Highway Agencies Research Articles

An Automated Sound Barrier Inventory Method Using Mobile LiDAR

A sound barrier, also called a noise barrier, plays an irreplaceable role in traffic noise abatement. The Federal Highway Administration’s (FHWA) highway noise regulation requires each state highway agency to maintain a complete inventory of all constructed noise-abatement features. Although key information for most of the newly constructed sound barriers has been inventoried, public transportation agencies are still struggling to keep close track of the in-service barriers because their inventory information is nonexisting, and manual inventory remains time-consuming, labor-intensive, and often dangerous. Therefore, many agencies have shown more interest in exploring the possibility of using light detection and ranging (LiDAR) data for assisting in sound barrier inventory, thanks to the widely available data set and much-improved data quality. This study proposes a LiDAR-based sound barrier inventory method to automatically extract the sound barrier’s location and measure the corresponding geometry. The extraction of a sound barrier is achieved using its unique features after random sample consensus (RANSAC)-based ground extraction and region-growing segmentation. The geometry measurement of the sound barrier is performed by analyzing the detailed dimension of the extracted point cloud, including location, height, length, and lateral offset. The experimental test conducted near Carver, Massachusetts showed the results with a precision rate of 99.9% and a recall rate of 93.8%. Moreover, the outcome of the experimental test has demonstrated the robustness of the proposed method in different complexities of the background and sound barrier types (linear and zigzag). This study has demonstrated the feasibility of using LiDAR for effectively inventorying in-service barriers. Besides the critical application of asset management, the detailed location and geometry information provided by the proposed method can provide valuable insight for other critical applications, such as traffic noise modeling.

Improving Data Quality of Automated Pavement Condition Data Collection: Summary of State of the Practices of Transportation Agencies and Views of Professionals

Automated or semi-automated pavement condition data collection is replacing manual data collection in many state and local highway agencies due to its advantages of reducing labor, time, and cost. However, the practical experience of highway agencies indicates that there are still data quality issues with the pavement condition data collected using existing image and sensor-based data collection technologies. This study aims to investigate the implementation experiences and issues of automated or semi-automated pavement condition surveys. An online questionnaire survey was conducted, along with scheduled virtual/phone interviews to gather information from government, industry, and academia about the state of the practice and state of the art. Open questions about the data quality and quality control & quality assurance (QC/QA) were used to receive first-hand inputs from highway agencies and pavement experts. The study has compiled the following observations: (1) Highway agencies urgently need a uniform data collection protocol for automated data collection; (2) the current QA requires too much human intervention; (3) cost ($100–$200 per mile) is a significant burden for state and local agencies; (4) the main issues regarding data quality are data inconsistencies and discrepancies; (5) agencies expect a greater accuracy once the image processing algorithms are improved using artificial intelligence technologies; and (6) existing automated data collection methods are not available for project-level data collection.

Modeling retroreflectivity degradation of traffic signs using artificial neural networks

