Different Types Of Vehicles Research Articles

Speed characteristics of heterogeneous traffic on inter-urban roads in Indonesia

Inter-urban roads in Indonesia accommodate heterogeneous traffic with mixed types of vehicles sharing the same lanes. The heterogeneity results in varied average speeds and speed deviations of different vehicle categories. Lack of posted speed limits, misplaced speed limit posts, and the vehicle speed and acceleration performances are among the possible factors leading to a wider spectrum of speed variations. Previous studies have been conducted to investigate speed characteristics, but few studies concentrate on the effects of heterogeneity. The present study analyzes the characteristics of traffic heterogeneity which is captured in six different types of vehicles, i.e. passenger cars, angkots, pickups, buses, trucks and motorcycles. Analysis was conducted on a set of six-year data of speed and speed deviations from 8 provinces. The average speeds and speed variations of different types of vehicles were analyzed for provinces with speed closeness created based on a Post hoc test. A pairwise multiple comparison was carried out to test the difference between each pair of means using R-E-G-W Q (Ryan-Einot-Gabriel-Welsch) Range test, which is a multiple stepdown procedure for One-Way Anova. Subset 1 consists of average speeds of NTT, NTB, South Sulawesi and Central Sulawesi. Subset 2 groups Central Sulawesi, South Sulawesi and South Kalimantan, subset 3 group consists of Bali and South East Sulawesi, and subset 4 groups South East Sulawesi and West Kalimantan. Within-group and between-group analyses were conducted on speeds and speed variations, and geographical comparisons by vehicle categories were made to a reference province. The results of this study can contribute to traffic policing, setting up the speed limits and traffic safety improvement programs.

Ways of comparing the advantages of operating different types of vehicles in the fleet

Reliability is one of the key features of road vehicle operation. Of course, the operational costs of a vehicle or vehicle fleets are closely related to reliability. If the fleet operator, in this case operator of limestone quarry fleet, operates with older vehicle fleet and gradually renews fleet, situation occurs, where it is possible to assess the advantageousness of fleet renewal not only in terms of reliability, but also the operational costs. Thus, it is possible the retrospective assessment of the correct decisions in tenders.

An Optimized Approach to Vehicle-Type Classification Using a Convolutional Neural Network

Vehicle type classification is considered a central part of an intelligent traffic system. In recent years, deep learning had a vital role in object detection in many computer vision tasks. To learn high-level deep features and semantics, deep learning offers powerful tools to address problems in traditional architectures of handcrafted feature-extraction techniques. Unlike other algorithms using handcrated visual features, convolutional neural network is able to automatically learn good features of vehicle type classification. This study develops an optimized automatic surveillance and auditing system to detect and classify vehicles of different categories. Transfer learning is used to quickly learn the features by recording a small number of training images from vehicle frontal view images. The proposed system employs extensive data-augmentation techniques for effective training while avoiding the problem of data shortage. In order to capture rich and discriminative information of vehicles, the convolutional neural network is fine-tuned for the classification of vehicle types using the augmented data. The network extracts the feature maps from the entire dataset and generates a label for each object (vehicle) in an image, which can help in vehicle-type detection and classification. Experimental results on a public dataset and our own dataset demonstrated that the proposed method is quite effective in detection and classification of different types of vehicles. The experimental results show that the proposed model achieves 96.04% accuracy on vehicle type classification.

An Empirical Analysis of Deep Learning Architectures for Vehicle Make and Model Recognition

Vehicle make and model recognition is an important component of the Intelligent Transport System (ITS), which plays an essential role in the vehicle surveillance and traffic monitoring. In this paper, we evaluated the performance of the recent deep neural networks for vehicle recognition to identify 196 different types of vehicles based on their make, model, and year using Stanford Cars data. Transfer learning has been employed to reduce the training time and also added a dropout of 0.5 at the last dense layer before the output layer for improved generalization performance on an applied dataset. Different augmentation methods such as random erasing, horizontal flip, random crop, resize, rotate, shear, and sharpen were applied to the training dataset to increase the number of images. Besides, optimization methods namely mixup and cyclical learning rate were also investigated to train these deep neural networks to avoid overfitting. Finally, the proposed ensemble learning framework using k-fold cross-validation has resulted in the improved accuracy of image classification for the vehicle make and model recognition. The final achieved accuracy is 93.96% through ensembling homogeneous models of DenseNet201, which is higher than previous state-of-the-art approaches for the vehicle make and model recognition. Simultaneously, it has also improved the performance of other models where MobileNetV2 has shown the highest improvement with an increase of 2.8% in the overall accuracy using the proposed ensemble learning technique.

