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Automatic Vehicle License Recognition Based on Video Vehicular Detection System
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作者 杨兆选 陈杨 +1 位作者 何英华 吴骏 《Transactions of Tianjin University》 EI CAS 2006年第3期199-203,共5页
Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based... Traditional methods of license character extraction cannot meet the requirements of recognition accuracy and speed rendered by the video vehicular detection system. Therefore, a license plate localization method based on multi-scale edge detection and a character segmentation algorithm based on Markov random field model is presented. Results of experiments demonstrate that the method yields more accurate license character extraction in contrast to traditional localization method based on edge detection by difference operator and character segmentation based on threshold. The accuracy increases from 90% to 94% under preferable illumination, while under poor condition, it increases more than 5%. When the two improved algorithms are used, the accuracy and speed of automatic license recognition meet the system's requirement even under the noisy circumstance or uneven illumination. 展开更多
关键词 vehicle license recognition license plate localization character segmentation
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Neural Network-Powered License Plate Recognition System Design
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作者 Sakib Hasan Md Nagib Mahfuz Sunny +1 位作者 Abdullah Al Nahian Mohammad Yasin 《Engineering(科研)》 2024年第9期284-300,共17页
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 ... 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. 展开更多
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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YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security 被引量:2
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作者 Fares Alharbi Reem Alshahrani +2 位作者 Mohammed Zakariah Amjad Aldweesh Abdulrahman Abdullah Alghamdi 《Computers, Materials & Continua》 SCIE EI 2023年第12期3697-3722,共26页
Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless... Privacy and trust are significant issues in intelligent transportation systems(ITS).Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels,optical fiber,and blockchain technology.The Internet of Things(IoT)is a network of connected,interconnected gadgets.Privacy issues occasionally arise due to the amount of data generated.However,they have been primarily addressed by blockchain and smart contract technology.While there are still security issues with smart contracts,primarily due to the complexity of writing the code,there are still many challenges to consider when designing blockchain designs for the IoT environment.This study uses traditional blockchain technology with the“You Only Look Once”(YOLO)object detection method to accurately locate and identify license plates.While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising,they have received limited research attention.Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network(RCNN)built with the tensor flow core open source libraries.This method works reasonably well for identifying any license plate.The Automatic License Plate Recognition(ALPR)approach delivered outstanding results in various datasets.First,with a recognition rate of 96.2%,our system(UFPR-ALPR)surpassed the previously used technology,consisting of 4500 frames and around 150 films.Second,a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset.Third,the license plate’s characters were complicated for standard methods to identify because of the shifting lighting correctly.The proposed model,however,produced beneficial outcomes. 展开更多
关键词 Intelligent transportation system blockchain technology license plate recognition PRIVACY YOLO deep learning technique ALPR
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Automatic Vehicle License Plate Recognition Using Optimal Deep Learning Model 被引量:5
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作者 Thavavel Vaiyapuri Sachi Nandan Mohanty +3 位作者 M.Sivaram Irina V.Pustokhina Denis A.Pustokhin K.Shankar 《Computers, Materials & Continua》 SCIE EI 2021年第5期1881-1897,共17页
The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of... The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%. 展开更多
关键词 Deep learning license plate recognition intelligent transportation SEGMENTATION
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Deep Learning Based License Plate Number Recognition for Smart Cities 被引量:1
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作者 T.Vetriselvi E.Laxmi Lydia +4 位作者 Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour 《Computers, Materials & Continua》 SCIE EI 2022年第1期2049-2064,共16页
Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enha... Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended objective.Precise controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city management.At the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic surveillance.Automated VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number plates.The current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license plate.The proposed model involves two main stages namely,license plate detection and Tesseract-based character recognition.The detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 model.Then,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)model.The performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods. 展开更多