Traffic signs are vital for communicating guidance, rules, warning, and other highway agency information for safe and efficient navigation through transportation networks. Signs must be clearly detectable, readable, and understandable to fulfill their intended purpose. Poor sign visibility, particularly during nighttime, is the leading cause of fatalities worldwide. Sign retroreflectivity is one of the key measures to evaluate sign visibility conditions. It gradually deteriorates over time with sign aging, exposure, and other environmental conditions necessitating periodic sign maintenance or, ultimately, replacement when the sign retro values fall below the minimum prescribed standards. In literature, studies have mostly used traditional statistical regression models to model sign retroreflectivity as a function of available explanatory variables. Further, these studies have proposed separate retro degradation prediction models for different sign sheeting grades and colors that limit their applications for other scenarios. To fill the research gap, this study compared the performance of the linear regression method with three different architecture of the neural network namely, Feed-Forward Neural Network (FFNN), Cascade Forward Neuran Network (CFNN), and Elman neural networkNeural Network (ELMNN) for signs retro prediction with an aim to optimize sign maintenance and replacement activities and to enhance road safety. All the Neural Network models were employed with varying combinations of training algorithms, activation functions, and model parameters. Sign retro data for 539 in-service signs along selected sections of two expressways (M-1 and M-2) near the capital city of Islamabad in Pakistan were collected through portable handheld retroreflectometer GR3. Data on other sign attributes like sign ages (0, 2, 5, and 10 years), sign orientation, observation angle, sign sheeting brand, grade, and color were also acquired. Feature-based sensitivity analysis was conducted to identify the relative importance and ranking of input predictor variables. Model prediction results expressed in terms of various statistical evaluation metrics root mean square error (RMSE), mean absolute percent error (MAPE), RMSE-observation standard deviation satio (RSR), coefficient of determination (R2), Willmott's index of agreement (WIA), Nash-Sutcliffe efficiency (NSEC), and percent bias (PBIAS) showed that all the NN models outperformed the regression technique. Comparing the NN models, ELMNN architecture with 21 neurons in the hidden layer for ‘tansig’ activation function and ‘trainlm’ training algorithm yielded better retro-prediction performance. Feature sensitivity analysis revealed that variables sign age, sheeting brand, color, and observation angle were the most sensitive variables in predicting the retro output. Findings of this study can guide the transport agencies and decision-makers for effective policy implications and sign management practices.

Insights into Increasing the Implementation of Concrete Overlay Rehabilitation Methods for Highway Pavements

Concrete overlays provide a promising solution to extending pavement life, and are currently the focus of the Federal Highway Administration Every Day Counts initiative. Despite their potential benefits, concrete overlays are not commonly used in many areas of the U.S.A., including the Southeast. A survey of 19 paving contractors active in the Southeastern U.S.A. was performed to quantify recent concrete overlay projects performed by these contractors, to assess the capabilities of contractors to construct concrete overlays in this region, and to identify barriers to implementation of concrete overlays as well as opportunities to increase their use. Findings indicated contractors in the region either had extensive overlay experience or no experience constructing concrete overlays. Regardless of the experience level of the contractor, the primary barrier to implementation is that this type of work is not being let for bid. Concerns about traffic control and safety was the second most important barrier among all contractors. Experienced contractors indicated the lack of available and skilled construction personnel were also key barriers. Inexperienced contractors cited concerns about the length and duration of construction as the third most important barrier. Respondents indicated an eagerness to bid on and complete overlays. The results of this study provide insight to regional state highway agencies, municipalities, and design engineers of the capabilities of concrete pavement contractors in the region to complete concrete overlay projects. The findings will help agencies understand the capabilities and concerns of concrete paving contractors and aid in informed decisions about overlays.

Incorporation of National Pavement Performance Measures Into Decision-Making Process

State highway agencies (SHAs) are required by Federal Highway Administration (FHWA) to develop Transportation Asset Management Plans (TAMP) in which the performance targets for roadway assets are determined. FHWA also developed national performance metrics to use in determining performance targets for pavements. However, for pavement assets, most SHAs employ their own performance metrics for pavement condition data reporting and performing maintenance and rehabilitation (M&R) analysis. Therefore, there is a need to incorporate national performance measures into SHAs’ pavement management system to support the development of TAMP. In this study, new pavement performance metrics were proposed to assist SHAs in conducting M&R analysis. The relationships between the new metrics and existing pavement metrics used in the State of Tennessee were investigated. Comparison between the new metrics and the State’s metrics was made with reference to network-level condition summary and performance prediction models. Results indicated that the new performance metrics appeared to be stable and less sensitive to sectioning method. Similar time-series patterns of network-level performance deterioration were found between the State’s metrics and the new metrics. It is recommended that performance models be established based on the new metrics, as the deterioration rates from the two metrics were not well correlated.

