. The results demonstrate that ATFM decision making is quite sensitive to variations in each of the parameters used to characterize capacity. Typically, the impact of capacity variations is marginally increasing. The results of this type of analysis have several applications. First, the particular sensitivities of this deterministic model suggest that benefits may be realized by reformulating the model to explicitly consider stochasticity in resource capacity. Also, results suggesting greater sensitivity to specific capacity parameters may help to motivate research on mitigating uncertainty. Finally, this analysis presents an interesting application of the interplay between simulation and optimization techniques.

Despite recent decreases in air traffic, congestion is still at higher levels than it was several years ago. Because of this congestion and expected increases in traffic, strategic planning for daily operations in the air traffic system has taken on an increasingly important role. Weather typically has a strong influence in developing such plans, as it leads to degraded airport and airspace capacity, which in turn causes delays. However, many reported delays are induced by these initial effects. Thus, it is essential to understand the scope and impact of propagation of flight delays in a strategic setting. In this paper, delay propagation is examined over several years in spatial and temporal terms. Two models, each incorporating different levels of fidelity and flexibility, are applied in an effort to examine this phenomenon. The first model tracks individual aircraft as they operate several flights and explicitly separates the new delay from the propagated delay. The second model operates at a higher level by relating earlier and later delays at several airports. Results identifying the various impacts of delay propagation are reported for several airports and scenarios, and recommendations are made for incorporating these models and their output into strategic planning efforts.

Experimentation and analysis methodologies for data intended for the development of microscopic car-following models are presented. The data methodology centers on the notion that it is better to sample a varied group of real drivers when the subjects are unaware that an experiment is being conducted. This contrasts with the methods used to calibrate most conventional models. The analysis focuses on determining possible causal factors, beyond the kinematic variables most often used, that could play a role in real traffic. These factors include various human characteristics (e.g., gender and distractions related to in-vehicle conditions like telephoning and vehicle occupancy); traffic and road characteristics (e.g., type of vehicle, congestion level, and location of driving lane); and environmental characteristics (e.g., time of day and weather conditions). The paper shows the magnitudes of the relationships that are discovered and offers suggestions as to how these results might contribute to the development of better models.

A stochastic air traffic flow management problem arises when airspace congestion is predicted, usually because of a weather disturbance, so that the number of flights passing through a volume of airspace must be reduced. In the United States, in such situations, the FAA identifies the congested volume of airspace and implements an airspace flow program to regulate traffic. An optimization model was formulated for the assignment of dispositions to flights whose preferred flight plans pass through the congestion. For each flight, the disposition can be either to depart as scheduled but via a secondary route that avoids the congestion or to use the originally intended route but to depart with a controlled departure time and accompanying ground delay. The capacity of the congested area may increase once the weather activity clears. The model is a two-stage stochastic program that represents the time of this capacity windfall as a random variable and determines expected costs given a second-stage decision, conditioning on that time. The goal is to minimize the expected cost over the entire distribution of possible capacity increase times.

Many transportation design problems involve the demarcation of safe zones surrounding the intended guideway, either to prevent physical collisions or to provide clearance for other requirements such as sight distance. Typically, an instantaneous solution to one of these problems can be represented by a line in the plane; the entire solution is then the envelope of this infinite family of lines. In this paper the theory of envelope curves is applied to show how these infrastructure design problems can be solved in closed form. When the solution cannot be expressed in terms of familiar functions, an intuitive practical solution might be to use a finite set of lines to produce an approximate piecewise-linear solution. We show that under general conditions, such discrete solutions converge uniformly to a single continuous curve. In many cases the approximate solutions are perfectly acceptable with regard to error bounds and computation time; our results affirm the reasonableness of these approximate solutions by proving their convergence to the real solution.

Car-following models have been used in all microscopic traffic simulation modeling for almost half a century to describe the process of driver behavior in following each other in the traffic stream. In recent years, a detailed understanding of car-following behavior has become more essential for both the design and the assessment of advanced driver assistance systems, such as adaptive cruise control, to help improve appropriate algorithms and develop control strategies. However, previous experimental studies and models of car-following behavior have some important limitations, which make them inconsistent with real driving experience. Hence, this study aims to contribute to the better understanding of driving behavior in following a lead vehicle in car-following situations. Efforts have been made to disclose the problems and the limitations in previous experimental studies and models of car-following behavior; to build a new data collection system, including hardware and software architecture; and to investigate and discover the characteristics of real driving behavior in following a lead vehicle. It is hoped that the findings will provide clues to guide the construction of more realistic car-following models.