An ils-based heuristic applied to the car renter salesman problem

The present paper tackles the Car Renter Salesman Problem (CaRS), which is a Traveling Salesman Problem variant. In CaRS, the goal is to travel through a set of cities using rented vehicles at minimum cost. The main aim of the current problem is to establish an optimal route using rented vehicles of different types to each trip. Since CaRS is N P-Hard, we herein present a heuristic approach to tackle it. The approach is based on a Multi-Start Iterated Local Search metaheuristic, where the local search step is based on the Random Variable Neighborhood Descent methodology. An Integer Linear Programming Formulation based on a Quadratic Formulation from literature is also proposed in the current study. Computational results for the proposed heuristic method in euclidean instances outperform current state-of-the-art results. The proposed formulation also has stronger bounds and relaxation when compared to others from literature.

Analysis of the intersection throughput at changes in the traffic flow structure

The paper addresses the throughput of an urban signalized intersection when the traffic flow structure varies widely. In the course of the study, data were collected from a street CCTV camera tracking traffic flows at a single intersection. At the preliminary step during the analysis of the dynamics of vehicles passing the intersection when leaving the queue that formed in front of the red light, it was found that it significantly changes for different types of vehicles. Therefore, the next step was to develop a statistically significant mathematical model of the intersection throughput as a multiple regression equation including all vehicle types under consideration. This model gave us various predictive estimates with the traffic flow structure varied. To obtain a visual representation of the results, we tested a model of category-based traffic flow at the intersection, built on the basis of fuzzy logic methods. This model allowed us to estimate the intersection throughput depending on some uncertain traffic flow factors in a graphic 3D view. The study was oriented towards the development of an algorithm to analyze the time required for a heterogeneous queue of vehicles to pass the intersection in real time. The algorithm can be used in intelligent transportation systems, in particular, in problems related to uninterrupted traffic of unmanned vehicles. The models considered may serve as a good basis for justified decisions on improving the vehicle-to-infrastructure communication (V2I).

FBG‐Based Sensing for Structural Health Monitoring of Road Infrastructure

Public road infrastructure is developed all around the world. To save resources, ensure public safety, and provide longer‐lasting road infrastructure, structural health monitoring (SHM) applications for roads have to be researched and developed. Asphalt is one of the largest used surface materials for the road building industry. This material also provides relatively easy fiber optical sensor technology installment, which can be effectively used for SHM applications—road infrastructure monitoring as well as for resource optimization when road building or their repairs are planned. This article focuses on the research of the fiber Bragg grating (FBG) optical temperature and strain sensor applications in road SHM, which is part of the greater interdisciplinary research project started at the Riga Technical University in the year 2017. Experimental work described in this article was realized in one of the largest Latvian road sites where the FBG strain and temperature sensors were installed into asphalt pavement, and experiments were carried out in two main scenarios. Firstly, in a controlled environment with a calibrated falling weight deflectometer (FWD) to test the installed FBG sensors. Secondly, by evaluating the real‐time traffic impact on the measured strain and temperature, where different types of vehicles passed the asphalt span in which the sensors were embedded. The findings in this research illustrate that by gathering and combining data from calibrated FWD measurements, measurements from embedded FBG optical sensors which were providing the essential information of how the pavement structure could sustain the load and information about the traffic intensity on the specific road section, and the structural life of the pavement can be evaluated and predicted. Thus, it enables the optimal pavement future design for necessary requirements and constraints as well as efficient use, maintenance, and timely repairs of the public roads, directly contributing to the overall safety of our transportation system.

