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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Real-Time Vehicle Number Plate Recognition and Smart Parking Allocation Using YOLOv8 and OCR for Intelligent Urban Mobility</title>
      <link>https://trid.trb.org/View/2658014</link>
      <description><![CDATA[Efficient vehicle monitoring and parking management are essential for modern smart city infrastructure. This study presents a real-time vehicle number plate recognition and automated parking allocation system using deep learning and OCR techniques. The framework employs YOLOv8 for fast and accurate license plate detection in live video streams, followed by text extraction with EasyOCR and Tesseract. A post-processing module refines the extracted text by correcting misclassified characters and validating formats based on country-specific rules. For operational efficiency, a rule-based parking allocation assigns vehicles to East Wing (first 10 slots) or West Wing (next 10 slots), triggering a real-time “Parking Full” notification when all 20 spaces are occupied. Vehicle details, including number, timestamp, and slot assignment, are logged into Excel sheets for monitoring. The system was evaluated on a custom real-world dataset of over 5,000 annotated vehicle images. Experimental results show strong performance, achieving 98.5% detection accuracy, 98.2% precision, 97.8% recall, and 98.0% F1-score, with an average inference time of 50 ms per frame. Comparative analysis demonstrates improvements over existing approaches in recognition accuracy, speed, and deployability. The proposed solution provides a scalable, cost-effective framework suitable for commercial, residential, and institutional parking environments.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658014</guid>
    </item>
    <item>
      <title>Has the special license plate promoted the adoption of electric vehicles? A quasi-natural experiment from China</title>
      <link>https://trid.trb.org/View/2639484</link>
      <description><![CDATA[The implementation of incentive policies has proved its importance in promoting the adoption of electric vehicles (EVs). In response, China’s government introduced a series of preferential measures aimed at speeding up the widespread adoption of EVs to replace traditional internal combustion engine vehicles (ICEVs). This study employs a panel data of 248 cities across China, spanning the period from 2016 to 2019, and utilizes a Difference-in-Differences (DID) approach to examine the impact of the Special License Plate for New Energy Vehicles (SPNEV) policy on the promotion of EVs. The results show that the SPNEV has a significant positive impact on EV sales among both individuals and institutional owners, with estimated increase in sales of 37.8% and 35%, respectively. Notably, the policy is more pronounced in cities subject to driving restrictions on ICEVs and those experiencing heavy traffic pressure. Furthermore, the findings suggest that the SPNEV plays a beneficial role in cities characterized by lower temperatures, second-tier and third-tier economic development levels, and smaller urban populations. These findings contribute meaningfully to the existing body of empirical evidence on the efficacy of incentives in facilitating low-carbon transformation in the transportation sector across diversified national settings.]]></description>
      <pubDate>Thu, 12 Mar 2026 14:02:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639484</guid>
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    <item>
      <title>On an Interlocking Flexible Car Use Restriction Policy: Theory, Learning and Experiment</title>
      <link>https://trid.trb.org/View/2616174</link>
      <description><![CDATA[Car use restrictions, such as the commonly implemented license plate restriction policy (PRP), are regarded as effective strategies for alleviating traffic congestion in many cities worldwide. Although these regulations help reduce daily vehicle volumes and evenly distribute them, they also limit commuters’ freedom to choose when to drive. A recent flexible car use restriction policy (FRP) allows commuters to select specific days to refrain from using their cars within a restriction cycle, enhancing travelers’ flexibility in urgent situations. Although the original FRP (O-FRP) may incur slightly higher average driving costs compared with the PRP due to uneven car use distribution, it can ultimately lower overall travel costs by accommodating more car uses during emergencies. This study introduces an interlocking FRP (IL-FRP), which groups travelers and assigns them different restriction cycles starting from distinct weekdays. Theoretical analysis reveals that, even under relaxed nonlinear travel cost assumptions, the user equilibrium solution of the IL-FRP, under ideal conditions, converges with the system optimal solution and further reaches the FRP’s theoretical lower bound while minimizing both driving and transit costs. Additionally, we develop two IL-FRP variants that are applicable regardless of the urgency probability distribution: the equally grouped FRP (ILE-FRP) and the arbitrarily grouped FRP (Arb-FRP). To derive equilibrium solutions for all FRPs under different parameter settings, we present a three-step learning algorithm using the mean field game framework. Numerical experiments validate the effectiveness of this algorithm and demonstrate that IL-FRP, ILE-FRP, and Arb-FRP offer advantages over O-FRP in terms of total travel cost. A series of laboratory experiments was conducted to support our theoretical findings. The results indicate some bounded rationality among individuals but align consistently with our theoretical predictions and learning outcomes.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:33:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2616174</guid>
