Electronic Toll Collection System Research Articles

Implementation on Location-Based Services Using Geofencing

Geo-fencing (geofencing) is an innovative technology that utilizes the Global Positioning System (GPS) to create virtual boundaries, enabling various applications such as electronic toll collection, contextual advertising, and tourist information systems. This system aims to explore the effectiveness of spatial data in enhancing advertising performance. By leveraging tracking systems and monitoring based on satellite navigation services, including the geofencing function, businesses can accurately pinpoint their locations, boost sales, and improve overall efficiency. Geofencing facilitates remote monitoring of defined geographic areas through virtual fences, allowing for automatic detections when tracked mobile objects enter or exit these areas. This paper introduces the fundamental concepts of geofencing and explores its applications in the transportation and logistics sector. Key Words: Geofence boundaries, Shopping app, User preferences, Geofence triggers, GPS technology, User notifications, Marketing strategy, Mobile commerce, Location-aware technology, Mobile user engagement, Retail industry.

Towards intelligent transportation system: A comprehensive review of electronic toll collection systems

AbstractElectronic toll collection system (ETCS) devices on vehicles communicate wirelessly with road head devices at toll stations. ETCS is preferred in countries because it is more convenient and efficient than manual toll collection. However, the scattered and constant innovations in this sector require a bridge between worldwide ETCS usage and advancements and a single page to grasp this sector's developments to ensure further developments. Following a systematic review, this study examined ETCS worldwide and technology adaption considering the scopes, limitations, usages, and related technology of the ever‐developing electronic toll collection system. Existing ETCS technologies have been investigated and synchronized with ingrowing tech disparity to overcome the research synchronization gap. The study also included a bibliometric assessment to identify the important areas of research, technologies most used, degree of knowledge generated, growth/trend of the researches, most well‐known authors, keywords, countries, documents, source of publication, and so on considering time‐to‐time changes. The study provided bibliometric solutions to the gap of lack of synchronization of research worldwide and even ETCS implementation difference within a country, which complicated collaboration with the central system and linked organizations and made it difficult for users to adapt the system.

The dynamics of traffic congestion: Data from a freeway Electronic Toll Collection system

Electronic Toll Collection systems installed at regular intervals along freeways provide a tremendous amount of individualized vehicle data useful for traffic analyses and predictions. We demonstrate how real data from the Taiwan freeway system enables us to monitor the evolution of traffic congestions on the fundamental diagram, and find that congestion is characterized by a loopy trajectory such that the area enclosed by the loop increases with the severity of congestion. There is a power law relating the area of the loop to the drop of influx during the congestion period. Data extracted from the time delays of individual vehicles show that the area enclosed by the loop in the fundamental diagram is a measure of the economic loss due to congestion. The use of the loss area to understand various highway characteristics is also explored.

Why Privacy-Preserving Protocols Are Sometimes Not Enough: A Case Study of the Brisbane Toll Collection Infrastructure

The use of Electronic Toll Collection (ETC) systems is on the rise, as these systems have a significant impact on reducing operational costs. Toll service providers (TSPs) access various information, including drivers' IDs and monthly toll fees, to bill drivers. While this is legitimate, such information could be misused for other purposes violating drivers' privacy, most prominent, to infer drivers' movement patterns. To this end, privacy-preserving ETC (PPETC) schemes have been designed to minimize the amount of information leaked while still allowing drivers to be charged. We demonstrate that merely applying such PPETC schemes to current ETC infrastructures may not ensure privacy. This is due to the (inevitable) minimal information leakage, such as monthly toll fees, which can potentially result in a privacy breach when combined with additional background information, such as road maps and statistical data. To show this, we provide a counterexample using the case study of Brisbane's ETC system. We present two attacks: the first, being a variant of the presence disclosure attack, tries to disclose the toll stations visited by a driver during a billing period as well as the frequency of visits. The second, being a stronger attack, aims to discover cycles of toll stations (e.g., the ones passed during a commute from home to work and back) and their frequencies. We evaluate the success rates of our attacks using real parameters and statistics from Brisbane's ETC system. In one scenario, the success rate of our toll station disclosure attack can be as high as 94%. This scenario affects about 61% of drivers. In the same scenario, our cycle disclosure attack can achieve a success rate of 51%. It is remarkable that these high success rates can be achieved by only using minimal information as input, which is, e.g., available to a driver's payment service provider or bank, and by following very simple attack strategies without exploiting optimizations. As a further contribution, we analyze how the choice of various parameters, such as the set of toll rates, the number of toll stations, and the billing period length, impact a driver's privacy level regarding our attacks.

