Toll Collection Research Articles

Visualization of commuting areas and examination of jobs-housing balance with the adoption of excess commuting

Rapid urban growth extends the boundaries of urbanized areas and makes people spend extra travel cost on the journey to work. Scholars have examined commuting behaviors between residence and work places within a region, and suggested the conceptual theory of jobs-housing balance to evaluate if there are sufficient job opportunities for residents in the region. A quantitative measurement of excess commuting is proposed to reflect the degree of jobs-house balance. However, current studies focus on the evaluation of excess commuting and the causality of spatial restructuring between residence and workplace, the spatial extend of excess commuting in an urban region is not visually delineated. In addition, conventional studies acquire static data such as statistic data or other survey data to analyze impacts of commuting patterns on urban formation. Today the emergence of spatial big data sheds light on the causality of urban commuting from higher spatial and temporal resolutions. This study aims to examine the degree of jobs-housing balance through the calculation of excess commuting and represent the spatial extent of excess commuting of Taichung metropolitan area in Taiwan. Electronic Toll Collection (ETC) data, the spatial big data which is publicly accessible in Taiwan, is introduced as the commuting data and to calculate three key parameters of excess commuting, Tmin, Tobs, and Tmax. The three parameters are used to visualize the extent of commuting zones and examine commuting patterns of Taichung metropolitan area and observe the spatial relationship between those patterns and land-use types. The evaluation of excess commuting indicates that the degree of jobs-housing imbalance in Taichung metropolitan area is similar to Los Angeles metropolis in US. The analytical results explain how ETC data can be successfully used for the calculation of excess commuting, and visualization of the extent of commuting regions. In addition, after the cluster analysis of residential areas, it is intuitive to observe spatial correlation between hot zones of residential areas and the spatial extent of theoretical maximum commuting, and shows that the scenarios of commuting can be considered as an indicator to explain the possibility of urban expansion in the future.

Modeling of Applying Road Pricing to Airport Highway Using VISUM Software in Jordan

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method.

Service time thresholds at barrier-operated freeway exit toll booths

This paper discusses service times based on vehicle class at barrier-operated toll booths. Service times were measured using four vehicle classifications: car (C), medium goods vehicle (MGV), trucks & bus (TB), and articulated truck (AT) with two payment options: electronic (ETC) and manual toll collection (MTC). Each toll booth has both payment systems and resulting in mixed toll booth utilization in terms of payment and vehicles. The collected data was utilized to estimate logistic regression, and non-parametric statistical tests were performed. Findings indicate, when vehicle size increases, so does service time for even ETC. Furthermore, logistic regression used to compute threshold service times shifting from ETC to MTC for C, MGV, TB, and AT, which were 5.92, 7.51, 10.00, and 12.80 s, respectively. The transition time for a vehicle switch from ETC to MTC based on the logistic curve is denoted by threshold service time. Below the threshold, ETC is more favorable option and affected by barrier and vehicle class. Overall, the results indicate that operators can benefit from more dynamic operational conditions facilitated by threshold service times, enabling adaptive and efficient toll booth operations. Additionally, these thresholds can serve as valuable measure of service quality for managing freeway exits.

Automatic toll collection system using RFID with vehicle classification using convolutional neural network

Automatic toll collection system using RFID with vehicle classification using convolutional neural network

A data-driven optimization-based approach for freeway traffic state estimation based on heterogeneous sensor data fusion

Accurate estimation of freeway traffic states is crucial for designing effective traffic management and operational strategies. The integration of various sensor data, such as data from the Electronic Toll Collection (ETC) system and traffic detectors, can significantly enhance the granularity and coverage of traffic state estimation. This study introduces a data-driven optimization-based approach for estimating freeway traffic states, leveraging the fusion of ETC data with detector data. This methodology capitalizes on the broad coverage provided by ETC data and the fine granularity offered by detector data. The probabilistic interdependence between the traffic state of a segment and its upstream and downstream counterparts is captured from real-world traffic state data. Two optimization models, based on the maximum likelihood and maximin likelihood principles, are developed to accurately depict the distribution patterns of freeway traffic states. To address the computational challenges of large-scale scenarios, the study proposes both a decomposition algorithm and a heuristic algorithm. A case study utilizing real-world data from the G92 freeway in Zhejiang, China, is conducted. The findings indicate that the two optimization models exhibit commendable accuracy, with mean absolute percentage errors of 0.9% and 2.3% during peak hours, and 0.9% and 1.4% during off-peak hours, respectively.