关键词 Deep learning smart city tesseract computer vision vehicle license plate recognition
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A novel license plate recognition method using HTD and VTD features 被引量:2
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作者 Zhang Xiangdong Shen Peiyi Li Liangchao Wang Wei Bai Jianhua Zhang Wenbo 《Engineering Sciences》 EI 2010年第1期71-76,共6页
In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features i... In this paper, a novel method of licence plate recognition (LPR) using the vertical traverse density (VTD) and horizontal traverse density (HTD) is presented. The neutral network algorithm using VTD and HTD features is also an innovation. In addition, a so called secondary recognition method which splits characters into different parts is developed. Experimental results show that it is a simple and fast algorithm, which meets the request of real time and nicety performances of LPR and thus has applied value in intelligence transportation system (ITS). 展开更多
关键词 license plate recognition character segment character recognition VTD and HTD features
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License Plate Recognition for Parking Control System by Mathematical Morphology 被引量:1
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作者 Javier Ortiz Alberto Gómez 《Journal of Electronic Science and Technology》 CAS 2014年第1期81-84,共4页
Nowadays, license plate recognition for parking systems is a critical task to provide automatic control of customers and payment. This paper introduces a new method for automatic recognition of license plates of vehic... Nowadays, license plate recognition for parking systems is a critical task to provide automatic control of customers and payment. This paper introduces a new method for automatic recognition of license plates of vehicles by mathematical morphology. The proposed method can provide the license plate number of the plates in different light conditions, colors, sizes, and inclination (angles). The algorithm can recognize the license plates of European Union vehicles quickly and correctly. The pattern learning of mathematical skeletons has high efficiency in the process. The performance of the algorithm is demonstrated well by the test in a parking control system. 展开更多
关键词 license plate recognition mathe-matical morphology skeleton.
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Adversarial Attacks on License Plate Recognition Systems 被引量:1
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作者 Zhaoquan Gu Yu Su +5 位作者 Chenwei Liu Yinyu Lyu Yunxiang Jian Hao Li Zhen Cao Le Wang 《Computers, Materials & Continua》 SCIE EI 2020年第11期1437-1452,共16页
The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,re... The license plate recognition system(LPRS)has been widely adopted in daily life due to its efficiency and high accuracy.Deep neural networks are commonly used in the LPRS to improve the recognition accuracy.However,researchers have found that deep neural networks have their own security problems that may lead to unexpected results.Specifically,they can be easily attacked by the adversarial examples that are generated by adding small perturbations to the original images,resulting in incorrect license plate recognition.There are some classic methods to generate adversarial examples,but they cannot be adopted on LPRS directly.In this paper,we modify some classic methods to generate adversarial examples that could mislead the LPRS.We conduct extensive evaluations on the HyperLPR system and the results show that the system could be easily attacked by such adversarial examples.In addition,we show that the generated images could also attack the black-box systems;we show some examples that the Baidu LPR system also makes incorrect recognitions.We hope this paper could help improve the LPRS by realizing the existence of such adversarial attacks. 展开更多
关键词 license plate recognition system adversarial examples deep neural networks
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Libyan Licenses Plate Recognition Using Template Matching Method 被引量:1
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作者 Alla A. El. Senoussi Abdella 《Journal of Computer and Communications》 2016年第7期62-71,共10页
License plate recognition (LPR) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. The work presented in this paper aims to create a compu... License plate recognition (LPR) applies image processing and character recognition technology to identify vehicles by automatically reading their license plates. The work presented in this paper aims to create a computer vision system capable of taking real-time input image from a static camera and identifying the license plate from extracted image. This problem is examined in two stages: First the license plate region detection and extraction from background and plate segmentation to sub-images, and second the character recognition stage. The method used for the license plate region detection is based on the assumption that the license plate area is a high concentration of smaller details, making it a region of high intensity of edges. The Sobel filter and their vertical and horizontal projections are used to identify the plate region. The result of testing this stage was an accuracy of 67.5%. The final stage of the LPR system is optical character recognition (OCR). The method adopted for this stage is based on template matching using correlation. Testing the performance of OCR resulted in an overall recognition rate of 87.76%. 展开更多
