This paper describes an examination of the relationship between vehicular speed and side friction demand on horizontal curves. Models of speed and friction demand are described. The curve speed model explains the effect of approach speed, radius, and superelevation rate on curve speed. The side friction model explains the effect of approach speed and curve speed reduction on side friction demand. The terms in the side friction model reflect a general desire by motorists for a lower side friction demand at higher speeds. Model terms also reflect a willingness by motorists to tolerate slightly higher side friction demand in an effort to minimize the amount of speed reduction required by relatively sharp curvature. This model is recommended as a rational basis for defining the maximum side friction factors for use in curve design.

The procedure of road safety audit (RSA), which originated in Great Britain and is now being spread in several countries around the world, can be incorporated in the framework of designing, constructing, and operating road infrastructure as a means for preventing accidents. RSA, which can be applied to both new and existing road facilities, is typically carried out by an independent team consisting of highway engineers and/or traffic safety experts. The present paper focuses on the possibilities for further enhancing the consideration of human factor-related issues in RSA guidelines. This can be achieved through the incorporation of quantitative checks on consistency or driver work load. Such tests can give more weight to the auditors' reasoning. Moreover, the scope is investigated for providing increased opportunities for road user involvement in the RSA process. Public involvement is examined from the aspect of a group's representation, appropriate RSA stages, effective procedures, and responsibility. The possible means and methods for communication between auditors and road users' representatives are also discussed.

The National Highway System Designation Act of 1995 repealed the national maximum speed limit. As a result, approximately 70% of the United States has increased posted speed limits to 104.7 km/h (65 mi/h) for select multilane highways. This study evaluates the application of the Highway Capacity Manual multilane highway rules-of-thumb for free-flow speed to both an 88.6 and a 104.7 km/h (55 and 65 mi/h) posted speed limit condition. The paper further quantifies the observed relationship between the posted speed limit and observed free-flow speed on rural multilane highways in Georgia. Specific issues evaluated include heavy vehicle influence, traffic volumes, access point density, and vertical grade. The research indicates that the current Highway Capacity Manual rule-of-thumb free-flow estimation technique based on posted speed limit does not adequately estimate free-flow speed for the higher speed limit condition.

This paper presents and discusses the results and implications of an empirical study on the impact of the lane control signals (LCS) on resulting traffic patterns in the case of freeway lane closures. Effectiveness of LCS designs is evaluated through simulation experiments. A traffic simulation laboratory developed at the Massachusetts Institute of Technology, MITSIMLab, is used for simulating traffic and the operation of the LCS. The network used for the empirical study is a part of the Central Artery/Tunnel project in Boston. The use of LCS involves a trade-off between smooth lane changing and capacity underutilization. This paper explores this trade-off through two case studies. It is found that the effectiveness of the LCS design depends on lane configurations and directional volumes. Two measures of effectiveness are analyzed: (1) Total network travel time; and O-D specific travel time. The findings will help the evaluation of Intelligent Transportation Systems deployment projects from both economic (using total travel time) and equity (using O-D specific travel times) perspectives.

Conditional probabilities were used to explore the potential relationships between driver age and factors related to crash involvement including crash location, manner of collision, roadway character, speed of vehicles prior to crash, roadway surface conditions, and light conditions. The analysis indicated that young and old age groups are usually overinvolved in crashes. While young drivers tend to drive in situations or conditions that increase their risk, elderly drivers tend to avoid adverse conditions, presumably in an attempt to compensate for the decline in their driving capabilities. The results show that young drivers are overinvolved in crashes that are related to speeding, crashes on roadway curves, crashes that resulted in an overturn, head-on, or ran-off-road collision, crashes on weekends, and crashes at night. Older drivers tend to avoid bad weather or adverse driving conditions, and therefore their crashes tend to be in clear weather and during daylight settings. Regardless of the crash location, it was concluded from the analysis of the data that elderly drivers are overinvolved in right turn, left turn, and angle collisions.

A unified methodology has been proposed for the quantification of congestion, incorporating the volume and operational characteristics of traffic movement. The level of congestion has been modeled to relate to the causal influences of traffic movement. Modeling congestion has provided a quantitative basis for understanding the contribution of different vehicle types in overall congestion, and it is useful for evolving the policy for congestion mitigation. Quantified congestion level has been used as a logical and improved measure of effectiveness to account for the conceptual definition of level of service in a quantitative manner. Based on the congestion level, 10 levels of service have been proposed, with 9 in a stable flow zone (presently designated as A–E), and 1 representing an unstable operation (presently designated as F). The philosophy has been demonstrated by developing congestion models and assessing the effect of roadway width on congestion levels and service volumes. While it is possible to assess the realized benefits from an increase in roadway width, the required number of traffic lanes for a desired level of service can also be estimated.

This paper examines how real-time information gathered as part of intelligent transportation systems can be used to predict link travel times for one through five time periods ahead (of 5-min duration). The study employed a spectral basis artificial neural network (SNN) that utilizes a sinusoidal transformation technique to increase the linear separability of the input features. Link travel times from Houston that had been collected as part of the automatic vehicle identification system of the TranStar system were used as a test bed. It was found that the SNN outperformed a conventional artificial neural network and gave similar results to that of modular neural networks. However, the SNN requires significantly less effort on the part of the modeler than modular neural networks. The results of the best SNN were compared with conventional link travel time prediction techniques including a Kalman filtering model, exponential smoothing model, historical profile, and real-time profile. It was found that the SNN gave the best overall results.