Evaluating Factors Shaping Real-Time Internet-of-Things-Based License Plate Recognition Using Single-Board Computer Technology

Reliable and cost-efficient license plate recognition (LPR) systems enhance security, traffic management, and automated toll collection in real-world applications. This study addresses optimal unique configurations for enhancing LPR system accuracy and reliability by evaluating the impact of camera angle, object velocity, and distance on the efficacy of real-time LPR systems. The Internet of Things (IoT) LPR framework is proposed and utilized on single-board computer (SBC) technology, such as the Raspberry Pi 4 platform, with a high-resolution webcam using advanced OpenCV and OCR–Tesseract algorithms applied. The research endeavors to simulate common deployment scenarios of the real-time LPR system and perform thorough testing by leveraging SBC computational capabilities and the webcam’s imaging capabilities. The testing process is not just comprehensive, but also meticulous, ensuring the system’s reliability in various operational settings. We performed extensive experiments with a hundred repetitions at diverse angles, velocities, and distances. An assessment of the data’s precision, recall, and F1 score indicates the accuracy with which Thai license plates are identified. The results show that camera angles close to 180° significantly reduce perspective distortion, thus enhancing precision. Lower vehicle speeds (<10 km/h) and shorter distances (<10 m) also improve recognition accuracy by reducing motion blur and improving image clarity. Images captured from shorter distances (approximately less than 10 m) are more accurate for high-resolution character recognition. This study substantially contributes to SBC technology utilizing IoT-based real-time LPR systems for practical, accurate, and cost-effective implementations.

Expressway Vehicle Arrival Time Estimation Algorithm Based on Electronic Toll Collection Data

Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time state perception of vehicles on expressways, we propose an algorithm to estimate the arrival time of vehicles in the next segment using Electronic Toll Collection (ETC) data. Firstly, the characteristics of ETC data and GPS data are meticulously described. We devise algorithms for data cleaning and fusion, subsequently segmenting the vehicle journey into multiple sub-segments. In the following step, feature vectors are constructed from the fused data to detect service areas and analyze the expressway segment characteristics, vehicle traits, and the influence of service areas. Finally, an algorithm utilizing LightGBM is introduced for estimating the arrival time of vehicles at various segments, corroborated by empirical tests using authentic traffic data. The MAE of the algorithm is recorded as 20.1 s, with an RMSE of 32.6 s, affirming its efficacy. The method proposed in this paper can help optimize transportation systems for improving efficiency, alleviating congestion, reducing emissions, and enhancing safety.

A study of uncertain 4D transportation problems with rough interval parameters and additional real-life factors

In developing countries like India and Nepal, a vehicle document verification system is implicated at the checkpoints and border crossings that allows users to authenticate and verify the validity of vehicle related documents such as registration certificates, driving licenses, pollution under control certificates, and insurance policies. This results in the formation of long queues of the vehicles and hence a significant time wastage. These vehicles waiting in the long queues, continue to emit carbon dioxide and other greenhouse gases into the atmosphere. To address these issues, the government has introduced the Automated Number Plate Recognition (ANPR) technology which plays a significant role in the efficient and accurate document verification by identifying and validating vehicle registration details. It also automates toll collection by capturing the vehicle’s number plate, reducing waiting times and congestion at toll barriers. To analyze all these benefits, in this study, for the first time, ANPR technology has been introduced in the multi-objective model of transportation problem and its impact on various objectives has been observed. Also, real-life factors: fixed-charge cost, carbon emission, multiple routes have been considered in the proposed model and the problem is named as multi-objective fixed-charge four dimensional green transportation problem. Rough set theory has been employed as a tool for effectively addressing the indeterminacy and facilitating the modeling of real-life transportation problems. Two methods: the expected value method and the rough chance constrained programming method, are proposed to transform the suggested model of multi-objective transportation problem into a deterministic form. To solve these transformed models, a new multi-objective optimization approach based on the neutrosophic theory, named as neutrosophic goal programming has been introduced. To further demonstrate the practicality of the proposed study, an application for the apple fruit transportation has been solved. Detailed comparisons between obtained results are done on the basis of different modeling perspectives, different solution techniques and also with/and without ANPR technology. Based on the outcomes, the suggested neutrosophic goal programming technique proved to be superior to the existing techniques in the literature.