Evaluation of driver behavior and safety at urban mid-block sections under mixed traffic conditions

Driver behavior on road sections is one of the most difficult phenomena to study, particularly in mixed traffic situations on urban roads, especially on the midblock sections. In this context, video data was collected from an access-controlled mid-block road sections. The main aims of this study are to establish vehicular trajectory data and to analyse driver lane changing and vehicle following behavior on the selected mid-block sections while taking into account relative velocities and relative distances between different types of vehicles. In traffic safety assessments, the time to collision is an effective time-based safety measure for detecting rear-ends collisions. Hence, the safe distance between successive vehicles was estimated. The study revealed that vehicles in the mixed stream, in general and in particular, Bikes and Autos particularly move substantially in the lateral direction. The safe distance between vehicles has been found to be dependent on traffic flow and roadway width.

Pricing and Investment Decision Issues of an Automobile Manufacturer for Different Types of Vehicles

With the raise of consumers’ environmental awareness, automobile manufacturers try to add production lines of new energy vehicles. This paper deals with pricing and investment decision issues of an automobile manufacturer for different types of cars. The consumers’ purchase preference for the new energy vehicle is formulated as a function of the manufacturer’s investment on facility and advertisement. Aiming to maximize the total profit of the manufacturer, we construct a decision model. By solving a differential equation, a necessary condition is proposed for guaranteeing that the manufacturer produces and sells both of the two types of vehicles. It is shown that both the consumers’ preference and the carbon tax affect the decisions of the manufacturer. For the possible scenarios in which the manufacturer only sells one kind of product, we present two simplified models. By comparing different achieved solutions, the optimal decision strategy is achieved. Finally, we show a numerical illustration to examine different decisions of the manufacturer under different sensitivity coefficients with regard to the investment. The main contribution of this paper is providing a jointly pricing and investment decision model under changeable consumers’ purchase preference, and revealing the influence factors of the automobile manufacturer’s transformation.

A discourse with architecture in the works of Krzysztof Wodiczko

The works of Krzysztof Wodiczko can be divided into three groups. The first one consists of different types of vehicles. The second group of Wodiczko’s works, according to Stach Szablowski, are designs of technological prostheses for socially > people - refugees, migrants, the homeless. The third and most well-known group are public projections, which consist in projecting images onto different famous buildings (first static images and, since 1996, moving ones, and images combined with sound), aimed at attracting the audience’s attention to cultural, social and political issues important to the dwellers of the given place. In the article, the author focuses on describing the last of the mentioned groups, pointing out that the artist's projections on the one hand relate to real, current problems of the dwellers of a given place and, on the other hand, as they are created with the thought of specific buildings in mind, they enter a discourse with architecture which is commonly perceived as universal, timeless and far from what is happening now.

Modeling the costs of project transportation maintenance

This study investigates variants in transportation maintenance of projects. The study subject is the mathematical notation of the dependence of costs incurred during the transportation maintenance of a project on the characteristics of maintenance options. Most projects involve a transportation component, which relates to the need to deliver raw materials, materials, equipment, manpower to the location of the product project (for example, construction projects, infrastructure projects). This study aims to identify variants in the transportation maintenance of projects, to establish mathematical dependences of transportation costs depending on the characteristics of vehicles and the conditions for their use in projects. The characteristics of the vehicles and the conditions of their operation in the projects form the essence of a specific variant of the transportation maintenance of a project. Each alternative transportation maintenance option affects the project, first of all, at the cost level. Therefore, the optimal transportation maintenance variant for a project should be selected considering the impact of options on project costs. In this study, the basis for modeling the cost of a project’s transportation maintenance is the project’s network schedule and an analysis of its activities in terms of the need for transportation maintenance. Mathematical expressions for the volume of transportation operations and the required number of vehicles of different types have been consistently derived. Cost dependences for three variants of vehicle utilization (rent, service, acquisition) have been derived, based on vehicle characteristics, for three levels of project consideration: certain operations, a certain time span, the project in general. The resulting formalization of transportation maintenance costs makes it possible to vary the characteristics of vehicles and the conditions for their use in a project during the decision-making phase of choosing the transportation maintenance option for a project, or their combination. The reported results underlie further development of the model for optimizing parameters and choosing the transportation maintenance variant of projects