Evaluating Crack Identification Performance of 3D Pavement Imaging Systems Using Portable High-Resolution 3D Scanning

With the increasing adoption of three-dimensional (3D) pavement imaging systems by highway agencies for automated pavement condition assessment, coupled with the advent of diverse systems from different manufacturers, there is a need for standard procedures for the verification and certification of systems’ performance in regard to distress identification, especially for cracking that is a key contributor for triggering maintenance and rehabilitation activities. Although some procedures were adopted by agencies for a rough verification of cracking identification accuracy using ground reference established subjectively by trained raters, a more rigorous and objective method is needed to match the continuous advancement in the systems’ capabilities and data quality requirements. As portable high-resolution 3D scanning technologies have become commercially available, there is an opportunity to leverage them for establishing a more trustable ground reference for the data quality evaluation. This paper proposes a methodology that uses high-resolution 3D scanners to establish the ground reference for field pavement cracking distress to evaluate the crack identification capability of 3D pavement imaging systems in regard to crack quantity, position, and width. A case study was performed by scanning sample pavement cracking spots using “FARO Arm Quantum S” scanner to collect ground reference images and a 3D pavement imaging system installed on the “Georgia Tech Sensing Van” to collect test images to validate the feasibility of the proposed methodology.

Investigation Into Collection Variability of Surface Crack Data for Network-Level Asphalt Pavement Evaluation

Surface cracking is a major type of pavement distress that is of interest to pavement engineers. Cracks are generally categorized in relation to patterns, orientations, and locations. Each type of crack is associated with one or more failure modes of pavements. Accurately detecting and rating surface cracks is crucial in pavement condition surveying. Currently, many State Highway Agencies employ automated survey methods to collect pavement condition data at network level. As network-level pavement evaluation is based on a single run, the traditional way of characterizing data variation based on multiple runs is no longer valid. Therefore, it is necessary to introduce a new method to evaluate the variability of pavement condition data at the network level. In this study, the variability of network-level surface crack data was evaluated by means of network-level sample parallel tests. The parallel test was conducted from 2018 to 2020 using two vehicles equipped with identical automated survey systems which consisted of a 3-D imaging system and automated distress identification system. A matrix-based method was proposed to evaluate the variations of crack data obtained from two testing vehicles. Crack data investigated in this study included fatigue cracks, longitudinal wheel-path and non-wheel-path cracks, and transverse cracks. Results indicated that change of testing speed could potentially influence the variation of automated crack data. The variations between severity levels for fatigue cracking were higher than other types of cracks. The variation of crack data decreased with the increase of reporting intervals.

Sustainable pavement maintenance and rehabilitation planning using the marine predator optimization algorithm

The sustainability of pavement, especially in Maintenance and Rehabilitation (M&R) scheduling, has become an immense concern and has received limited attention in previous studies. Therefore, this study aimed to develop the M&R scheduling optimization based on sustainability. To this end, a novel sustainability index was introduced, in which all the sustainable development aspects were considered, including highway agency cost, environmental impacts, and social effects. A conventional model was used to assess the sustainable model’s effectiveness. Two new constraints are introduced to reduce the budget fluctuation and not to apply the M&R treatments for two consecutive years to make the model practical. On the other hand, highway agencies face large-scale networks, in which the optimization of M&R scheduling has computational complexities. Thus, the novel and powerful metaheuristic algorithm, named Marine Predator Algorithm (MPA), was applied to solve the pavement M&R scheduling problem. A large-scale pavement network, including 110 sections, was analyzed over a 5-year plan as the case study. The results indicated that using the sustainable model rather than the conventional one leads to a 6.5% reduction in CO2 emission. Besides, utilizing the sustainable approach enhances the equity and safety indices by 40.7% and 2.5% compared to the conventional treatment. However, the highway agency cost is increased by 1.1% using the sustainable model.