Multi – criteria analysis of toll collection technologies from a financial and economic aspect

The choice of the appropriate toll collection system is a very important decision because it requires significant investment and a poor decision regarding the choice of toll collection system can lead to major economic, environmental, and social problems for both present and future generations. This study aims to propose a methodological framework for decision support in selecting toll collection systems based on comprehensive multi-criteria analysis. The selection of the most effective toll collection system was performed from financial and economic aspects, using the AHP method. The study included 9 different toll collection technologies: DSRC with barriers, DSRC MLFF, RFID, GNSS/CN, ACM, ANPR, SmartCard, Smartphone, and vignettes. By the defined problem and specific priorities, it was found that GNSS/CN (0.174) is the best toll-collection technology. RFID toll collection technology (0.169) and DSRC MLFF technology (0.163) are equally good, while automated toll payment booths (0.040) are at the bottom. The obtained results can help decision-makers when choosing the most profitable toll-collection technology from the financial and economic aspects.

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

IMPROVING MARKETING STRATEGY BY USING MACHINE LEARNING AND GPS TRACKING

"Hawkar and Vendor is a remarkable software program that utilizes Global Positioning System (GPS) or radio frequency identification (RFID) to define geographical boundaries. Essentially, Hawkar and vendor create a virtual barrier known as geofencing, which is an innovative technology enabling an online marketplace for proactive contextual services. This technology allows users to easily discover and subscribe to interesting services, while enabling providers to offer their services for a variety of applications such as electronic toll collection, contextual advertising, or tourist information systems, without the need for additional infrastructure. The primary objective of this research was to explore how the use of spatial data can enhance advertising performance for customers. Tracking systems and monitoring, based on global navigation services by satellite and including geofencing functionality, could also contribute to pinpointing the exact location of an institution or company, thereby enhancing sales and business prospects efficiently. This shift allows for advertising on smartphones as an economically and accurately tested alternative to large billboards." Key Words: Hawkars &Vendors, Special offer ,Machine learning, Blind people

Research on the Expansion Application of Electronic Toll Collection System

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

Swift Tag: Advancing Toll Collection Through Automated Number Plate Recognition and OCR Technologies

Swift Tag presents a novel approach to toll collection, leveraging Automated Number Plate Recognition (ANPR), Optical Character Recognition (OCR), and modern frontend/backend technologies to streamline operations, enhance accuracy, and improve user experience. By automating the identification and processing of vehicles at toll booths, Swift Tag eliminates manual intervention, reduces congestion, and minimizes errors. Utilizing YOLOv8 for object detection and EasyOCR for character recognition, Swift Tag ensures real-time capture and extraction of license plate information with remarkable precision. Complemented by a comprehensive frontend interface developed using React and JavaScript, and a robust backend infrastructure for seamless integration with existing toll management systems, Swift Tag offers a scalable, cost-effective, and universally applicable solution to toll collection challenges

Research on the Development Status of Highway Electronic Toll Collection System

Highway Electronic toll collection system(ETC) is the most important part of intelligent transport system. Research and analysis on the development status of ETC can provide a research basis for its future development. In this paper, three subsystems of the highway Electronic toll collection system (ETC): automatic vehicle identification, automatic vehicle classification, and audit system are studied, and by comparing the research methods of scholars in different periods of time on automatic vehicle identification, automatic vehicle model classification, and audit system, it is found that with the application of new technologies such as big data, edge computing, AI algorithms, and laser radar, the highway Electronic toll collection system (ETC) development level is increasing.

INTEGRATED PIEZOELECTRIC ENERGY HARVESTING: AUTOMATING TOLL COLLECTION AND HIGHWAY STREET LIGHTS

This research paper presents a comprehensive study on the integration of piezoelectric energy harvesting technology for the dual purpose of automatic toll collection and highway street lighting. The project proposes a novel system architecture that leverages the piezoelectric effect to convert mechanical stress from passing vehicles into electrical energy, thereby powering both toll collection mechanisms and street lights. The system incorporates RFID technology for seamless vehicle identification and toll deduction, along with an intelligent control system for efficient energy management. The paper discusses the design, implementation, advantages, challenges, and considerations of the proposed system, highlighting its potential for sustainable infrastructure development and energy efficiency.

A Review on Automatic Toll Collection System

One of the most crucial elements for any country's development is logistics, or transportation. Since India is one of the world's largest nations, road transportation plays a vital role in the country's economy. For any government, having fast road transit becomes essential, and toll plazas are essential to this. The most commonly used toll systems in India at the moment are the manual toll collection and FASTag systems. Toll plaza processing times rise as a result of their need for a toll collector or attendant. The Automatic Toll Collection System, or ATCS, is a tool used to automatically collect taxes. Here, radio frequency is used to help in identification. An RFID tag will be attached to a car. This tag is nothing more than an allocated unique identification number. The RTO, or traffic controlling body, will assign this. We shall keep all of his basic data and the amount he has paid in advance for the TOLL collection in line with this number. At the toll collection facility, the reader will be positioned strategically. His prepaid balance will be reduced by the tax amount each time the car passes the toll collection center. This essay discusses several automated toll collection systems that are intended to improve overall transportation efficiency, ease toll payment procedures, and lessen traffic congestion. The suggested method computes toll fees automatically by utilizing cutting-edge technology including mobile communication, GPS, and RFID (Radio Frequency Identification).