TranStat: an intelligent system for producing road and maritime transport statistics using big data sources

The development of digital technologies, increasing the availability of big data and advanced processing techniques have enabled Statistics Poland to modernise the system for producing road and maritime transport statistics. As a result of the activities undertaken to adopt modern big data technologies and data from sensors, e.g. the Automatic Identification System (AIS) or the e-TOLL electronic toll collection system, new statistics have been obtained and data dissemination has accelerated. In addition, these activities ensure continuity in data production, especially in situations where collecting data from individuals may be difficult (e.g. the COVID-19 epidemic). The primary purpose of this article is to present the innovative TranStat system that enables the production of road and maritime transport statistics based on large volumes of data in order to shape the country’s transport policy. The system was developed under the GOSPOSTRATEG programme and implemented by Statistics Poland, the Statistical Office in Szczecin, the Maritime University of Szczecin, and the Cracow University of Technology. The study presents the most important aspects of the TranStat system, i.e. the characteristics of data sources, the description of functional subsystems, assumptions of the developed models and result data for traffic statistics, transport performance and exhaust emissions calculations for both types of transport. This study also provides information on smart forms implemented by Polish official statistics, reducing the burden on respondents and the costs of surveys.

Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems.

The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field.

The Design of a Circularly Polarized Antenna Array with Flat-Top Beam for an Electronic Toll Collection System

The Design of a Circularly Polarized Antenna Array with Flat-Top Beam for an Electronic Toll Collection System

Research and design of simulation and verification system of intelligent expressway based on ETC big data

Electronic toll collection (ETC) system records a large number of travel trajectories of vehicles on expressways, and it has a great potential application value. However, the current simulation system mainly focuses on simulating the characteristics of traffic flow while ignoring the real-time flow conditions of the road is difficult to calculate and display quantitatively, and the overall optimization cost is also notably substantial. Currently, there is a lack of a simulation system tailored for the ETC environment, which addresses the challenge of real-time traffic flow computation and holistic optimization, fulfilling the requisites of pertinent research. According to the topological structure inherent to an actual provincial road network on expressways, this paper devises a framework for a simulation system that conforms to the current ETC environment. We solved the critical problem of generating simulation data in the simulation system by establishing a Feature Extraction Algorithm for spatio-temporal features derived from ETC transaction data (Edata). Then we put forward Traffic Control Strategy Algorithm in ETC simulation system, which can provide decision indicators for simulating the control of traffic flow of the expressway. At the same time, we optimized the improved Multi-Task Scheduling Algorithm (ETC_MTS) based on the application scenario of real-time parallelism of multi-task on expressways, which provides better execution performance compared with the current mainstream algorithms such as Shortest Job First Scheduling Algorithm (SJFS), Priority Scheduling Algorithm (Priority), First Come First Serve Scheduling Algorithm (FCFS) and Round Robin Scheduling Algorithm (RR).

A scalable approach towards decentralized electronic toll collection system using Solana and RFID

A scalable approach towards decentralized electronic toll collection system using Solana and RFID

Dynamic Anomaly Detection in Gantry Transactions Using Graph Convolutional Network-Gate Recurrent Unit with Adaptive Attention

With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management.

A Wideband, High Gain and Low Sidelobe Array Antenna for Modern ETC Systems

Modern electronic toll collection (ETC) systems are currently moving towards a system design that is capable of fast and optimal payment handling for a single lane. Thus, the antenna of a roadside reader unit (RSU) needs to provide sufficient coverage over the vehicle lane during a payment cycle. In this paper, a left-hand circularly polarized (LHCP) 2×4 array antenna is proposed for European standard RSU readers at 5.8 GHz. The proposed array antenna consists of patch element antennas with parasitic elements to enhance gain and bandwidth. Sequential phase rotation feeding networks are applied to make the antenna low-profile. By using the optimized nut-shaped metasurface, the antenna can achieve higher gain due to concentrated radiation power in the boresight direction. The antenna has a wide impedance bandwidth of 2.34 GHz (37.73%), axis ratio bandwidth of 2.07 GHz (32.9%), high gain of 17 dBi, and sidelobe level (SLL) lower than -15 dB at 5.8 GHz. Especially, half-power beamwidths are 34∘ and 17∘ in horizontal and vertical planes, respectively, which covers sufficient space for a single lane while avoiding interference with other lanes. The performance of the proposed antenna is verified by measured results. It showed that the proposed antenna is a promising candidate for ETC applications.