Risk Analysis of Road Tunnels: A Computational Fluid Dynamic Model for Assessing the Effects of Natural Ventilation

We have developed an appropriate Computational Fluid Dynamics (CFD) model for assessing the exposure to risk of tunnel users during their evacuation process in the event of fire. The effects on escaping users, which can be caused by fire from different types of vehicles located in various longitudinal positions within a one-way tunnel with natural ventilation only and length less than 1 km are shown. Simulated fires, in terms of maximum Heat Release Rate (HRR) are: 8, 30, 50, and 100 MW for two cars, a bus, and two types of Heavy Goods Vehicles (HGVs), respectively. With reference to environmental conditions (i.e., temperatures, radiant heat fluxes, visibility distances, and CO and CO2 concentrations) along the evacuation path, the results prove that these are always within the limits acceptable for user safety. The exposure to toxic gases and heat also confirms that the tunnel users can safely evacuate. The evacuation time was found to be higher when fire was related to the bus, which is due to a major pre-movement time required for leaving the vehicle. The findings show that mechanical ventilation is not necessary in the case of the tunnel investigated. It is to be emphasized that our modeling might represent a reference in investigating the effects of natural ventilation in tunnels.

DETERMINATION OF THE ROLLING RESISTANCE COEFFICIENT UNDER DIFFERENT TRAFFIC CONDITIONS

In this paper, an experimental study of the determination of the rolling resistance coefficient is carried out. The experiment tests a total of six different types of vehicles and calculates the rolling resistance coefficient depending on the condition of the surface and the type of tires. The main aim of the research is to introduce new values of the rolling resistance coefficient and its impact on fuel consumption in real traffic conditions. Motor vehicles are subjected to a "free stop" method on a horizontal road. In doing so, the vehicle speed is registered every 10 seconds from an initial speed to stopping. In order to eliminate an error of possible roadway inclination or wind impact, the experiment is repeated five times on the same road section as well as in the opposite direction. The experimental study was carried out during December 2016 and January 2017. Three sets of tires were used for each vehicle, the tires with tread depths of 8 mm, 6-7 mm and 4-5 mm, while the type of surface referred to dry and wet conditions of the roadway. Both hypotheses have been confirmed using Analysis of Variances. The results show that the tread depth of tires and the meteorological conditions affect increasing the values of the rolling resistance coefficient.

Имитационное моделирование работы пункта взимания платы с транспортных средств на внутригородской дороге

A large number of toll road projects with a barrier toll collection system are currently being implemented in Russia. Therefore, it seems relevant to study the toll plaza as an element of the transport infrastructure. Insuffi cient attention to the issues of assessing current and predicted intensity on TPscan cause regular traffi c congestions on toll roads. The goal of this study is to build a simulation model that allows to evaluate the capacity of the toll collection point during the operation of the toll road at different traffi c fl ow rates, taking into account the ratio of different types of vehicles and user behavior errors. Visual observation materials are used, the research method is discrete-event simulation of PVP using AnyLogic software, processing of the results is performed in the statistical package R. The toll point (TP) the Western High-Speed Diameter toll road in St. Petersburg, Russia was considered as a case for the study.As a result was build simulation model of TP was developed, taking into account the traffi c specifi cs and user behavior errors. Conducted experiments established the peak traffi c intensities, when traffi c congestion begins to form at the TP, with different ratios of electronic toll collection usage. During the analysis, few cases of service time distribution were considered — from low to high traffi c intensities. Main conclusions of the study: -for the low intensity case, the results of the analysis showed the splitting of the total distribution of the service time into two distributions for different operating modes of toll lanes, — for high intensity, the infl uence of user behavior on service time distribution was revealed, — for each case, the parameters of the gamma laws of service time distribution were determined, — in case of insuffi cient throughput capacity, the TP stops working effi ciently, and service time distribution increases, regardless of the type of payment. Estimated peak hours of TP operation, when there is a potential for congestion at the TP were defi ned. Possible ways of further increase thetoll collection system throughput capacity for the TP were indicated.