Conceptual estimation of construction duration and cost of public highway projects

State Highway Agencies (SHAs) and Departments of Transportation (DOTs) allocate their limited resources to thousands of competing projects in multi-year transportation programs using expert judgement for the expected construction costs and durations. Such estimates overlook influencing parameters known in the planning phase and the importance of building reliable databases to support decision making. Meanwhile, it is possible to generate meaningful predictions in early stages of project development based on historical data gathering and analysis. The present research introduces a newly developed method for conceptual cost and duration estimation for public highway projects utilizing an ensemble of machine learning (ML) models and data collected for projects completed between 2004 and 2015 (roads, bridges, and drainage projects). Unlike previous studies, the proposed method includes project parameters that affect construction durations and costs and were not studied simultaneously before. The parameters considered are facility type, project scope, highway type, length, width, location, level of technical complexity, and new parameters pertinent to payment and procurement methods. The developed method was tested using 29 and 56 randomly selected projects, and the results yielded a Mean Absolute Percentage Error (MAPE) of 7.4% and 4.5% for the duration and cost, respectively, which are lower than the estimation errors of methods reported in recent literature. Additionally, the generalization abilities were assessed by the Mann-Whitney test, and the developed method is found to successfully handle diverse projects. Thus, machine learning models can assist agencies in the review process of competing projects from a high-level management perspective to ultimately develop better management execution programs.

Surface layer modulus prediction of asphalt pavement based on LTPP database and machine learning for Mechanical-Empirical rehabilitation design applications

Evaluating the modulus of the existing asphalt concrete (AC) layer is a critical procedure in Mechanical-Empirical (ME) rehabilitation analysis. Generally, the modulus could be back-calculated by the Falling Weight Deflectometer (FWD) test. However, the raw FWD data of each pavement section is not always readily prepared for local highway agencies. To address this issue, the main objective of this study is to establish a reliable model by machine learning (ML) methods to predict AC layer modulus for the existing flexible pavement with data readily available from the local pavement management system, which could be an auxiliary tool for network-level sections with no FWD tests. The long-term pavement performance (LTPP) database was used to collect the original data for model training and testing, including pavement structures, service age, climate records, and pavement distresses. After preliminary data processing, matrix correlation analysis, and feature selection, the prepared dataset (total data points = 6477) with 14 predictors was fed into three regression models, including Ordinary Least-Squares regression (OLS), Random Forest regression (RF), and Gradient Boosting regression Method (GBM). The related key hyperparameters were optimized by grid search and 5-folds cross-validation. By comparison, the GBM model was finally selected due to its considerably higher prediction accuracy (R2 = 0.7921) than RF model (R2 = 0.7525) and OLS model (R2 = 0.4371) in the test set. According to the variable importance given by GBM model, surface temperature and AC layer thickness are more dominant variables in modulus estimation. In addition, a case study with predicted AC layer moduli in ME rehabilitation design was provided to verify the model application. In summary, the trained GBM model can be utilized to predict AC layer modulus for pavement evaluation and then ME rehabilitation when FWD data is not available.

Developing a Risk Assessment Model for a Highway Site during the Winter Season and Quantifying the Functional Loss in Terms of Traffic Reduction Caused by Winter Hazards Conditions

This paper introduces a methodology to quantify traffic change triggered by the combined effect of weather hazards based on winter weather hazards models. The winter weather hazards models for three vehicle types were developed with weigh-in-motion (WIM) data collected in the commuter highway in the cold Canadian region for 5 years. The developed model was utilized to simulate the variations of the percentage reduction for each vehicle type based on the 239 pairs of weather combinations composed of six cold categories and various amounts of snowfall. The first phase involved measuring the marginal effect of weather factors such as cold category (or temperature) on the percentage reduction in traffic volume. The second phase involved utilizing the same winter weather traffic model to quantify the effect of combined weather factors on the percentage reduction. The percentage reduction of the total traffic and passenger cars increased as temperature deteriorated and snowfall increased. Truck traffic decreased as snowfall increased, but interestingly, as temperature deteriorated, it was estimated that the truck traffic volume increased. This phenomenon assumed that truck traffic moves from low-maintenance to high-maintenance highways as the weather deteriorates. The methodology to quantify traffic volume changes can be adopted by highway agencies to determine the timing of the snowplow operation based on the risk assessed in terms of traffic volume reduction. It can also be used to predict the percentage reduction of traffic and then determine whether to open or close a highway.