关键词 license Plate recognition Optical Character recognition Computer Vision System
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Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition 被引量:1
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作者 Nitin Sharma Mohd Anul Haq +4 位作者 Pawan Kumar Dahiya B.R.Marwah Reema Lalit Nitin Mittal Ismail Keshta 《Computers, Materials & Continua》 SCIE EI 2023年第1期881-895,共15页
Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the au... Every developing country relies on transportation,and there has been an exponential expansion in the development of various sorts of vehicles with various configurations,which is a major component strengthening the automobile sector.India is a developing country with increasing road traffic,which has resulted in challenges such as increased road accidents and traffic oversight issues.In the lack of a parametric technique for accurate vehicle recognition,which is a major worry in terms of reliability,high traffic density also leads to mayhem at checkpoints and toll plazas.A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues.The Automatic Licence Plate Recognition(ALPR)system is one of the components of the intelligent transportation system for traffic monitoring.This study is based on a comprehensive and detailed literature evaluation in the field of ALPR.The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate.The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition.The properties of characters are employed to recognise the Indian licence plate in this study.For licence plate recognition,more than 200 images were analysed with various parameters and soft computing techniques were applied.In comparison to neural networks,a hybrid technique using a Convolution Neural Network(CNN)and a Support Vector Machine(SVM)classifier has a 98.45%efficiency. 展开更多
关键词 Intelligent transportation system automatic license plate recognition system neural network random forest convolutional neural network support vector machine
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Research and Implementation for License Plate Recognition Based on Improved Projection Algorithm 被引量:1
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作者 LI Xiu-juan Yimamu' aishan.Abudoulikemu 《Computer Aided Drafting,Design and Manufacturing》 2013年第2期53-56,75,共5页
This paper analyzes and dissertates the discrete wavelet transform and improved projection algorithm in four kernel stages (image preprocessing, license plate localization, character segmentation, license plate recog... This paper analyzes and dissertates the discrete wavelet transform and improved projection algorithm in four kernel stages (image preprocessing, license plate localization, character segmentation, license plate recognition, i.e.) of license plate recognition system in detail. At last, it gives some conclusions and suggestions on future research. 展开更多
关键词 license plate recognition wavelet transform improved projection algorithm
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Hybrid Metaheuristics Based License Plate Character Recognition in Smart City
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作者 Esam A.Al.Qaralleh Fahad Aldhaban +2 位作者 Halah Nasseif Bassam A.Y.Alqaralleh Tamer AbuKhalil 《Computers, Materials & Continua》 SCIE EI 2022年第9期5727-5740,共14页
Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On th... Recent technological advancements have been used to improve the quality of living in smart cities.At the same time,automated detection of vehicles can be utilized to reduce crime rate and improve public security.On the other hand,the automatic identification of vehicle license plate(LP)character becomes an essential process to recognize vehicles in real time scenarios,which can be achieved by the exploitation of optimal deep learning(DL)approaches.In this article,a novel hybrid metaheuristic optimization based deep learning model for automated license plate character recognition(HMODL-ALPCR)technique has been presented for smart city environments.The major intention of the HMODL-ALPCR technique is to detect LPs and recognize the characters that exist in them.For effective LP detection process,mask regional convolutional neural network(Mask-RCNN)model is applied and the Inception with Residual Network(ResNet)-v2 as the baseline network.In addition,hybrid sunflower optimization with butterfly optimization algorithm(HSFO-BOA)is utilized for the hyperparameter tuning of the Inception-ResNetv2 model.Finally,Tesseract based character recognition model is applied to effectively recognize the characters present in the LPs.The experimental result analysis of the HMODL-ALPCR technique takes place against the benchmark dataset and the experimental outcomes pointed out the improved efficacy of the HMODL-ALPCR technique over the recent methods. 展开更多
