QuickRide is an innovative project designed to more effectively use the capacity of the high-occupancy-vehicle (HOV) lanes on the Katy (I-10) and Northwest (US-290) freeways in Houston. Under this project, two-person carpools could pay $2 to use the HOV lanes during the peak period, even though the lanes were normally restricted to vehicles with three or more occupants. This form of HOV lane is typically termed a high-occupancy toll (HOT) lane and can be an effective travel demand management and congestion mitigation tool. However, relatively little is known about drivers who choose to use the HOT lane option. The commute and socioeconomic characteristics of Houston's QuickRide participants are examined by their frequency of QuickRide usage. The study is based on a survey of QuickRide enrollees conducted in March 2003. It was found that QuickRide participation increases with increasing trip length, perceived time savings, and frequency of trips in the travel corridor. Participation decreases with increasing carpool formation times but is generally irresponsive to minor changes in the $2 toll. QuickRide is also more likely to be used for commute trips than other trips. Socioeconomic characteristics such as age, household type, and education also have significant effects on QuickRide trip frequency. However, household size, occupation, and hourly wage rate were not good indicators of the frequency of QuickRide usage.

Travel time reliability quantifies variability in travel times and has become a critical aspect for evaluating transportation network performance. The empirical travel time cumulative distribution function (CDF) has been used as a tool to preserve inherent information on the variability and distribution of travel times. With advances in data collection technology, probe vehicle data has been frequently used to measure highway system performance. One challenge with using CDFs when handling large amounts of probe vehicle data is deciding how many different CDFs are necessary to fully characterize experienced travel times. This paper explores statistical methods for clustering CDFs of travel times at segment level into an optimal number of homogeneous clusters that retain all relevant distributional information. Two clustering methods were tested, one based on classic hierarchical clustering and the other used model-based functional data clustering, to find out their performance on clustering distributions using travel time data from Interstate 64 in Virginia. Freeway segments and those within interchange areas were clustered separately. To find the proper data format as clustering input, both scaled and original travel times were considered. In addition, a non-data-driven method based on geometric features was included for comparison. The results showed that for freeway segments, clustering using travel times and the Anderson–Darling dissimilarity matrix and Ward’s linkage had the best performance. For interchange segments, model-based clustering provided the best clusters. By clustering segments into homogenous groups, the results of this study could improve the efficiency of further travel time reliability modeling.

Driver behavior within the dilemma zone can be a major safety concern at high-speed signalized intersections. The Nebraska Department of Roads (DOR) has developed and implemented an actuated advance warning dilemma zone protection system. This paper investigates the impact that system has had on safety at high-speed signalized intersections. The operating algorithm has been designed such that the system continually monitors an upstream detector, as well as traffic at the intersection, to predict the onset of the yellow signal indication. Flashing beacons are used to warn drivers of the impending end of the green indication. Although these systems have received positive reviews from the public—and commercial vehicle operators in particular—there has been no comprehensive analysis of their effect on safety. The focus of this research was to address this evaluative need and provide answers about the effectiveness of the Nebraska DOR system in improving safety. Crash records from before and after the implementation of the system at 26 intersections were compared. In addition, 29 control intersections were used to compare crash rates over time, and a fully Bayesian technique was employed to ensure that no exogenous variables affected the study. Results of the analysis were promising (an overall crash reduction rate of 8%) and suggested that the use of the system should be encouraged as an effective safety treatment for the dilemma zone problem at high-speed signalized intersections.

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LAHIRI, SPIEGELMAN, APPIAH AND RILETT and then use some analytical considerations to put the individual pieces together, thereby alleviating the computational issues associated with large data sets to a great extent.The class of problems we consider here is the estimation of standard errors of estimators of population parameters based on massive multivariate data sets that may have heterogeneous distributions. A primary example is the origin-destination (OD) model in transportation engineering. In an OD model, which motivates this work and which is described in detail in Section 2 below, the data represent traffic volumes at a number of origins and destinations collected over short intervals of time (e.g., 5 minute intervals) daily, over a long period (several months), thereby leading to a massive data set. Here, the main goals of statistical analysis are (i) uncertainty quantification associated with the estimation of the parameters in the OD model and (ii) to improve prediction of traffic volumes at the origins and the destinations over a given stretch of the highway. Other examples of massive data sets having the required structural property include (i) receptor modeling in environmental monitoring, where spatio-temporal data are collected for many pollution receptors over a long time, and (ii) toxicological models for dietary intakes and drugs, where blood levels of a large number of toxins and organic compounds are monitored in repeated samples for a large number of patients. The key feature of these data sets is the presence of "gaps" which allow one to partition the original data set into smaller subsets with nice properties.The "largeness" and potential inhomogeneity of such data sets present challenges for estimated model uncertainty evaluation. The standard propagation of error formula or the delta method relies on assumptions of independence and identical distributions, stationarity (for space-time data) or other kinds of uniformity which, in most instances, are not appropriate for such data sets. Alternatively, one may try to apply the bootstrap and other resampling methods to assess the uncertainty. It is known that the ordinary bootstrap method typically underestimates the standard error for parameters when the data are dependent (positively correlated). The block bootstrap has become a popular tool for dealing with dependent data. By using blocks, the local dependence structure in the data is maintained and, hence, the resulting estimates from the block bootstrap tend to be less biased than those from the traditional (i.i.d.) bootstrap. For more details, see Lahiri (1999Lahiri ( , 2003. However, computational complexity of naive block bootstrap methods increases significantly with the size of the data sets, as the given estimator has to be computed repeatedly based on resamples that have the same size as the original data set. In this paper, we propose two resampling methods, generally both referred to as Gap Bootstraps, that exploit the "gap" in the dependence structure of such large-scale data se...