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Neural Network-Powered License Plate Recognition System Design

Engineering（科研） 2024年第9期16卷 284-300页

作者： Sakib Hasan Md Nagib Mahfuz Sunny Abdullah Al Nahian Mohammad Yasin School of Information and Electronics Beijing Institute of Technology Beijing China Department of Engineering and Technology Trine University Fort Wayne USA Department of Information Technology Management Westcliff University Irvine USA

The development of scientific inquiry and research has yielded numerous benefits in the realm of intelligent traffic control systems, particularly in the realm of automatic license plate recognition for vehicles. The design of license plate recognition algorithms has undergone digitalization through the utilization of neural networks. In contemporary times, there is a growing demand for vehicle surveillance due to the need for efficient vehicle processing and traffic management. The design, development, and implementation of a license plate recognition system hold significant social, economic, and academic importance. The study aims to present contemporary methodologies and empirical findings pertaining to automated license plate recognition. The primary focus of the automatic license plate recognition algorithm was on image extraction, character segmentation, and recognition. The task of character segmentation has been identified as the most challenging function based on my observations. The license plate recognition project that we designed demonstrated the effectiveness of this method across various observed conditions. Particularly in low-light environments, such as during periods of limited illumination or inclement weather characterized by precipitation. The method has been subjected to testing using a sample size of fifty images, resulting in a 100% accuracy rate. The findings of this study demonstrate the project’s ability to effectively determine the optimal outcomes of simulations.

关键词： Intelligent Traffic Control Systems Automatic License Plate Recognition (ALPR) Neural Networks Vehicle Surveillance Traffic Management License Plate Recognition Algorithms Image Extraction Character Segmentation Character Recognition Low-Light Environments inclement weather Empirical Findings Algorithm Accuracy Simulation Outcomes Digitalization

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Effects of winter weather on traffic operations and optimization of signalized intersections

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Journal of Traffic and Transportation Engineering(English Edition) 2019年第2期6卷 196-208页

作者： Zhengyang Lu Tae J.Kwon Liping Fu Big Data Research Center Jianghan University Department of Civil and Environmental Engineering University of Alberta Department of Civil and Environmental Engineering University of Waterloo Intelligent Transportation Systems Research Center Wuhan University of Technology

Adverse winter weather has always been a cause of traffic congestion and road *** mitigate the negative impacts of winter weather, transportation agencies are under increasing pressure to introduce weather responsive traffic management ***, most traffic signal control systems are designed for normal weather conditions and are therefore suboptimal regarding efficiency and safety for controlling traffic during winter snow events due to changes in traffic patterns and driver behaviors. The main objective of this research is to explore how to modify pre-timed traffic signal control parameters under adverse weather conditions to increase traffic efficiency and road *** research consists of two main components. First, we examine the impacts of winter weather on three key traffic parameters, i.e., saturation flow rate, start-up lost time, and free flow speed. Secondly, we investigate the potential benefits of implementing weatherspecific signal control plans for uncoordinated intersections as well as coordinated corridors. Two case studies are conducted, each with varying levels of traffic demand and winter event severity, to compare the performance of different signal plans. Evaluation results from both Synchro and VISSIM show that implementing such signal plans is most beneficial for intersection with a medium level of traffic demand. It is also found that the benefit of implementing weather-responsive plans was more compelling at a coordinatedcorridor level than at an uncoordinated-intersection level.

关键词： Signal optimization inclement weather Traffic simulation Signal coordination

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Microscopic and macroscopic diagnosis of the effect of winter weather conditions on distribution pattern of truck types in regional commuter road

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Journal of Modern Transportation 2018年第1期26卷 57-71页

作者： Hyuk-Jae Roh Satish Sharma City of Regina Old Fire Hall #1 1640 11th AvenuePO Box 1790 Regina SK S4P 3C8 Canada University of Regina Regina SK Canada

It has been reported in the past literature that total traffic volume and passenger car volume are affected by snowfall and cold temperature, but trucks are not sig- nificantly affected. This paper aims to confirm that the distribution of truck types is not affected by winter weather conditions through combined statistical analysis in the framework of microscopic and macroscopic impact anal- ysis. A micro-level analysis was conducted to investigate the effect of snowfall and temperature on changes in truck type distribution. A macro-level analysis was also con- ducted to investigate the effect of the months （or seasons） on changes in truck type distribution. Truck traffic data were collected for 5 years at the weigh-in-motion site in Highway 2A, which is used for regional commuters near the City of Leduc, Alberta, Canada. The trucks were sub- divided into straight unit truck, single trailer and multi trailer unit and then analyzed by applying a nonparametric Chi-squared test combined with the binomial probability test. As a result of the nonparametric test, the truck type distribution was stable irrespective of the severity of winter weather conditions, which are microscopic factors such as snowfall and temperature, and was not influenced by changes in months and seasons, which are macroscopic factors.

关键词： Truck traffic inclement weather Regionalcommuter road Weigh in motion Snowfall Temperature

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