Crash narrative classification: Identifying agricultural crashes using machine learning with curated keywords

Objective Traditionally, structured or coded data fields from a crash report are the basis for identifying crashes involving different types of vehicles, such as farm equipment. However, using only the structured data can lead to misclassification of vehicle or crash type. The objective of the current article is to examine the use of machine learning methods for identifying agricultural crashes based on the crash narrative and to transfer the application of models to different settings (e.g., future years of data, other states). Methods Different data representations (e.g., bag-of-words [BoW], bag-of-keywords [BoK]) and document classification algorithms (e.g., support vector machine [SVM], multinomial naïve Bayes classifier [MNB]) were explored using Texas and Louisiana crash narratives across different time periods. Results The BoK-support vector classifier (SVC), BoK-MNB, and BoW-SVC models trained with Texas data were better predictive models than the baseline rule-based algorithm on the future year test data, with F1 scores of 0.88, 0.89, 0.85 vs. 0.84. The BoK-MNB trained with Louisiana data performed the closest to the baseline rule-based algorithm on the future year test data (F1 scores, 0.91 baseline rule-based algorithm vs. 0.89 BoK-MNB). The BoK-SVC and BoK-MNB models trained with Texas and Louisiana data were better productive models for Texas future year test data with F1 scores 0.89 and 0.90 vs. 0.84. The BoK-MNB model trained with both states’ data was a better predictive model for the Louisiana future year test data, F1 score 0.94 vs. 0.91. Conclusions The findings of this study support that machine learning methodologies can potentially reduce the amount of human power required to develop key word lists and manually review narratives.

Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications: The application of CAVs can improve the stability and safety of traffic flow.

Improvements of some roundabout Intersections using capacity model Analysis

Several methods of analysis have been proposed to analyze the ability of vehicles to rotate and manoeuvre during roundabout intersections. Some models have been using formulas and calculations for extracting the results manually, while other models have been used to use the engineering software for the model. Given these differences, it may not be clear how best to use them in a particular situation. When comparing methods of capacity analysis, it will be useful to know how the various techniques are performed across a set of approaches Conflicting volumes. This paper reports on the ability of techniques to rotate multiple pathways based on maximum entry and exit traffic volumes using several methods of analysis. The research study the capacity model for some roundabout intersections in Hilla city, they are, (Al- Mohafada, Al-Saia and, Sahat Abn-Idreas). The capacity equations have been examined using some of the software programs, Highway Capacity HSC 2010, (Signalized Intersection Design and Research Aid) SIDRA, (macroscopic analysis and optimization software application). Synchro 10, to reach the best solutions to describe traffic capacity for each intersection within the study area. Data have been collected according to field survey include traffic volume for different types of vehicles and pedestrians, within antemeridian & post-meridiem (a.m&p.m ) Peak Hour Volume (PHV), Number of lanes, lane width, median width, shoulder width and pedestrians crossing included too, using video technique method. Comparing the capacity for each approach and intersections using the above software programs on the bases of several variables: V/c, delay time, LOS and queue length. It was concluded that all intersections appear with the level of service LOS (E–F) according to (V/c)>1 and increase the delay time. Also, LOS improved from (F to C) when adding one lane for each approach for Al-Mohafada roundabout intersection according to output SIDRA software, as well as it was found that SIDRA software program gives significant results on a capacity model due to take into account the geometric characteristics of the intersections and pedestrian movements and crossing. Likewise, the Syncro program creates simulations of the roundabout with the traffic volumes entering the roundabout intersection, through which it identifies the conflicting points inside the circular intersection and shows the delay in each approach and also shows the queue for each approach.

Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements

Accurate vehicle configurations (vehicle speed, number of axles, and axle spacing) are commonly required in bridge health monitoring systems and are prerequisites in bridge weigh-in-motion (BWIM) systems. Using the ‘nothing on the road’ principle, this data is found using axle detecting sensors, usually strain gauges, placed at particular locations on the underside of the bridge. To improve axle detection in the measured signals, this paper proposes a wavelet transform and Shannon entropy with a correlation factor. The proposed approach is first verified by numerical simulation and is then tested in two field trials. The fidelity of the proposed approach is investigated including noise in the measurement, multiple presence, different vehicle velocities, different types of vehicle and in real traffic flow.

Hair follicle targeting with curcumin nanocrystals: Influence of the formulation properties on the penetration efficacy

Nanocrystals are a universal formulation approach for improved drug delivery of poorly water-soluble drug substances. Besides oral application, also topical application of the nanocrystals is feasible, because the increased kinetic solubility of the nanocrystals results in an increased concentration gradient, thus fostering passive, dermal penetration. Nanocrystals are also promising for targeting drug substances into the hair follicle. After penetration into the hair follicle, the nanocrystals could form a depot from which the active is released into the hair follicle. Thus, leading to a long-lasting and very efficient dermal drug delivery. The efficacy of nanocrystals to penetrate the hair follicles and the influence of the vehicle in which the nanocrystals are suspended was not yet investigated. Therefore, in this study curcumin nanocrystals with a size of about 300 nm were produced and incorporated into gels with different properties. The efficacy to penetrate the hair follicles, as well as the passive, dermal penetration, was assessed on the ex-vivo pig ear model. Nanocrystals were efficiently taken up by the hair follicles and reached the lower part of the infundibulum. This region is optimal for efficient drug delivery because the barrier of the lower infundibulum is not fully developed and thus more permeable, which results in a less hindered passive diffusion of drug substances. The penetration efficacy of the nanocrystals into the hair follicles was not affected by the different types of vehicles, which represented either oleogels or hydrogels that varied in viscosity as well as in the type and the concentration of the gelling agent. All gels possessed a shear-thinning flow behavior and it is hypothesized that all gels fluidized during the skin massage, whereby leading to similarly low viscosities than the aqueous nanosuspension and thus to similar penetration results. The passive, dermal penetration of curcumin was different for the different gels and the main driving parameter leading to good passive diffusion was caused by good skin hydrating properties of the vehicle. The best passive penetration was achieved from hydrogels that contained a humectant. However, the addition of the humectant reduced the efficacy of the nanocrystals to penetrate the hair follicle. Data so far, therefore, suggest that hair follicle targeting with nanocrystals that are suspended in water or simple, shear-thinning gels is highly effective. However, the addition of other excipients, e.g. humectants, to these vehicles might cause changes in the penetration profiles. More research in this regard is needed to understand these observations in more detail.

Graph attention network for detecting license plates in crowded street scenes

• A new problem of multiple vehicles license plate detection is addressed. • Proposed a new framework that combines ResNet and GAT layers in a new way • The proposed method outperforms the existing methods. Detecting multiple license plate numbers in crowded street scenes is challenging and requires the attention of researchers. In contrast to existing methods that focus on images that are not crowded with vehicles, in this work we aim at situations that are common and complex, for example, in city environments where numerous vehicles of different types like cars, trucks, motorbike etc. may present in a single image. In such cases, one can expect large variations in license plates in terms of quality, backgrounds, and various forms of occlusion. To address these challenges, we explore Adaptive Progressive Scale Expansion based Graph Attention Network (APSEGAT). This approach extracts local information which represents the license plates irrespective of vehicle types and numbers because it works at the pixel level in a progressive way, and identifies the dominant information in the image. This may include other parts of vehicles, drivers and pedestrians, and various other background objects. To overcome this problem, we integrate concepts of graph attention networks with progressive scale expansion networks. For evaluating the proposed method, we use our own dataset, named as AMLPR, which contains images captured in different crowded street scenes in different time span, and the benchmark dataset namely, UFPR-ALPR, which provides images of a single vehicle, and another benchmark dataset called, UCSD, which contains images of cars with different orientations. Experimental results on these datasets show that the method outperforms existing methods and is effective in detecting license plate numbers in crowded street scenes.