This paper presents results from a comprehensive laboratory study of the effects of a fine grained sediment (clay) carrier fluid on the hydraulic gradient of sand-sized sediments (600–2,000 microns) in a 103-mm diameter pipeline. Three sand sizes were evaluated in the pipeline: (1) medium sand (670 microns), coarse sand (1,260 microns), and very coarse sand (2,040 microns). Initial experiments were conducted with water as the carrier fluid. Sand concentrations of 0.05, 0.10, 0.20, and 0.30 by volume $\left(C_v\right)$ were evaluated. For each sand concentration, the effect of phosphate clay concentrations on the hydraulic gradient was evaluated. The clay carrier fluids exhibited both Newtonian and non-Newtonian fluid properties. Study results indicate that the hydraulic gradient reduction effect resulting from phosphate clay added to sand and water slurries increases with sand concentration, decreases with particle size, and is maximum within the flow range representing maximum solids stratification with bed contact (2.0–3.0 m/s) in the test pipeline.

The purpose of this investigation was to verify the results of an extensive laboratory experiment conducted to determine the effects of numerous variables on the performance of fiber-stabilized sand specimens. Laboratory unconfined compression tests were conducted on sand specimens reinforced with randomly oriented discrete fibers to provide an index of performance between specimens of different combinations of experiment variables. The laboratory results were used to design two full-scale field test sections to validate the performance of fiber-reinforced sands for low-volume roads. The laboratory experiment is discussed in detail in a separate paper. Only the field validation phase of the experiment will be discussed in this paper. The primary conclusion was that fiber-stabilized sands are a viable alternative to traditional road construction materials for temporary or low-volume roads. Additionally, the optimum values of the key variables identified in the laboratory experiment were verified through the comparison of individual test section items. Construction and maintenance guidance is provided.

Highway signs require breakaway base connections. These connections will slip under impact, therefore preventing injuries to the occupants of an impacting vehicle. The breakaway connection works as long as the tension of the bolts is maintained between an allowable range. A method to ensure that the bolts are tensioned to the proper value is presented. It is based on the use of Belleville spring washers. Laboratory and field tests were performed to verify the adequacy of the new method. Seven signs were selected along Florida highways. At each sign, the variations of tension in bolts installed with flat washers and with spring washers were monitored over a period of 1 year. The measurements showed that, at the time of installation, the new method was significantly more effective in ensuring the proper tension in the bolts. Similarly, over the long term, the bolts installed with spring washers maintained the tension in the bolt more effectively and without any loosening. The effectiveness of the breakaway connection with spring washers was also demonstrated during an actual car accident.

A multiparadigm general methodology is advanced for development of reliable, efficient, and practical freeway incident detection algorithms. The performance of the new fuzzy-wavelet radial basis function neural network (RBFNN) freeway incident detection model of Adeli and Karim is evaluated and compared with the benchmark California algorithm #8 using both real and simulated data. The evaluation is based on three quantitative measures of detection rate, false alarm rate, and detection time, and the qualitative measure of algorithm portability. The new algorithm outperformed the California algorithm consistently under various scenarios. False alarms are a major hindrance to the widespread implementation of automatic freeway incident detection algorithms. The false alarm rate ranges from 0 to 0.07% for the new algorithm and from 0.53 to 3.82% for the California algorithm. The new fuzzy-wavelet RBFNN freeway incident detection model is a single-station pattern-based algorithm that is computationally efficient and requires no recalibration. The new model can be readily transferred without retraining and without any performance deterioration.

The most widely used algorithm for solving traffic assignment problems is the Frank-Wolfe (FW) algorithm. Its popularity is attributed to its modest memory requirements and simplicity. However, it is also known that FW converges very slowly. This paper describes a method to accelerate the performance of the FW algorithm. It utilizes path information to speed the convergence of the algorithm by updating the flow pattern one origin-destination (OD) at a time. The results indicate that a FW implementation with an OD-based flow update takes less iteration to reach convergence but requires more computational times per iteration. This computational burden is due to the fact that the flow update of each OD pair requires a separate line search, which demands a significant amount of computational times, making it only competitive to the standard FW algorithm in some extreme cases. However, since the OD-based Frank-Wolfe algorithm takes fewer iterations to reach convergence, it requires much less computer storage and thus, is a better alternative to the standard FW if path-flow solutions are to be derived from a link-flow based FW algorithm.

A benefit-cost analysis framework is developed for an added bicycle phase at an existing signalized intersection. The intersection operates with both high volume levels of both vehicles and bicycles. As part of this study, traffic volume counts were collected and average vehicle delays computed and extrapolated to represent a full year. The costs and disbenefits included in the analysis were construction costs and changes in operating costs, safety, and vehicle capacity. We find that the overwhelming benefits associated with increased bicycle safety, which can be seen as a decrease in the number of bicycle-vehicle conflicts, outweigh all costs and the disbenefits associated with increased vehicle delay. Externalities such as the change in vehicle emissions are also examined but are not included in the benefit-cost ratio. We find that the change in emissions associated with increased vehicle delay is relatively minor.

