A finite element procedure for the dynamic analysis of rigid airport pavements with discontinuities is presented. The analysis procedure considers the dynamic interaction between aircraft and a rigid pavement. The rigid pavement is modeled as a series of discontinuous, thin‐plate finite elements resting on a viscoelastic foundation. At the discontinuities, the load transfer devices are modeled as vertical springs, the stiffness of which depends on the dowel properties and the dowel‐concrete interaction. The viscoelastic foundation is modeled by distributed springs and dashpots. The moving aircraft loads are represented by masses supported by linear spring and dashpot systems, which have specified horizontal velocities and accelerations. Numerical results obtained from the analytical formulation presented are compared with available analytical solutions and experimental data. A parametric study is conducted to determine the effects of the various parameters on the dynamic response of jointed rigid pavements to moving loads.

This paper reviews some of the past instrumentation and performance measuring used by the U.S. Army Corps of Engineers to develop its rigid‐pavement design procedures, and it will consider two areas where future studies could improve our understanding of rigid pavement performance. Results of instrumental model tests and full‐scale traffic tests gave confidence in the Westergaard edge‐loaded analytical model to calculate design stresses. This model has been the backbone of the design concept for more than 40 years, and it continues to serve the Corps well. Also, performance measurements of joints developed the concept of joint load transfer that allowed more economical design of pavements and that also defined standards of joint design. There are still challenges facing the Corps, where instrumentation and performance measurements in the field can help develop better design standards. The actual effects of temperature and moisture gradients on rigid‐pavement performance and bonding in rigid‐pavement overlays remain poor and could be improved by field measurements.

Forecasting of air travel demand using demand models enables better planning for infrastructure development for air transportation. Modeling for air travel demand is the process of relating a vector of socioeconomic variables and a vector of transport system variables to the demand for air travel. In microanalysis of air travel demand, when models are stratified by origin and destination, the resulting models are called city‐pair models. In this study, a city‐pair model for the demand for domestic air travel has been developed based on the air travel in 40 city pairs in India. The model was calibrated with aggregate cross‐sectional data using multiple linear regression analysis. The model has been validated by two different procedures: the cross‐validation technique and a backward prediction method to know the statistical and logical validity of the model for use in travel demand forecasting. As an illustration of the usefulness of the model in travel demand forecasting, the model was used to predict future demand for air travel.

This paper presents a slow‐moving‐vehicle (SMV) model for estimating vehicular delays and queuing. The model is a simplified version of a shock‐wave model for the case where passing is restricted. The main inputs of the model consist of traffic volume and speed of both the traffice stream and the slow‐moving vehicle. Model outputs include queue length and vehicular delay for a specific traffic flow and distance over which passing is restricted. The slow‐moving‐vehicle model was used to estimate vehicular delay and queue‐length buildup for a proposed system of truck turnouts on a two‐lane highway during the movement of large overdimensional loads. The paper includes a description of the truck‐turnout system on the high‐wide‐load corridor in northern Alberta, Canada. The slow‐moving‐vehicle model could also be used to develop a system of turnouts along two‐lane highways, which would limit vehicular delays and queue length to acceptable levels.

A scheduling algorithm for the time‐window‐based, many‐to‐many, dial‐a‐ride problem is presented. The algorithm is based on two steps: developing the initial route and inserting leftover trips. These two steps are applied to the scheduling of one vehicle at a time. The paper introduces fuzziness in the values of two basic input parameters, travel time and the desired time of vehicle stop, and applies fuzzy‐arithmetic rules and logic to develop the schedules. Since travel time is fuzzy, the possibility of inserting a stop between two existing stops is evaluated by fuzzy grade. Further, the degree that the arrival time of the vehicle satisfies the desired time of the passenger is evaluated by the intersection of two fuzzy sets: arrival time and desired time of vehicle arrival. Thus, the feasibility of inserting a stop between two existing stops is evaluated by two criteria: (1) The feasibility of stopping at the stop; and (2) user satisfaction. Threshold values that specify the minimum acceptable level of stopping possibility and passenger satisfaction are introduced to evaluate the acceptability of the schedule. The proposed approach allows the dispatcher to develop different sets of schedules based on his judgment of the balance between supply and demand. The schedules may vary from a conservative (reliable) schedule requiring a large number of vehicles to a “risky” (unreliable) schedule requiring a small number of vehicles. The method should be useful for making schedules when travel time is not certain and has a large variation, such as the scheduling of vehicles in an urban area.

