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This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.

SUMMARYThe entry capacity at a traffic roundabout is typically evaluated for each entry approach, considering the circulating flow and geometric characteristics, e.g., the US highway capacity manual model and the UK Linear Regression model. These models are not appropriate for analyzing multi-lane roundabouts because they do not take into account the possible unequal traffic distribution between the circulating lanes. This paper introduces a lane-based methodology that evaluates the entry capacity for each individual lane while considering the traffic distribution on the circulating lanes. The arrival and circulating flows are formulated based on drivers' lane choice patterns. We then modify and extend the formulae from existing models for the analysis of capacity of multi-lane roundabout. Based on the analysis, we show that higher capacity can be achieved when the utilization on the circulating lanes is more balanced. This result can lead to improved design and management techniques to increase the capacity of multi-lane roundabout.

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Constrained cognitive abilities cause imperfections in drivers' choice behaviour and appear largely systematic and predictable. This study introduces the concept of 'effective control space' to build upon this knowledge as an opportunity to increase the effectiveness of Dynamic Traffic Management (DTM). Within the control space boundaries it is assumed that drivers do not act upon the effects of DTM measures, they behave as being indifferent to them. This study debates that: (i) drivers' ability to detect changes in attributes of their trip or the performance of a traffic system is limited, (ii) drivers make mistakes in estimating the value of such changes and (iii) drivers apply a great diversity of choice patterns but do not necessary adapt their choice. Hence, for some DTM measures to be effective effects should not exceed the control space boundaries, whereas other DTM measures need to give drivers an incentive that exceeds these boundaries. Knowledge on the effective control space may support road authorities to operationalise their measures most effectively. With the theories of indifference bands and decision-making as starting point a theoretical and conceptual framework are provided, supported by a numerical example to demonstrate how application can steer a system towards its optimal state.