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    <item>
      <title>Efficient Filtering of Transit Traffic Using Cleaned Matched License Plate Data: A Case Study in Prague</title>
      <link>https://trid.trb.org/View/2627782</link>
      <description><![CDATA[Transit traffic, vehicles that traverse a study area without stopping, can significantly distort origin-destination matrices and compromise the effectiveness of traffic planning. This paper presents a refined statistical algorithm designed to isolate transit traffic from matched license plate data derived from ANPR (Automatic Number Plate Recognition) surveys. Building upon a previously validated method based on travel time distributions and outlier detection, we apply the algorithm to a real-world case study in Prague-Zbraslav. The results confirm the algorithm’s practical effectiveness, highlighting its utility in refining origin-destination matrices and supporting data-informed urban mobility decisions.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627782</guid>
    </item>
    <item>
      <title>Attention-Assisted Dense Gated Convolutional Network: License-Plate Classification Using Deep-Learning-Based Effective Feature-Extraction Module</title>
      <link>https://trid.trb.org/View/2639373</link>
      <description><![CDATA[Traffic-management systems depend on the identification and recognition of license plates to perform tasks such as traffic monitoring, toll collection, and regulation enforcement. However, challenges such as blurriness, dust, shadows, and various lighting conditions often hinder accurate license-plate detection. This paper addresses these challenges by using a novel hybrid deep-learning model. Initially, enhancement contrast-limited adaptive cumulative histogram equalization (ECLACHE) is utilized to improve the contrast and quality of the images. The modified Swin transformer (M-ST) is then employed to extract deep features. The extracted features are fused together using a feature-pyramid fusion module. Finally, the attention-assisted dense gated convolutional network (AADGCN) model is utilized to classify the license plates efficiently. The proposed model achieves an accuracy of 99.12%. Through comprehensive experimentation and evaluation, this paper demonstrates the effectiveness of the proposed strategy in addressing the problems of overfitting and underfitting that are common in existing detection models. These results show significant improvements in detection and recognition accuracy, demonstrating the potential of the proposed methodology to enhance the performance of intelligent traffic-management systems.]]></description>
      <pubDate>Thu, 11 Dec 2025 16:38:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639373</guid>
    </item>
    <item>
      <title>Examining public pre-acceptance of license plate restriction policy cancellation using structural equation modeling: Evidence from Hangzhou</title>
      <link>https://trid.trb.org/View/2604823</link>
      <description><![CDATA[To better understand the factors influencing the pre-acceptance of canceling the License Plate Restriction (LPR) policy among Hangzhou residents, a questionnaire survey was conducted with 958 residents, and two analytical methods were applied. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to examine the complex relationships between psychological factors. Meanwhile, ordinal logistic regression was employed to analyze the influence of socio-economic attributes, such as gender, family life cycle, commuting time, and vehicle ownership, on policy pre-acceptance. The results indicate that the acceptance of alternative measures, perceived infringement, social norms, post-acceptance of the LPR policy, perceived effectiveness, perceived cost-benefit, and fairness significantly influence the public's pre-acceptance of canceling the LPR policy. Through the analysis of socio-economic attributes, it was found that families with children, groups with higher travel demands, and those with fewer vehicles are more likely to support canceling the LPR policy. Among these, males show a greater willingness to cancel the LPR policy compared to females. This study provides valuable insights for policymakers, offering evidence-based recommendations for optimizing Traffic Demand Management (TDM) strategies, particularly as the effectiveness of the LPR policy declines over time. The findings are significant as they contribute to the understanding of public attitudes toward policy change and can inform future traffic management reforms.]]></description>
      <pubDate>Tue, 11 Nov 2025 09:25:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604823</guid>
    </item>
    <item>
      <title>Vehicle Detection at Night Based on the Feature of Taillight and License Plate</title>
      <link>https://trid.trb.org/View/2407673</link>