A variety of different materials have been considered and implemented for occupant protection in automotive head impact countermeasures since the beginning of the five-year phase of FMVSS 201 (Upper) on September 1, 1998. Polyurethane foam has been the material of choice because of its good performance, ability to maintain properties across a wide range of temperature conditions, reasonable cost, and ability to be molded into complex shapes. In recent years, pressure to reduce costs has resulted in an increased use of lower-cost thermoplastic materials. The thermoplastic nature of some of these materials can lead to significant changes in energy management characteristics with changes in environmental conditions. This paper shows that polyurethane foams are superior at retaining their energy-absorbing characteristics over the service temperature range of 30 to 85 °C.

When optimizing a highway alignment, it is desirable to consider new and modified intersections along it. In this paper, new methods are developed for locally optimizing intersections within highway alignment optimization processes. Design and operational characteristics for intersections are reviewed from the literature. The formulation considers the major costs that are sensitive to intersection characteristics. Genetic algorithms are used for optimal search. The proposed methods are implemented on an artificial study area and on a real one through the use of geographic information systems. Results show how the methods work for local optimization of intersections as well as for optimizing entire alignments. These methods can be used for improving search flexibility, thus allowing more effective intersections, and also provide a basis for extending the alignment optimization from single highways to networks.

Determining the optimal location of a fleet of vehicles is necessary in a number of potential applications, such as special repair vehicles for buses on a large public transportation network. The Athens Urban Transport Authority operates a large bus fleet over an extensive network for 19 h a day and serves a population of approximately 4 million people, all in a heavily congested road network. During the 2004 Summer Olympic Games, held in Athens, most spectators, employees, and volunteers were transported to and from Olympic Games venues by public transportation. Dedicated Olympic Games bus lines operated under a tight around-the-clock schedule. During normal operations and particularly during events such as the Olympic Games, incidents such as vehicle breakdowns and minor accidents can have a severe effect on the operation of the public transport network and can cause a significant decrease in the level of service. To help the authority locate bus repair vehicles over the entire network, a decision support system was developed on the basis of an embedded genetic algorithm used for obtaining optimal location solutions. The system's design and performance make it easy to operate under real-time conditions, which is useful for planning and for fast vehicle redeployment.

In this paper we propose a new model showing how genetic algorithms can be manipulated to help optimize bus transit routing design, incorporating unique service frequency settings for each route. The main lesson is in the power that can be given to heuristic methods if problem content is exploited appropriately. In this example, seven proposed genetic operators are designed for this specific problem to facilitate the search within a reasonable amount of time. In addition, headway coordination is applied by the ranking of transfer demands at the transfer terminals. The model is applied on a benchmark network to test its efficiency, and performance results are presented. It is shown that the proposed model is more efficient than the binary-coded genetic algorithm benchmark, in which problem content cannot be utilized.

This paper demonstrates how to construct the boundary of the clear zone adjacent to a roadway alignment, based only on horizontal sight distance considerations, when the alignment data are piecewise-linear on a fine lattice. In this case, it is shown that the clear zone envelope is piecewise-quadratic, and an efficient algorithm is given for constructing the parameters of each of the pieces of this curve. Special algorithm considerations are included, such as how to deal with overlapping and underlapping quadratic segments, as well as an efficient sort algorithm provided to initialize problem data properly.

This paper describes an enabling technology that could be used to develop electronic roadway signs and markers whose display content can be changed and that are selectively retroreflective. This would give them the good visibility of retroreflective signs at night, coupled with invisibility under circumstances where they are not meant to be seen, thereby reducing both confusion and light pollution. This paper describes how the half-silvered and blackened glass beads that constitute the visible components of the display were fabricated in the lab and derives their geometric optics, demonstrating their retroreflective capabilities. The paper also describes the construction of a transparent top electrode necessary to establish the electric field for changing the displays and derives some of the electric properties of the electrode and the resulting capacitorlike display. The work is in an early developmental stage, and the paper concludes with an outline of some of the remaining issues that need to be solved before a working device could be constructed.

This paper describes improvements developed for an existing algorithm that calculates sight distance profiles based on horizontal geometry. Exact expressions are developed for vector derivatives for plane curves including clothoid spirals. Coordinate and derivative vectors are developed for partial spirals that possess nonzero curvature at both ends, frequently used as transitions between circular curves of different radii. As with complete spirals, the coordinate vectors are approximate and the derivative vectors are exact. Finally, an exact method for measuring distance along an offset curve to a clothoid spiral is developed. Taken together with the results in a previous referenced paper, this represents a complete recipe for computing sight distance profiles along arbitrary horizontal alignments. A numerical example is included to illustrate the use of the algorithm.