Assessment of Its Implementation on Safety on Yamuna Expressway

With the promise of fast connectivity between Greater Noida and Agra, the Yamuna Expressway represents modern infrastructure in India. Amidst all this splendor, though, is a grave worry: security. In order to reduce hazards and improve safety standards, this article investigates the complex network of obstacles that has been encountered while implementing safety measures on the highway. Numerous accidents, including fatalities, have been a recurring problem on the Yamuna Expressway since its opening. The expressway's relatively straight and smooth construction makes it easy for drivers to speed and act irresponsibly, which is the main cause of these incidents. Drivers disregard safety protocols and speed limits with complete disregard for the consequences, which is worsened by the lack of effective enforcement of traffic regulations. The absence of necessary safety elements is one of the primary obstacles to guaranteeing safety on the Yamuna Expressway. Poor lighting, inadequate signage, and restricted emergency services are some of the initial shortcomings that have greatly impacted drivers' ability to navigate safely, especially in bad weather or at night. There have been ongoing attempts to resolve safety problems, but the results have been mixed. The expressway's safety infrastructure has been strengthened by the implementation of measures such as crash barriers, better signage, expanded CCTV surveillance, and more police patrolling. Unfortunately, problems with corruption within law enforcement agencies, a lack of staff, and limited resources make it difficult to apply these restrictions effectively. The Yamuna Expressway can only be made safer with a comprehensive plan. A culture of safety among drivers can be greatly enhanced by public awareness programs that promote responsible driving and emphasize the repercussions of reckless driving. Intelligent transportation systems (ITS) integrate cutting-edge tech to improve traffic management and accident prevention via tools like speed cameras, automatic toll collecting, and real-time traffic monitoring. The Yamuna Expressway is an impressive example of India's infrastructure, yet it will remain unfulfilled until the critical problem of safety is resolved. The Yamuna Expressway has the potential to become a model of safe and efficient transportation for the millions of people who use it every day if we recognize the problems, find lasting solutions, and encourage everyone to do their part to keep everyone safe.

Automatic Number Plate Recognition

Automatic license plate recognition (ANPR) systems have become suitable for various applications, including traffic monitoring, law enforcement, and toll collection. This paper completes the study on automatic license plate recognition (ANPR) systems that use advanced imaging technology and machine learning algorithms to achieve accuracy in license plate verification and validation. The preparation process is adopted in various ways: image acquisition, preprocessing, location plate, character segmentation, and optical character recognition (OCR). The system, which integrates deep learning models for extraction and classification, performs better in different environments. Experimental results show that the identification accuracy of the proposed ANPR exceeds 95%, demonstrating its potential in practical applications. In addition, this paper also discusses the problems encountered in ANPR implementation, including changes in plate design, illumination, and shading, and provides suggestions for future research to improve stability and efficiency. Keywords: automatic plate recognition, image processing, machine learning, optical behavior recognition, deep learning, vehicle tracking, driving license inspection.

Compact design of circularly polarized microstrip patch antenna with double slip ring resonator for vehicle-to-vehicle communication

The paper mainly focuses on the compact design of circularly polarized microstrip patch antenna for vehicle-to-vehicle communication. The charging time of electric vehicle is longer waiting time than compared to traditional gasoline-based vehicles. The charging is done at the public charging stations. When the vehicles need charging while driving, they communicate with Dedicated Short Range Communications (DSRC) band to send the information to the nearest vehicles and receive information from the charging station. Moreover, DSRC band is used in many applications area such as Intelligent Transportation System (ITS), Electronic Toll Collection (ETC), Collision Avoidance, and connected vehicles. The research problem in this study is that when installing an antenna on a vehicle, it is important to have a compact size and achieve circular polarization. The research solution for the problem statement in this study is emphasized on 45? inclined slot and corner trimmed is added in rectangular patch to make the current part longer and the impedance matching better, and to get circular polarization. After that, Swiss roll structure CSRR is added in ground plane to improve bandwidth. Finally, outer SRR is added in the ground plane to improve S11. The proposed design has been validated for car-to-car (5.85-5.925 GHZ) communication.

Technological Standards for Interoperable Toll Collection: A Framework for Seamless Integration

Technological Standards for Interoperable Toll Collection: A Framework for Seamless Integration