Advanced Parking Management Systems use variable message signs to provide drivers with up-to-date information on the number of open spaces at selected parking lots throughout a city. Drivers passing a variable message sign must decide in which lot to park. Currently, it is not known what fraction will divert to each of the possible lots. This fraction is important to know, because it determines how large an affect an Advanced Parking Management System will have on traffic congestion. In this article, we report two experiments designed to test alternative models of drivers’ parking decisions. Both experiments use an advanced driving simulator. This simulator makes it possible to approximate the load that drivers actually experience on the roadway so as get to a more realistic assessment of the strategies employed. Previous research had indicated that drivers use some variation of expected utility theory to select among lots. We find instead that most drivers use a lexicographic decision strategy. Such a strategy greatly reduces the cognitive load on the driver.

Many existing freeway traffic management systems (FTMS) use speed data from loop detectors as input to various traffic management functions, such as automatic incident detection, and to traveler information system components. In many cases, these FTMS include single-loop detectors that are not able to measure vehicle speed. Typically, speed estimates are made on the basis of single-loop traffic volume, occupancy measurements, and estimates of average vehicle length. Unfortunately, the accuracy of these speed estimates is generally very poor. This paper presents a method for improving these speed estimates. The proposed method is applicable to FTMS that contain both single- and dual-loop detector stations. It does not require modification to field hardware or additional field equipment. The proposed method reduces the root mean squared speed estimation error by 23% on average over the traditional speed estimation method of using a constant, average, effective vehicle length for the entire day.

The decisions regarding data collection technique, instrument format, and sample design are crucial steps in any transportation planning. This paper presents the experiences of the writers with a pilot survey conducted for the identification of the best data collection technique and for the standardization of the survey instrument format to be used in studying the transit access behavior of commuters in the Mumbai (Bombay) region of India. Three data collection techniques and three variants of survey instrument format were considered for the collection of revealed information and stated information. The criteria used for the evaluation of these techniques were the nonresponse rate and missing data. It is noted that the face-to-face personal interview method is suitable for the collection of revealed information, and the delivered and mailed back method is suitable for the collection of stated information. Column format is found to be better for the collection of journey characteristics. Detailed format is better for recording responses under policy effects. The economic and efficiency analysis of the survey process is also reported. The problems faced by the survey administrators and enumerators are discussed.

This paper is an attempt to address the issue of data flow interdependency and error propagation in Advanced Traveler Information Systems/Advanced Traffic Management Systems supporting simulation-based models. A complex dynamic simulation model constitutes a number of processes, variables, or parameters with varying impact on the model’s state estimation accuracy. The overall model error might be attributed to the propagating internal system errors. This paper presents a framework for calibrating simulation models developed to support the dynamic traffic assignment systems. The calibration framework employs off-line analyses on the model processes, input, and system data, and the results are utilized to identify the model processes causing significant errors and to quantify the propagation of a specific variable error. The framework could be utilized to rank the model processes and their variables so that resources are allocated to calibrating significant error-causing entities. The framework is tested on a microscopic simulation model, MTSSIMA, and the results are discussed.

Suburban trains and public buses can play a better role in public transportation if they are coordinated. Coordination of these two services will reduce the journeys made by intermediate public transport services and private vehicles from railway stations, which have become major traffic generators. Thus, congestion, delays, and environmental pollution due to these services can be reduced to a great extent. In this study, the Andheri and Vileparle suburban railway stations in Mumbai, India are taken as study locations, and schedule coordination between suburban trains and public buses [Bombay Electric and Suburban Transport (BEST) buses] at these suburban railway stations is attempted. The coordinated schedules of BEST buses have been determined on already developed feeder routes for these two stations using the schedule optimization model (SOM). The objective function of the SOM is the minimization of transfer time between two services and vehicle operating costs of BEST buses. The objective function and constraints make the problem nonlinear and nonconvex with a large number of variables, making it difficult to solve by classical approaches. Therefore, the genetic algorithm, a robust optimization technique, is used for optimization. So far there have been few studies pertaining to the integration of public transport modes, and these studies were limited to analytical modeling. Analytical models do not meet real-life objectives under realistic constraints. In the absence of studies related to realistic modeling, it can be claimed that this study is a specific contribution toward operational integration of public transport modes.

Data collection is one of the most expensive tasks in many transportation-air quality studies. Finding effective ways to minimize data collection time and cost without losing vital information is crucial. This paper presents a new cost-efficient data sampling approach to solve an important transportation-air quality problem directly affecting estimates of mobile emissions. We use hierarchical, complete linkage cluster analysis on the correlation (similarity) matrices to statistically identify groups or clusters of count locations with similar hourly profiles. Once counts have been clustered, a single count within each cluster may be chosen to represent the hourly diurnal profile of count proportions. Our results indicate a savings of approximately $500,000 for data collection of 21 count-days over 319 count locations in the Central California Ozone Study. Without the sampling optimization, there would have been little way to determine the key counts needed to both minimize data costs as well as provide sufficient and robust information for subsequent modeling.