The traffic experimental and analytical simulation (TEXAS) model for intersection traffic, version 3.0 was used to simulate actuated signal control and traffic response at a diamond interchange with representative geometric features. Graeco‐Latin Square and one‐way analysis of variance experimental techniques were applied to investigate the effects of various actuated traffic signal controller settings under three different traffic demand conditions—two symmetrical, and one lopsided. Overall average total delay and stop delay for vehicles traveling through the simulated interchange area were used as the response variables in the statistical analysis and significance testing of data obtained from more than 2,500 runs of the TEXAS model. Results indicated that the settings of the timers that determine clearance‐green, green‐split, and end‐of‐maximum intervals on actuated diamond‐interchange signal controllers were sensitive to both intersection geometry and traffic demand and played a significant role in defining the optimum controller settings when implementing a three‐phase, lag‐lag phase‐sequence pattern. Findings from the study of a four‐phase‐with‐two‐overlaps, lead‐lead phasing pattern indicated that the advanced green timer and the internal detector logic were the only controller settings that had a significant effect on traffic performance for the conditions studied. The TEXAS model, version 3.0 is a powerful new analysis tool for determining the best signal timing at individual diamond interchanges operating under specific traffic, geometry, and control conditions; this study illustrates its application.

Improved traffic management and control systems are widely reported to be cost‐effective investments. Simply retiming signals can provide significant benefits by reducing vehicle stops, travel times, and fuel consumption. The installation of advanced traffic management systems (ATMS) can provide even greater savings. However, many hardware and software obstacles have impeded the actual implementation of advanced traffic management systems. The general hardware and software limitations of current traffic signal control technology are reviewed. The impact of these deficiencies is discussed in the context of three example applications. Based on this discussion, the paper identifies several computing issues that should be addressed in order to reduce the effort involved with integrating existing traffic control devices. Adoption of standard industrial control computing platforms and development of new communication and software engineering models are recommended.

Based on headway data collected under subfreezing conditions, with ice, snow, or frost present, saturation flow rates at signalized intersections were found to be substantially lower than the rates suggested in the Highway Capacity Manual of 1985. A reduction in flow rates on the order of 20% is proposed. A review of available U.S. and Canadian data indicated similar reductions. It was concluded that such reductions should be taken into account for level of service and capacity for areas where temperatures below freezing are encountered for substantial parts of the year. Even when these conditions are encountered for only a few days a year, it may be beneficial to assess levels of service and capacity, based on reduced saturation flow rates, to better understand the operation of the intersections and mitigate the effects of inclement weather.

The Illinois Department of Transportation (IDOT) operates a model freeway traffic management program in the Chicago area. There are three major parts to the IDOT program: (1) The traffic systems center; (2) the communications center; and (3) the emergency traffic patrol. With Chicago‐area expressway traffic peaking above 300,000 vehicles per day in some sections, this program helps maintain urban‐area mobility while promoting safety for 2,000,000 daily trips. This paper describes the overall IDOT program developed since 1961, with emphasis on the real‐time “advanced technology” applications. Although many of these applications are not really “advanced,” the entire program is perceived as “advanced” by most observers, including the public, media, and other government agencies.

This paper describes the 1990 test of an automatic vehicle classification (AVC) system employing an inductance detector loop and photoelectric sensors emitting infrared‐spectrum light beams, which were reflected off commonly used raised traffic lane markers. The passing of a vehicle activated the sensors. Signals from the activation go through a special microprocessor into a personal computer for interpretations, display, and recording. Vehicle speed and axle patterns are inferred from the pattern of the photoelectric beam interruptions. The system is tested several times at a high‐speed (50–65 mph) Oklahoma turnpike site by comparing the automatic classifications with classifications by a person. The researchers use computer printouts and written field observations to evaluate the number and type of errors. An error is said to occur when the automatic vehicle classification does not agree with the classification by the observer. The evaluation shows that the first‐generation AVC system correctly classifies about 95% of 1,736 observed vehicles. Further analysis of the raw data and the errors found therein suggests that, with modifications, the AVC system can correctly classify about 97–98% of the vehicles. Snow or heavy rain does not occur during the tests, so ability to classify in these conditions is not evaluated.