License plate recognition (LPR) technology is a mature yet imperfect technology used for automated toll collection and speed enforcement. The portion of license plates that can be correctly recognized and matched at two separate stations is typically in the range of 35% or less. Existing methods for improving the matching of plates recognized by LPR units rely on intensive manual data reduction, such that the misread plates are manually entered into the system. Recently, an advanced matching technique that combines Bayesian probability and Levenshtein text-mining techniques was proposed to increase the accuracy of automated license plate matching. The key component of this method is what we called the association matrix, which contains the conditional probabilities of observing one character at one station for a given observed character at another station. However, the estimation of the association matrix relies on the manually extracted ground truth of a large number of plates, which is a cumbersome and tedious process. To overcome this drawback, in this study, we propose an ingenious novel self-learning algorithm that eliminates the need for extracting ground truth manually. These automatically learned association matrices are found to perform well in the correctness in plate matching, in comparison with those generated from the painstaking manual method. Furthermore, these automatically learned association matrices outperform their manual counterparts in reducing false matching rates. The automatic self-learning method is also cheaper and easier to implement and continues to improve and correct itself over time.

In recent years, bus priority techniques for signals controlled by traffic management centers have become a viable alternative to reduce passenger delays at signalized intersections, especially in mixed traffic corridors. However, before any bus signal priority strategy is deployed in such corridors, the impacts on the different users of the system should be evaluated. The main objective of this work was to assess the operational performance of passive and active bus priority techniques in fixed and real-time signal systems of one of the main arterial corridors in Fortaleza, Brazil. As a secondary objective, it also evaluated the operational benefits of a SCOOT adaptive signal control system, comparing it with well-adjusted fixed-time plans optimized by TRANSYT, for periods of medium and high traffic volumes. In the evaluation of alternative scenarios, the following performance measures were considered: vehicle delay and number of stops simulated by SCOOT, as well as bus and automobile travel times observed in the field during the operation of each scenario. The results did not favor the adoption of passive and active priority schemes in the studied corridor; this led to the conclusion that SCOOT's real-time control, programmed for a good signal progression of the general traffic (buses and automobiles), is the best signal control strategy for an arterial corridor similar to the one under analysis.

Large-truck speeds on Interstate highways are not only a safety concern but have also become a problem with respect to air quality and energy consumption. Many cities have reduced truck speed limits to curb emissions. As potentially key building blocks of a proposed automated large-truck speed monitoring and enforcement system, license plate recognition (LPR) and plate-matching algorithms are the focus of this study. Present LPR systems are not perfect and can fail to read one-fifth to one-half of the characters on license plates, depending on various factors. Fortunately, even when LPR fails to read all characters on license plates, it is still able to read most of the characters with an appreciable degree of accuracy. By employing a text-mining technique called edit distance (with judicious use of travel time), this study demonstrates that it is possible to match 97% of license plates when only just more than 60% of the same plates were read correctly by LPR units at two different locations. This high matching rate does not entail a high false-positive matching rate either (about 2%). This paper presents the plate-matching algorithm in detail and provides statistics resulting from a field study conducted on I-40 in 2007. Several promising research directions for better matching efficiency and further reduction of the false-positive rate are identified.

In transportation planning, one of the first steps is to estimate the travel demand. The final product of the estimation process is an origin-destination (OD) matrix, whose entries correspond to the number of trips between pairs of origin-destination zones in a study region. In this paper, we review the main statistical models proposed in the literature for the estimation of the OD matrix based on traffic counts. Unlike reconstruction models, statistical models do not aim at estimating the exact OD matrix corresponding to observed traffic volumes, but they rather aim at estimating the parameters of a statistical model of the population of OD matrices. Initially we define the estimation problem, emphasizing its underspecified nature, which has lead to the development of several models based on different approaches. We describe static models whose parameters are estimated by means of maximum likelihood, the method of moments, and Bayesian inference. We also describe some recent dynamic models. Following that, we discuss research questions related to the underspecification problem, model assumptions and the estimation of the route choice matrix, and indicate promising research directions.