Dynamic Generation Method of Highway ETC Gantry Topology Based on LightGBM

In Electronic Toll Collection (ETC) systems, accurate gantry topology data are crucial for fair and efficient toll collection. Currently, inaccuracies in the topology data can cause tolls to be based on the shortest route rather than the actual distance travelled, contradicting the ETC system’s purpose. To address this, we adopt a novel Gradient Boosting Decision Tree (GBDT) algorithm, Light Gradient Boosting Machine (LightGBM), to dynamically update ETC gantry topology data on highways. We use ETC gantry and toll booth transaction data from a province in southeast China, where ETC usage is high at 72.8%. From this data, we generate a candidate topology set and extract five key characteristics. We then use Amap API and QGIS map analysis to annotate the candidate set, and, finally, apply LightGBM to train on these features, generating the dynamic topology. Our comparison of LightGBM with 14 other machine learning algorithms showed that LightGBM outperformed the others, achieving an impressive accuracy of 97.6%. This methodology can help transportation departments maintain accurate and up-to-date toll systems, reducing errors and improving efficiency.

MODELING TRAFFIC OPERATIONS AT A TOLL PLAZA USING DISCRETE-EVENT SIMULATION

The objective of this study is to develop a discrete-event simulation model of traffic operations at a toll plaza to compare the performance measures between the manual and electronic toll collection (ETC) systems. This study also attempts to simulate the scenarios where the number of servers in these systems is reallocated to improve the overall operational performance. The field data were collected at a toll plaza on an expressway on-ramp during a PM peak period via video recording. The collected data were summarized to determine the characteristics of parameters as inputs for the simulation model. The simulation model was developed in EZStrobe, while the results were carried out via STROBOSCOPE. The simulation model was calibrated with the field data prior to making modifications and simulating various scenarios. The results show that, under the existing conditions, switching an electronic tollgate to a manual tollgate reduces the average overall queue delay and queue length by 71% and 78%, respectively. After reallocation, the average queue delays in these systems equalize as the proportion of ETC users reaches 72%. The additional throughput time of 0.4 s/veh in the ETC system is small compensation for the 6.8-second deduction in the average overall throughput time. In conclusion, shortening the queue lengths at the toll plaza by efficiently utilizing the existing tollgates is possible for improving the overall operational performance of the toll plaza without the additional tollgate installation.

Drivers’ willingness to shift towards electronic toll collection system in india

Electronic toll collection (ETC), locally known as the FASTag system in India, has been made compulsory for payment of tolls on all National Highways (NHs) from January 2020. The users are thus adopting the newly developed FASTag system for toll payment, but the penetration rates are very low. The present research aims at studying the factors affecting road users’ willingness or unwillingness to use the FASTag. The nationwide survey data are collected from the different vehicle users using an online survey and personal interview methods. Various trip-related parameters like vehicle type, trip purpose, etc.; attitudinal parameters like awareness of toll roads, and current toll rates; FASTag-related parameters like awareness of FASTag, and having or not having the FASTag, and socioeconomic and demographic parameters like age, gender, monthly income, etc., are considered in developing the questionnaire. Analysis of these survey data showed that drivers of big cars, Bus, multi axel vehicle (MAV) and Truck Trailers are more willing to use the FASTag facility to pay toll. A willingness to adopt the FASTag model is developed using the binary choice model. The results showed that trip-related attributes such as vehicle class, trip purpose and trip frequency significantly impact willingness to own FASTag. Further, awareness about FASTag is one of the important factors for shifting towards the FASTag system. Moreover, frequent users passing during peak hours are more likely to use FASTag. Also, the users who are in service, either Government or Private, have shown more willingness to adopt FASTag than those who are unemployed. Further, the stated preference analysis is also carried out for framing possible policy measures to increase the FASTag share. The outcome of the present study is expected to provide essential inputs to the policymakers for enhancing the adoption of FASTag among toll road users.

The Traffic Flow Prediction of Newly-Constructed Expresseway Based on ETC Database

The traffic flow prediction of newly-constructed expressway can provide reference for highway capacity design and highway toll standard formulation, but the traffic flow prediction of newly-constructed expressway is always considered a complicated project. With the wide application of electronic toll collection system, it is possible to effectively predict the traffic flow of newly-constructed expressways. In this paper, We propose a new highway traffic flow prediction method based on ETC database. ETC database can be used to extract OD matrix of expressway network, and the dynamic traffic allocation model is applied to predict the traffic flow of newly-constructed expressway. The experimental results show that the method is feasible and effective.