      <description><![CDATA[This paper proposes an algorithm of vehicle feature extraction and detection based on video data for night time. The color characteristics of taillights can be roughly divided into two parts no matter how far or near they are: inner ring-highlights area partial to pink and outer ring-high saturation red areas. Through a large number of sampling statistics, this method obtains the accurate threshold range of each layer based on HSV color space. Thus, the suspected area of the inner and outer ring of tail lights can be segmented accurately and filtered preliminarily according to the shape characteristics of the tail lamp. In order to improve the detection rate and image recognition quality, the paper carry out AOI region segmentation and median filtering. After getting the suspected area of license plate, the tail light and license plate are combined to determine the rear of the vehicle. Secondly, all the connected regions of the tail lamp suspected area are paired and the confidence level of the pair is established. The confidence level is evaluated according to the characteristics of the tail lamp pair such as the horizontal height, the distance width and the symmetry centered on the license plate. According to the confidence level, whether it is qualified to pair with the license plate is determined Finally, according to the characteristics of taillight pairs, the mismatched relationship pairs are eliminated and the vehicles are identified. The experimental results show that the method can accurately detect the vehicle tail light features to identify the vehicle, and the false detection rate is low.]]></description>
      <pubDate>Mon, 18 Aug 2025 08:51:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407673</guid>
    </item>
    <item>
      <title>License plate lottery vs charging station density in electric vehicle adoption: Evidence from the early stage of market penetration in Beijing, China</title>
      <link>https://trid.trb.org/View/2564245</link>
      <description><![CDATA[The widespread adoption of electric vehicles (EV) is essential for mitigating climate change and reducing greenhouse gas emissions. While numerous studies have examined the primary drivers of EV adoption, the role of license plate policies remains underexplored. To address this gap, the authors collected stated preference data in Beijing, China, during the early phase of EV market penetration in 2017. Utilizing both of multinomial logit (MNL) and mixed logit (MXL) models, they assess the influence of several attributes on consumer vehicle choice, including vehicle price, charging infrastructure density, driving range, recharging time, and waiting time for license plates. Moreover, examining elasticities and conducting a series of scenario simulations provides us with a more thorough analysis of the impact of these attributes. The results indicate that purchase price negatively affects the EV adoption, though medium- and high-income customers exhibit a significant willingness to pay for both EVs and higher-end petrol vehicles (PV). Moreover, extended waiting times for license plates and longer recharging durations significantly reduce the market shares of PV and hybrid vehicles (HV). These findings offer valuable insights for researchers and provide evidence-based guidance for policymakers and industry stakeholders aiming to promote electric mobility. The findings of the study can provide insights for future research and can serve as a reference for policymakers and EV operators to implement appropriate policy incentives to promote electric mobility.]]></description>
      <pubDate>Thu, 26 Jun 2025 16:12:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2564245</guid>
    </item>
    <item>
      <title>Image Enhancement Technique Utilizing Yolo Model for Automatic Number Plate Recognition</title>
      <link>https://trid.trb.org/View/2551243</link>
      <description><![CDATA[The significant increase in Indonesian vehicle numbers has highlighted the importance of a robust ANPR system. Over the past few years, the number has increased by up to 4% each year and is expected to continue rising as long as economic growth continues. This study utilized the YOLOv9 model and EasyOCR along with image enhancement as a pipeline for the license plate recognition process. YOLOv9 was chosen for object detection because its architecture offers good stability in terms of performance and efficiency, even outperforming newer models with a 99.3% mAP@50 in the YOLOv9s variant. EasyOCR was used to recognize and extract characters from license plates. To enhance image quality, Real-ESRGAN upscales image resolution using a GAN architecture and addresses character blurring caused by low-resolution images. Additionally, CLAHE further enhances the clarity of low-contrast characters by employing a histogram to redistribute image intensities. The proposed method achieved an accuracy of 84.36% when tested on 100 image samples in real-world situations, indicating fairly good performance despite challenges like blurring and low contrast. The results highlight the potential of ANPR solutions in addressing the common challenges of real-time license plate recognition, contributing to more efficient traffic surveillance and enforcement systems, especially in Indonesia.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2551243</guid>
    </item>
    <item>
      <title>Applications of Automated Speed Measurement Equipment</title>
      <link>https://trid.trb.org/View/2548892</link>