The use of probe vehicles to provide estimates of link travel times has been suggested as a means of obtaining travel times within signalized networks for use in advanced travel information systems. Past research in the literature has provided contradictory conclusions regarding the expected accuracy of these probe-based estimates, and consequently has estimated different levels of market penetration of probe vehicles required to sustain accurate data within an advanced traveler information system. This paper examines the effect of sampling bias on the accuracy of the probe estimates. An analytical expression is derived on the basis of queuing theory to prove that bias in arrival time distributions and/or in the proportion of probes associated with each link departure turning movement will lead to a systematic bias in the sample estimate of the mean delay. Subsequently, the potential for and impact of sampling bias on a signalized link is examined by simulating an arterial corridor. The analytical derivation and the simulation analysis show that the reliability of probe-based average link travel times is highly affected by sampling bias. Furthermore, this analysis shows that the contradictory conclusions of previous research are directly related to the presence or absence of sample bias.

Travel time is the reciprocal of speed and is a useful measure of road congestion and traffic system performance. Travel time is also a basic traffic variable that is used in many intelligent transportation system strategies such as route guidance, incident detection, and traveler information systems. Previously, speeds were mainly acquired from double inductive loops configured as speed traps, because single loop speed estimates based on assumptions of a constant vehicle length were inaccurate. However, more accurate measurements of speed can now be accomplished with single loops by utilizing inductive waveforms of vehicles that are output from newer detector cards. An algorithm using signal processing and statistical methods was developed to extract speeds from inductive waveforms. The results show that the proposed algorithm performs better than conventional single loop estimation methods. The results also show that the algorithm is robust under different traffic conditions and is transferrable across surveillance sites without the need for recalibration. The use of the extensive single loop surveillance infrastructure is a cost-effective way of obtaining more accurate networkwide travel time information.

Excessive natural sand contents can increase the susceptibility of asphalt concrete to permanent deformation-type distresses. For this reason, pavement construction specifications usually include a limit for the proportion of natural sand that can be included in an aggregate blend. Although proportions of natural sand are controlled during the development of asphalt mixture designs, direct methods for monitoring natural sand contents are typically not included as part of construction quality control plans. The packing density of free-fallen aggregates, which can provide an indirect measure of particle angularity and surface texture, shows promise as a tool for monitoring natural sand content in aggregate blends. In the laboratory study presented in this paper, the free-fallen packing densities of aggregates extracted from asphalt concrete permitted predictions of natural sand contents to within approximately 5%. When used as a quality control tool in this study, these packing density measurements effectively identified changes in the natural sand content of asphalt concrete.

Evaluation of pavement condition is important in a pavement management system. In the existing evaluation methods, systematic analysis and regression modeling analysis are often used. But the former relies more on experienced judgment and the latter is restrained to use by the territory condition. For pavement evaluation, some data or properties relating to the environment and structure may not be very clear, and so the pavement system is gray in its nature. By applying the gray sole characteristic principle of the gray system theory, a comprehensive condition evaluation can be completed under partial data circumstances. In this paper, the principles used in the evaluation and selection of indexes are presented. In view of the gray characteristics of the pavement system, a set of comprehensive evaluating methods for concrete pavement organically combining the single indexes and a comprehensive index is presented. Dimensionless treatment is used to deal with the data, types of gray weight functions and their thresholds are determined, the gray weight function of each index when it belongs to a different gray class is introduced and deduced, and a general algorithm of the comprehensive condition evaluation is presented. Evaluations of various sections in a highway are carried out and compared.

Numerical simulation of fatigue crack growth in pavements could provide a simple and fast method to assess the resistance of the pavement to crack initiation and propagation under applied traffic loads. Such studies are facilitated through the development of appropriate computer algorithms and the use of high speed computers. On the basis of elastic-plastic fracture mechanics, a numerical methodology is presented in this paper to simulate fatigue crack growth and to predict fatigue life. This method utilizes two parameters, J-integral and R _curve, which characterize material elastic-plastic fracture behavior to define the crack growth criterion and find critical crack growth length. Fatigue crack propagation is simulated by shifting the R _curve along the crack growth direction. Fatigue life is calculated by integrating the fatigue crack growth rate established in this process from initial crack length to critical crack length. A simple example is presented to illustrate the proposed methodology. The numerical result is consistent with available experimental result.

In the capsule hoist system, cylindrical capsules, loaded with minerals, are inserted into the lower end of a vertical water-filled shaft. The capsules having low mass density are lifted due to natural buoyant forces. These capsules are taken out at the upper end of the shaft. The attractive feature of the system is that it only requires power for inserting the capsules. Compared with slurry transport no mechanism is required to separate the transported mineral from water, and it does not require a continuous supply of water. Although some research work has appeared in this area, the design equations have not been attempted as yet. In this paper the design of a capsule transporting hoist system has been formulated as a constrained geometric programming problem having zero degree of difficulty. The solution of the problem yielded the design variables in the form of explicit equations.

A nationwide survey of state Departments of Transportation (DOTs) found that 15 states are using design-build (DB) to procure highway construction projects. Analysis of their methods yielded three different systems to advertise and award DB highway projects. The methods are low-bid DB, adjusted score DB, and best value DB. The paper details the three methods and compares their strengths and weaknesses. It is concluded that each method is a valid system for procuring highway construction projects and that each method can be matched with certain project characteristics to optimize the project delivery method with respect to the salient aspects of the project.