Pavement Maintenance Program at the Network Level: Mixed-Integer Programming with Multiple Objectives

Pavement network conditions deteriorate over the years of use. To keep pavement conditions at acceptable levels, highway agencies plan pavement maintenance and rehabilitation (M&R) programs and perform accordingly. Highway agencies usually face budget variability for pavement M&R activities because of limited resources, economic conditions, and changes in policies. The situation makes it difficult for highway agencies to keep an acceptable pavement condition at the network level. Therefore, it is important for highway agencies to adopt M&R policies that can maximize the network condition as well as handle the deviation of the network condition considering the available maintenance funds. In this paper, a multi-period multi-objective linear integer programming model is proposed. Two objectives, maximization of the average network condition and minimization of deviation of the network condition from an idealized network condition trend, are considered in the formulation. The model is formulated for fixed M&R budgets, as well as for variable M&R budgets. The proposed model provides an M&R program for the pavement network that helps decision makers to manage pavement maintenance programs considering budgetary constraints. A case study examining a network of 45 pavement sections is conducted. The solutions of the fixed-budget and variable-budget model are presented. In addition, the values of the system to the decision maker are discussed. Results show that the proposed model is an attractive way to manage pavement maintenance programs at the network level.

Probabilistic deterioration modeling of bridge component condition with random effects

ABSTRACT Timely maintenance of bridge components is critical for bridge management functions. With reliable deterioration models, highway agencies can efficiently allocate funding for bridge maintenance and customize maintenance schedules to meet agency budgets. The increased public expectation of acceptable levels of service for bridges coupled with other competing needs makes it crucially important to accurately estimate bridge future conditions so that adequate resources may be allocated for repair and reconstruction purposes. Accurately predicting bridge condition is challenging due to the inherent random nature of factors impacting deterioration, the existence of unobserved variables that are not measured, panel nature of the data and the effects of bridge-specific correlation. Without accounting for these factors, the resulting estimated deterioration models may have biased and inconsistent parameter estimates. This article assembled a comprehensive set of bridge and climate data from the National Bridge Inventory (NBI) and the South Carolina Climatology office. Bridge component deterioration models for bridges on state highways in South Carolina were estimated using an ordered probit model with random effects specification to account for the randomness and panel nature of the bridge data. The study results are useful for various bridge management tasks including maintenance programming, budgeting and bridge asset evaluation.

Encouraging Sustainable Use of RAP Materials for Pavement Construction in Oman: A Review

The Sultanate of Oman has experienced rapid development over the last thirty years and has constructed environmentally friendly and sustainable infrastructure while it continues to find economical alternative resources to achieve the goals of the Oman 2040 vision. The primary concerns are preserving natural resources and reducing the impact of carbon dioxide (CO₂) emissions on the environment. This review aims to encourage the sustainable use of reclaimed asphalt pavement (RAP) materials in pavement construction and focuses primarily on employing RAP materials in new pavement projects. Currently, new construction projects utilise a significant percentage of demolished asphalt pavement to save costs and natural resources. The key issue that arises when mixing RAP into new asphalt mixtures is the effects on the mixtures’ resistance to permanent disfigurements, such as fatigue cracks, that influence asphalt mixture performance. Numerous studies have assessed the impact of using RAP in asphalt mixtures and found that RAP increases the stiffness of asphalt mixtures, and thus improves rutting resistance at high temperatures. Nevertheless, the findings for thermal and fatigue cracking were found to be contradictory. This review will address the primary concerns regarding the use of RAP in asphalt pavements, and aims to encourage highway agencies and academic researchers in the Gulf countries to develop frameworks for the practical usage of RAP in the construction of sustainable pavement systems.