关键词 Smart city license plate recognition optimal deep learning metaheuristic algorithms parameter tuning
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PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data
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作者 Shan Zhang Qinkai Jiang +2 位作者 Hao Li Bin Cao Jing Fan 《Big Data Mining and Analytics》 EI CSCD 2024年第1期171-187,共17页
Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing met... Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain extent.However,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure.To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective characteristics.Subsequently,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online.The experimental results show that PURP retains strong prediction efficiency as the prediction period increases. 展开更多
关键词 traffic flow prediction k-Nearest Neighbor(KNN) license Plate recognition(LPR)data spatio-temporalcontext
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A Practical Method of Car License Plate Character Segmentation Based on Morphology and Labeling
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作者 WANG Ming-xiang, MO Yu-long School of Communication and Information Engineering , Shanghai University, Shanghai 200072,China 《Advances in Manufacturing》 SCIE CAS 2000年第S1期54-57,共4页
In this paper, a kind of practical image segmentation algorithm for segment characters from car license plate is presented, based on morphology and labeling. First by morphological operation, noise in the binary image... In this paper, a kind of practical image segmentation algorithm for segment characters from car license plate is presented, based on morphology and labeling. First by morphological operation, noise in the binary image of license plate can be greatly decreased. Then, by labeling, each connected pixel component is given a unique label. Finally, by the known data of license plate, each character is extracted correctly. The advantage of this method is that it can deal with plates with different sizes and connected characters plates, and inclined plates. The experiment results show that it is an effective way to extract characters from the license plate, and can be put into practical use. 展开更多
关键词 MORPHOLOGY LABELING car license plate recognition image segmentation character segmentation
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Traffic control optimization strategy based on license plate recognition data 被引量:2
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作者 Ruimin Li Shi Wang +1 位作者 Pengpeng Jiao Shichao Lin 《Journal of Traffic and Transportation Engineering(English Edition)》 EI CSCD 2023年第1期45-57,共13页
Traffic signal control is essential to the efficiency of the road network’s operation.In recent years,more and more detailed detection data provide potential data support for traffic signal control,such as license pl... Traffic signal control is essential to the efficiency of the road network’s operation.In recent years,more and more detailed detection data provide potential data support for traffic signal control,such as license plate recognition(LPR)data.This study aims to develop a traffic signal control optimization method based on model predictive control(MPC)and LPR data.The proposed framework of a closed-loop control system is described in detail.First,the control objectives and queue prediction model for signalized intersection are determined.Then,online optimization and feedback compensation are discussed and implemented.Calculations of the arrival rate at the downstream are based on the LPR data detected at the upstream intersection,and dynamic optimization method of the offset is proposed for a coordinated control.The model is validated using the LPR data of two consecutive intersections with a traffic simulation platform.Results demonstrate that the model can restrain extreme long queuing,improve intersection capacity,and reduce intersection average delay.The developed model promotes the system operating efficiency and shows the general advantage of real-time optimization,feedback,and control.The proposed framework can be potentially applied by local traffic management centers to improve the quality of traffic signal control. 展开更多
关键词 Traffic control Model predictive control Closed-loop control license plate recognition data
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Implementing VLPR systems based on TMS320DM642 被引量:3
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作者 ZHU Le-qing ZHANG San-yuan YE Xiu-zi 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2007年第12期2005-2016,共12页