This paper demonstrates how to approximate the boundary of the clear zone adjacent to a roadway alignment, based only on horizontal sight distance considerations. The alignment is divided into subproblems, and the clear zone boundary for each subproblem is approximated by a piecewise-linear space curve. The algorithms for this approximation are specifically designed to avoid numerical instability. Pseudocode versions of the algorithms are presented for the entire method. The errors in this approach are quite small compared to the expected errors generated while surveying and constructing roadways, and the error bound is shown to converge to zero uniformly as the parameter governing the resolution of the algorithm is made smaller.

This paper presents improved time-dependent control strategies for small freeway networks with bottlenecks and unique origin–destination paths. It is assumed that there are no spill-overs from any of the freeway exits so that freeway queues and delays can be completely avoided by regulating access to the system so as to maintain bottleneck flows strictly below capacity. It is also assumed that the time-dependent origin–destination table and the time-dependent bottleneck capacities are known, although not always a priori. The proposed control strategies attempt to minimize the total delay (including both system delay and access delay) while avoiding queues inside the system. The problem is formulated as a constrained calculus of variations exercise that can be cast in the conventional form of optimal control theory and can also be discretized as a mathematical program. Although the first-in-first-out (FIFO) requirement for the access queues introduces undesirable non-linearities, exact solutions for four important special cases can be obtained easily. More specifically, for networks with (1) a single origin or (2) a single bottleneck, a myopic strategy which requires the solution of a sequence of simple linear programs is optimal. For networks with (3) a single destination the non-linearities disappear and the problem becomes a large-scale linear program. This is also true for general networks if (4) the fractional distribution of flow across destinations for every origin is independent of time. A greedy heuristic algorithm is proposed for the general case. It has been programmed for a personal computer running Windows. The algorithm is non-anticipative in that it regulates access at the current time without using future information. As a result, it is computationally efficient and can be bolstered with dynamically-updated information. Globally optimal for cases (1) and (2), the heuristic has been developed with slow-varying O–D tables in mind. Significant improvements will likely require anticipatory information. An illustrative example is given.

We investigated seasonal changes in haematology and blood viscosity in free-ranging echidnas, Tachyglossus aculeatus (37 females, 28 males, and 17 juveniles), some of which had been fitted with radio transmitters, allowing them to be located and bled during hibernation. Only one haematological parameter, mean cell haemoglobin concentration, appeared to show any clear relationship with hibernation. Seasonal variation was most marked in females, with red-cell count, haematocrit, and haemoglobin levels highest in summer and lowest in spring (P < 0.001). Males had higher values than females or juveniles (P < 0.05). Plasma osmolality was highest in summer (307 ± 4 mosmol/kg, n = 11) and lowest in autumn (290 ± 6 mosmol/kg, n = 6). Contrary to expectation, blood from hibernating echidnas, when measured at 32°C and at a haematocrit of 45%, showed significantly higher viscosity than blood from nonhibernating echidnas. These increases in mean cell haemoglobin concentration and blood viscosity during hibernation appear to be due to a build-up of senescent red cells and a reduced rate of erythropoiesis.

This paper describes a method for automatic calculation of sight distance, subject only to constraints of horizontal geometry, along an arbitrary alignment of tangent sections, circular curves, and spiral transition curves. A parametric representation is used to describe the alignment and its tangent and normal vectors at all points. From this information, other parametric forms are derived that describe the curve along the center of the traveled lane and the lateral obstruction curve. Finally, an automatic method to determine sight distance is described, which is analogous to the manual “graphical method” often employed in practice. An example alignment is considered for demonstration purposes. The method is accurate, efficient, and consistent with manual calculations. It might be used for situations where detailed information about possible sight distance obstructions is not available, such as planning, preliminary design, or to support passing behavior modules of microscopic simulation models.

Technical and economic investigations of automated highway systems (AHS) are addressed. It has generally been accepted that such systems show potential to alleviate urban traffic congestion, so most of the AHS research has been focused instead on technical design and implementation issues. It is demonstrated that, despite making a number of assumptions that are favorable to AHS, the actual viable implementation opportunities for AHS are scarce, and that most existing congested urban areas can be disqualified on the basis of at least one criterion developed herein. Technical investigations are described, including realistic estimates of AHS capacity, interfacing with the local street system, and storage issues. Discussion then turns to identifying criteria to help establish the types of urban areas that might be likely candidates for AHS technology. These criteria relate to the nature of the surrounding infrastructure and the traffic demands placed on it, as well as the economic realities of AHS implementation. Certain “boutique” locations where AHS might be beneficial are identified, but it is uncertain whether enough benefit could be realized to make AHS palatable to the general public. AHS technology is not dismissed, but the simple analyses contained herein should warn that much more research into these areas is required before fully informed decision making about the future of AHS technologies can be accomplished.