Impact of Subgrade Strength on Pavement Performance

Based on the standard design of flexible pavement structure conducted in Turkey, only subbase course layer is affected by the variation of subgrade strength and other pavement layers (wearing course, binder course, asphalt concrete (AC) base course, and crushed stone base course) are not influenced by the variation of subgrade strength. Therefore, the aim of this study is to investigate the impact of subgrade strength on flexible pavement distresses. To achieve the objectives, firstly, three different flexible pavement structures designed using the Turkish Flexible Pavement Design Guide 2008 were chosen and secondly, the effect of variation of subgrade strength [5 to 50 California Breading Ratio (CBR)] on pavement performance (rutting and fatigue cracking) for three different traffic levels [5000, 10000, and 15000 average annual daily truck traffic (AADTT)] for climatic condition of Izmir region using AASHTOWare Pavement ME Design v2.5.5 were analyzed. The results of the study show that, reducing subgrade CBR values from 50 to 5 CBR resulted in an increase by 3.7mm in total rutting depth and 5.6% in alligator cracking magnitude. Also, results demonstrated that the influence of subgrade strength on rutting within asphalt concrete layers (AC rutting) is negligible. By contrast, increasing subgrade CBR value from 5 to 50 resulted in a significant increase (65.1 to 314.7 m/km) in longitudinal cracking magnitude while thermal (transverse) cracking is not sensitive to the variation of subgrade CBR values. This study can assist highway agencies and individual pavement designers in the design a more practical pavement.

Machine Learning Based Automated Left-Turn Vehicle Counts With Conventional Presence Mode Long-Loop Detectors: Alabama Case Studies

Inductive-loop detectors are still used in many intersections because highway agencies have insufficient budget to replace them with new technologies or when accurate traffic monitoring is not necessary. Particularly, long-loop stop-bar detectors have been widely used for left-turning vehicles and are operated under “Presence” mode, which focuses only on detecting the presence of vehicles for signal operations, not for counting vehicles. As a result, a significant discrepancy exists between the number of vehicle detections and the actual vehicle observations. The present study developed machine learning (ML)-based methods to overcome this discrepancy so that conventional long-loop detectors can also be used for estimating hourly left-turn volume. Several ML classifiers were adopted to predict the difference (i.e., detection versus observation) for every vehicle detection event. Three predictors (detector occupancy time, left-turn phases when the detector is on and off) from high-resolution data were used as input of the ML models. Results showed that all the models are statistically significant with p-value <0.05. The developed models were then applied to estimate hourly left-turn volume, by adding its prediction result to left-turn detection counts by the detector. The hourly left-turn volume estimated with the proposed method accounts for 96% of the actual left turns for low left-turn traffic conditions, and 93% for heavy left-turn traffic conditions. This indicates that the proposed method can provide a good estimation of hourly left-turn volume for signalized intersections operated with long-loop detectors. The method is simple and based on data from existing vehicle detection technologies already in typical signalized intersections.

Generalized link cost function and network design for dedicated truck platoon lanes to improve energy, pavement sustainability and traffic efficiency

Recent development on autonomous and connected trucks (ACT) has provided the freight industry with the new option of using truck platooning to improve their fuel efficiency, traffic throughput, and safety. However, closely spaced and longitudinally aligned trucks impose frequent and concentrated loading on pavements, which often accelerates pavement deterioration and increase the life-cycle costs for the highway agency. Also, effectiveness of truck platooning can only be maximized in dedicated lanes, and its benefits and costs need to be properly balanced between stakeholders. This paper proposes a network design model to optimize (i) placement of dedicated truck platoon lanes and toll price in a highway network, (ii) pooling and routing of ACT traffic from multiple origins and destinations to utilize these lanes, and (iii) configuration of truck platoons within these lanes (e.g., lateral displacements and vehicle separations). The problem is formulated as an integrated bi-level optimization model. The upper level makes decisions on converting existing highway lanes into dedicated platoon lanes, as well as setting user fees. The lower level decisions are made by independent shippers regarding the choice of routes, use of platoon lanes vs. regular lanes, and they collectively determine truck traffic in all lanes. Link cost functions for platoon lanes are obtained by simultaneously optimizing, through dynamic programming, pavement rehabilitation activities and platoon configuration in the pavement’s life cycle. A numerical case study is used to demonstrate the applicability and performance of the proposed model framework over the Illinois freeway system. It is shown that the freight traffic is effectively channelized on a few corridors of platoon lanes and, by setting proper user fees to cover pavement rehabilitation costs, system-wide improvements for both freight shippers and highway agencies can be achieved.