      <description><![CDATA[Previous studies have shown that the presence of the highway patrol can reduce traffic speeds in work zones, but unless officers actually issue citations, the effect of presence alone is temporary. Sufficient numbers of officers are not available for continuous enforcement. The project’s technical panel selected Automated Speed Enforcement (ASE) equipment to be used in this study to photograph the rear of vehicles violating the speed limit, resulting in high-resolution photographs revealing the vehicles’ license plate number. A vehicle-mounted ASE system was leased from Traffipax, Inc. for one month during the summer of 2002. It was placed in work zone and school zone environments. The objective for this system is to issue a citation to every speeder. In a short time, it becomes an effective deterrent to motorists who repeatedly travel through that work zone without the need for a law enforcement officer to be present. The Traffipax system performed reliably when the batteries were kept charged. It was designed to be operated as a manned system, rather than operated unmonitored for a day at a time as it was envisioned by the panel. The system compared favorably with a Lidar speed measurement unit, and never took a photograph of a vehicle not exceeding the set threshold. Photographs gave clear presentations of the license plates. When configured to take photographs of the rear of the vehicle, identification of the driver is impossible. Recommendations were as follows: 1. Seek legislation using model language; 2. Narrow scope to work zones and school zones rather than statewide; 3. Emphasize reliability, accuracy and effectiveness of available equipment and operating ASE programs; 4. Incorporate ASE vehicle into DOTCOP program; and 5. Establish program to share ASE with local jurisdictions during off-peak times.]]></description>
      <pubDate>Tue, 10 Jun 2025 11:57:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548892</guid>
    </item>
    <item>
      <title>Toward Reliable License Plate Detection in Varied Contexts: Overcoming the Issue of Undersized Plate Annotations</title>
      <link>https://trid.trb.org/View/2449284</link>
      <description><![CDATA[License plate detection and recognition (LPDR) is of paramount importance in the Intelligent Transportation Systems. Most existing license plate (LP) detectors rely on anchors, rendering them vulnerable to multi-scale LPs, especially those of smaller scales, which limited the overall performance of LPDR. Another issue prevalent in LP datasets arises from the inherent ambiguity in manual labeling standards. Owing to this uncertainty, certain small-scale LPs that are distinctly detectable often suffer from annotation omissions. The presence of such noisy data has a detrimental impact on the training of LP detectors. In this paper, the authors propose ALPD, an anchor-free LP detector along with three key designs, namely the Multi-To-One scale-fusion block (MTO) for cross-scale feature integration, the Multi-Domain Feature Simulation (MDFS) for narrowing the disparities across multiple domains even the unseen ones, and the decoupled heads for better optimizing classification and regression tasks. Besides, ALPD incorporates a semi-supervised training framework using an abstention strategy known as arbitration, wherein a Teacher model and a Student model are trained collaboratively, enabling the supplementation of missing annotations for small license plates. Furthermore, it possesses immunity to model performance degradation when fed with massive quantities of unlabeled or even mislabeled data. The arbitration method along with a penalty factor can effectively guarantee the pseudo-label quality and balance complexity between the Teacher and Student tasks, thus preventing the Student from being constrained by ambiguous pseudo-labels. ALPD outperforms previous state-of-the-art methods on two widely recognized benchmarks and exhibits its robustness and generalizability on the All-round CCPD dataset.]]></description>
      <pubDate>Mon, 17 Mar 2025 09:18:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449284</guid>
    </item>
    <item>
      <title>Predicting the Traffic Volume of Expressway Sections Based on OD Investigation</title>
      <link>https://trid.trb.org/View/2475415</link>
      <description><![CDATA[The traffic volume of highway sections is the key to carrying out highway network renovation and expansion planning, maintenance engineering scheme design, and investment benefit analysis. At present, the cross-sectional traffic volume is mainly collected through observation stations. Due to the limited coverage of traffic volume observation stations, it is often difficult to obtain traffic volume information for the required sections of the project, which affects the accuracy of later analysis results. This paper proposes a method for predicting traffic volume at any section along a highway using vehicle registration information at the entrance and exit of the highway. Firstly, use data mining tools to obtain the OD investigation probability matrix of traffic volume along the highway. Secondly, based on the grey theory model, predict the unknown traffic volume of the section to be tested, and combine the unknown traffic volume prediction results to calculate the traffic volume data of any entrance, exit, and route. Finally, calculate the traffic volume of any section along the project.]]></description>
      <pubDate>Thu, 12 Dec 2024 16:57:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2475415</guid>
    </item>
    <item>
      <title>An Efficient Approach for Slant Correction of Vehicle Licenses Based on Hough Transform and Mathematics Morphology</title>
      <link>https://trid.trb.org/View/2283003</link>