This paper gives a practical schema for using DSP boards to construct Vehicle License Plate Recognition (VLPR) modules that could be embedded in any Intelligent Transportation System (ITS). Using DSP can avoid the hea... This paper gives a practical schema for using DSP boards to construct Vehicle License Plate Recognition (VLPR) modules that could be embedded in any Intelligent Transportation System (ITS). Using DSP can avoid the heavy investment in dedicated VLPR system and improve the computational power compared to PC software environment. Low cost, high computational power, and high flexibility of DSP provide the License Plate Recognition System (LPRS) an excellent cost-effective solution to execute the major part of the recognition tasks. This paper describes a successful implementation of VLPR system based on Texas Instruments (TI)'s TMS320DM642. The DSP board acquires video (which could be output to a monitor for surveillance) from a camera, captures images from the video, locates and recognizes the license plates in images, and then sends the recognized results and related images after compression to a host PC through the network. Finally, the overall software is optimized according to the features of DM642 chip. Experiments showed that the DSP VLPR system performs well on the local license plates, and that the processing speed and accuracy can meet the requirement of practical applications. 展开更多
关键词 license Plate recognition (LPR) Embedded system Image processing DSP DM642
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Precise and efficient Chinese license plate recognition in the real monitoring scene of intelligent transportation system
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作者 Jia Wei Gong Chao 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2022年第3期1-14,共14页
In this paper, the performance of you only look once(YOLO) series detectors on Chinese license plate recognition(LPR) in the real intelligent transportation system(ITS) monitoring scene is investigated. Specially, a p... In this paper, the performance of you only look once(YOLO) series detectors on Chinese license plate recognition(LPR) in the real intelligent transportation system(ITS) monitoring scene is investigated. Specially, a precise and efficient automatic license plate recognition(ALPR) system based on the YOLOv4 detector is proposed. The proposed ALPR system contains three stages including vehicle detection, license plate detection(LPD) and LPR. In vehicle detection stage, YOLOv4 detector is directly applied. In LPD stage, YOLOv4-tiny detector is exploited. In the last stage, the YOLOv4-tiny detector with attention mechanism for LPR is proposed to use. In addition, a large Chinese license plate dataset containing 10 500 images collected from all 31 provinces in the Chinese mainland is created. This Chinese license plate dataset is named Hefei University of Technology license plate version 1(HFUT-LP v1). Particularly, HFUT-LP v1 dataset is collected in the real ITS monitoring scene. In order to compare the performance of different object detection algorithms for ALPR, a variety of object detection algorithms are used to make a comprehensive performance evaluation. Experimental results show that the proposed ALPR system achieves very high accuracy and has very fast processing speed, which is suitable for real-time LPR. 展开更多
关键词 license plate detection(LPD) license plate recognition(LPR) YOLOv4-tiny detector attention mechanism intelligent transportation
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Uncovering the CO_(2)emissions of vehicles:A well-to-wheel approach
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作者 Zuoming Zhang Hongyang Su +3 位作者 Wenbin Yao Fujian Wang Simon Hu Sheng Jin 《Fundamental Research》 CAS CSCD 2024年第5期1025-1035,共11页
Carbon dioxide(CO_(2))from road traffic is a non-negligible part of global greenhouse gas(GHG)emissions,and it is a challenge for the world today to accurately estimate road traffic CO_(2)emissions and formulate effec... Carbon dioxide(CO_(2))from road traffic is a non-negligible part of global greenhouse gas(GHG)emissions,and it is a challenge for the world today to accurately estimate road traffic CO_(2)emissions and formulate effective emission reduction policies.Current emission inventories for vehicles have either low-resolution,or limited coverage,and they have not adequately focused on the CO_(2)emission produced by new energy vehicles(NEV)considering fuel life cycle.To fill the research gap,this paper proposed a framework of a high-resolution well-to-wheel(WTW)CO_(2)emission estimation for a full sample of vehicles and revealed the unique CO_(2)emission characteristics of different categories of vehicles combined with vehicle behavior.Based on this,the spatiotemporal characteristics and influencing factors of CO_(2)emissions were analyzed with the geographical and temporal weighted regression(GTWR)model.Finally,the CO_(2)emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies.The results show that the distribution of vehicle CO_(2)emissions shows obvious heterogeneity in time,space,and vehicle category.By simply adjusting the existing NEV promotion policy,the emission reduction effect can be improved by 6.5%-13.5%under the same NEV penetration.If combined with changes in power generation structure,it can further release the emission reduction potential of NEVs,which can reduce the current CO_(2)emissions by 78.1%in the optimal scenario. 展开更多
关键词 Carbon neutrality Well-to-wheel emission Emission characteristics license plate recognition data Geographical and temporal weighted regression model Emission reduction policy
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