Impacts of future climate change on flexible road pavement economics: A life cycle costs analysis of 24 case studies across the United States

• A methodological framework to analyze the impacts of climate change on pavement LCC is presented • Pavement LCC are estimated to increase 0.08-0.21% per °C increase in future temperature • Pavement LCC were found to be particularly sensitive to gasoline and diesel unit costs Highway agencies are facing pressure for repairing and replacing underfunded and aged road pavement networks that were initially designed for historical climatic conditions. Road pavements must be updated while accounting for the impacts of climate change, which are likely to be exacerbated in the years to come, and with limited budgets. Methodologies and frameworks that help agencies incorporate the long-term effects of climate change on pavement performance and make informed decisions on how to spend their limited funds are therefore of utmost importance. This paper presents a methodological framework that combines downscaled climate projections, pavement performance predictions using the AASHTOWare Pavement ME Design TM tool, maintenance and rehabilitation strategies, and life cycle costs analysis (LCCA) in a comprehensive system. The proposed methodological framework was adopted in the LCCA of 24 case studies across the contiguous United States under four alternative periods corresponding to simulated climate projections for four 20-year periods (1981-2000, 2001-2020, 2041-2060, and 2081-2100) with a higher Representative Concentration Pathway (RCP8.5). The case study results show that climate change per degree Celsius will lead to approximately $650-700 million/year additional agency costs in the U.S. In addition, climate change will have greater impacts on the costs incurred during the maintenance and end-of-life phases.

Implementation of a Real-Time Vision Based Vehicle License Plate Recognition System

Abstract: Automatic Number-plate recognition is a technology that uses visual cues in images to read vehicle registration plates to create vehicle location data. It can use closed television, traffic law cameras, or cameras designed for work. ANPR is used by police around the world for legal purposes, including checking whether a vehicle is registered or licensed. It is also used for the collection of electronic toll on individual toll roads and as a catalyst for traffic mobility, for example by highway agencies. The automatic number plate recognition can be used to store camera-captured images and text from the license plate, another configurable to save the driver's image. Systems usually use infrared light to allow the camera to take pictures at any time of the day or night. An Intelligent Vehicle Safety recording system that uses image processing usually requires a large internal memory register or DDR SDRAM (Random Double Data Access) for temporary video and photos. But these processes often increase the complexity of hardware and software. In this program, we propose a VLSI implementation for a photographic route departure system. It is not only a calculation of line melting and discrimination outside the line, but also a real-time analysis of car tires on the left and right side of the track marker with a video image, and then judge the car offsets phenomenon. Keywords: ANPR, Artificial Intelligence, Government Tracking, Security etc.

Establishment of Performance Evaluation Model of Highway Environmental Protection Based on Deep Learning

As the foundation of the social economic system, public transportation roads play an important role in the development of the national economy. At the same time, the opposition between public transportation roads and the natural environment, ecological environment, and natural resources is becoming more and more obvious. This paper is aimed at studying the performance evaluation model of highway environmental protection based on deep learning. For this reason, related methods such as environmental protection, performance evaluation, and deep learning algorithms are proposed in the article, and corresponding experiments are also carried out in the article. The experimental results show that the highway environmental protection performance evaluation model based on deep learning can quickly help environmental supervisors to inspect the work of relevant highway environmental protection agencies and also accurately mention the environmental protection performance detection to 80%.