Called Edit Distance Research Articles

Tracking Large Trucks in Real Time with License Plate Recognition and Text-Mining Techniques

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.