The super heavy transport is one of the advanced vehicles envisioned in the future aviation systems. Due to its size and operational characteristics, some impact on the surface movements of other conventional vehicles is expected. Current practices regarding Airbus A380 aircraft at some U.S. airports might give us a clue as to how much such an impact would be. We have conducted descriptive analyses on taxi activities at two U.S. airports that currently have A380 operations and investigated the taxi delay of those operations around an A380 operation. Several possible cases are identified where super heavy transport could potentially cause interference with other surface movements. Corresponding models are then proposed to help incorporate the super heavy transport impact on surface movement into a large-scale simulation tool, and these are demonstrated with a hypothetical flight schedule. With the limitation of publicly accessible data sources, suitable statistical methods are applied to quantify the taxi delay due to super heavy transports, which is a necessary input of the proposed models.

Vehicular ad hoc networks (VANETs) are promising platforms from which decentralized traffic information systems can be built. In VANETs, susceptible situations, such as communication disruptions and low market penetration, could cause a dearth of information to be provided. This study examined the minimum quantity of data required for reliable travel time measurement over a variety of traffic conditions. Individual travel time data were obtained from computer simulation. The premise of this study, which is that data quantities can be reduced in certain situations, has important implications for the statistical methodologies used to assess sample quality, since appropriate conditions for the Central Limit Theorem might not be met. We compare such results with a distribution-free method called Acceptance Probability. Importantly, unstable traffic conditions, which are those of greatest interest for the purposes of studying congestion, also contribute to reduction in sample sizes, regardless of the technical quality of the wireless system at any given time. We show that in many realistic cases, standard sample size techniques under-estimate the required sample sizes and therefore a more refined approach is called for when using these data for estimation purposes.

A detailed understanding of car-following behavior has become more essential for both the design and assessment of advanced driver assistance systems (ADAS) to help improve appropriate algorithms and develop control strategies. We have analyzed data that have been collected with an instrumented test vehicle to show how driving patterns could be discerned in such data and described the initial findings from the preliminary results.

Short term traffic flow forecasting has played a key role in proactive and dynamic traffic control systems. A variety of methods and techniques have been developed to forecast traffic flow. Current nearest neighbor non-parametric traffic flow forecasting models treat the dynamic evolution of traffic flows at a given state as a memoryless process; i.e., the current state of traffic flow entirely determines the future state of traffic flow, with no dependence on the past sequences of traffic flow patterns that produced the current state (in existing nearest neighbor non-parametric models, the state includes only instantaneous conditions, not historic ones). Of course, traffic flow is not completely random in nature. There should be some patterns in which the past traffic flow repeats itself. In this paper, we have proposed a pattern recognition technique, which enables us to consider the past sequences of traffic flow patterns to predict the future state. It was found that the pattern recognition model is capable of predicting the future state of traffic flow reasonably well compared with the k-nearest neighbor non-parametric regression model. We hope that this paper is a good platform for the development of more effective nearest neighbor non-parametric regression models.

The primary goal of the paper was to elucidate the basic structure of a freeway access control problem, captured realistically before the application of any assumptions to improve tractability. Granted, it is not reasonable to postpone these assumptions indefinitely; the fluid approximations, for example, greatly improve the chances of realizing good analytical solutions. Rather than take them for granted, however, it is useful to investigate their consequences in the context of the original problem. This is an important problem, and there are many other aspects worth expanding upon that cannot fit within the confines of this survey paper.

E. Maring 1 , G. Raspanti 1 , G. Jaschek 2 , K. Hogan 3 , C. Farmer 4 , D. Lovell 3 , S. Grutzmacher 5 , P. Parikh 6 , S. Olcese 6 ; 1 University of Maryland, Maryland Institute for Applied Environmental Health, College Park, MD/US, 2 University of Maryland, Epidemiology and Biostatistics, College Park, MD/US, 3 University of Maryland, A. James Clark School of Engineering, College Park, MD/US, 4 University of Maryland, Dean’s Office, College Park, MD/US, 5 University of Maryland, Department of Family Science, College Park, MD/US, 6 University of Maryland, Global Public Health Scholars, College Park, MD/US