      <description><![CDATA[In a real Vehicle License Plate Recognition System, the license images obtained by vidicon are usually slantwise. The slant of vehicle licenses will do harm to the Character Segment and Recognition. The paper advances a new method combining Hough Transform and Mathematics Morphology by the analysis of the vehicle licenses' slant pattern and the interference characteristics. Compared with the conventional methods, it overcomes the perplexity that too many disturbed lines and imperfect detection criterions. The experimental results show that the proposed method can improve the accuracy of the slant correction. It is confirmed that the noise immunity of the method is excellent, and the performance is robust. The correction rate of the newly developed algorithm has reached over 95%.]]></description>
      <pubDate>Thu, 26 Sep 2024 09:02:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2283003</guid>
    </item>
    <item>
      <title>Data-Driven Compensation Models for Enhanced Efficiency and Service Quality at License Plate Agencies</title>
      <link>https://trid.trb.org/View/2414051</link>
      <description><![CDATA[In the state of North Carolina, a network of 128 License Plate Agencies (LPAs) are vital community hubs for vehicle titling and registration services, serving as critical points of communication between citizens and the Department of Motor Vehicles (DMV). These LPAs offer diverse services ranging from title work and registration to wildlife services and commercial vehicle in different parts of North Carolina and are indispensable due to NCDMV's continued use of paper titles.  However, the efficient operation of these LPAs while maintaining exceptional customer service has been a challenge. The current compensation model, governed by NCGS 20-63(h), rewards LPAs with payments per transaction, inadvertently incentivizing faster transactions over quality of service.
The overarching goal of this study is to enhance customer service quality and optimize transactional efficiency within LPAs by designing innovative tiered compensation models leveraging data-driven approaches. This research goal is driven by three core needs: enhancing customer service, ensuring the financial sustainability of LPAs, and aligning with the evolving technological landscape of digital services while preserving the crucial role of LPAs in our communities. This goal will be achieved through five tasks: (a) reviewing compensation models, (b) assessing stakeholder needs through data collection and conducting data-driven analyses of current LPA operations, (c) developing innovative compensation models with contract terms and incentives and performing detailed cost-benefit analysis, (d) conducting case studies and feedback for validation, and (d) facilitating technology transfer. 
The project's impact is threefold: first, it aims to reshape LPAs' service delivery approach, prioritizing customer satisfaction and financial sustainability. Second, it integrates insights from diverse fields, including compensation models, queuing theory, and multi-stakeholder analysis, into designing tier-specific compensation models. Lastly, the project is immediately relevant, given the rising customer complaints, reduced compensations, and the shift towards online services. The project is highly significant and time-critical for the state, as it will provide insights for improving LPA functions through stakeholder analysis and data-driven approaches. This, in turn, will inform future contracts and policy decisions, potentially serving as a model for other states. The anticipated research products include comprehensive reports, documentation of proposed compensation models, stakeholder insights, guidelines on benefit and cost evaluation of proposed models, and knowledge dissemination materials, all geared toward improving LPAs' operations and customer service. These products along with preliminary implementation plan will enable the North Carolina Department of Transportation (NCDOT) and NCDMV to ensure financial sustainability for LPAs, adapt to evolving technological landscape, and ultimately deliver great customer service (NCDOT’s Goal #2)
]]></description>
      <pubDate>Thu, 08 Aug 2024 10:57:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2414051</guid>
    </item>
    <item>
      <title>The effect of color on license plate recall</title>
      <link>https://trid.trb.org/View/2362210</link>
      <description><![CDATA[Previous research has shown there are particular patterns of license plate designs that are easier to recall. Missouri license plate patterns (AB1-C2D) somewhat diverge from what research suggests works best for recall. The current study examined whether incorporating color into license plates would improve recall, and also whether awareness or explanation of license plate formats would affect recall accuracy. Across two experiments, participants viewed license plate stimuli with and without color and attempted to recall them. The hypothesis was that incorporating color would improve recall, but the hypothesis was not supported. Results also did not show that prior exposure or explanation of formats affected accuracy. Future research should explore additional ways to improve license plate designs that would be easy to implement. Such improvements to license plate design would be useful because efforts to improve the public's awareness of formats would be expensive and likely ineffective.]]></description>
      <pubDate>Mon, 13 May 2024 16:33:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2362210</guid>
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