STEP TOWARDS INTELLIGENT TRANSPORTATION SYSTEM WITH VEHICLE CLASSIFICATION AND RECOGNITION USING SPEEDED-UP ROBUST FEATURES

<p>Vehicle classification is a crucial task owing to vehicles' diverse and intricate features, such as edges, colors, shadows, corners, and textures. The accurate classification of vehicles enables their detection and identification on roads and facilitates the development of an electronic tollcollection system for smart cities. Furthermore, vehicle classification is useful for traffic signal control strategy. However, achieving accurate vehicle classification poses significant challenges due to the limited processing time for real-time applications, image resolution, illumination variations in the video, and other interferences. This study proposes a method for automated automobile detection, recognition, and classification using statistics derived from approximately 11,000 images. We employ SURF-based detection and different classifiers to categorize vehicles into three groups. The Traffic Management System (TMS) is crucial for studying mobility and smart cities. Our study achieves a high automobile classification rate of 91% with the medium Gaussian Support Vector Machine (SVM) classifier. The paper's main objective is to analyze five object classifiers for vehicle recognition: Decision Tree, Discriminant Analysis, SVM, K-Nearest Neighbor Classifier (KNN), and Ensemble Classifier. In the discussion section, we present the limitations of our work and provide insights into future research directions.</p>

Development of Toll Equivalency Factors for FASTag Lanes Under Mixed Traffic Conditions

The traffic conditions in India consist of a high degree of heterogeneity in vehicle categories plying on the highways without any lane-following behavior. A similar behavior is observed at the toll plazas where a dedicated lane assigned to a vehicle class is encroached by the vehicles of other categories, causing a mixed nature of the traffic in the same lane. In the present study, service headway is taken as the measure of effectiveness for studying the effect of mixed traffic conditions on traffic operations at electronic toll collection system (ETC) lanes, which are called FASTag lanes in India. Service headway is composed of processing time and the clearance time of the vehicle. Videographic data were collected at three different toll plazas located on National Highways in India to capture the variability in traffic and human behavior. The results showed that the service headway for a vehicle varies between 1.56 s and 34.40 s. The service headway-based toll equivalency factors (HTEF) are developed for converting the mixed traffic flow into a homogeneous equivalent for FASTag lanes. The results showed that the HTEF varies from 1.01 to 2.61. The results are validated spatially with data taken from a different site, and no significant difference was found between both the converted and equivalent data. Field engineers can use the developed HTEFs to estimate the traffic flow in equivalent terms based on the traffic composition. Further, the present study results can also be used to study the capacity and level of service (LOS) of FASTag lanes.

Tagcaster: Activating Wireless Voice of Electronic Toll Collection Systems With Zero Start-Up Cost

This work enhances the machine-to-human communication between electronic toll collection (ETC) systems and drivers by providing an AM broadcast service to deployed ETC systems. This study is the first to show that ultra-high radio frequency identification signals can be received by an AM radio receiver due to the presence of the nonlinearity effect in the AM receiver. Such a phenomenon allows the development of a previously infeasible cross-technology and cross-frequency communication, called Tagcaster, which converts an ETC reader to an AM station for broadcasting short messages (e.g., charged-fees and traffic forecast) to drivers at tollbooths. The key innovation in this work is the engineering of Tagcaster over off-the-shelf ETC systems using shadow carrier and baseband whitening without the need for hardware nor firmware changes. This feature allows zero-cost rapid deployment in the existing ETC infrastructure. Two prototypes of Tagcaster are designed, implemented, and evaluated over four general and five vehicle-mounted AM receivers (e.g., Toyota, Audi, and Jetta). Experiments reveal that Tagcaster can provide good-quality (PESQ>2) and stable AM broadcasting service with a 30 m coverage range. Tagcaster remarkably improves user experience at ETC stations, and two-thirds of volunteer drivers rate it with a score of 4+ out of 5.

Which Factors Affect User Satisfaction with ETC? Evidence from Shanghai and Beijing

Owing to the current development trend of globalization and informatization, traditional transportation technologies and means can no longer meet the needs of economic and social development. Thus, intelligent transportation will be an inevitable and revolutionary choice for the development of the transportation industry. As a subsystem of the intelligent transportation system, the electronic nonstop toll collection (ETC) system provides a good solution to the problems of traffic congestion, environmental pollution, energy consumption, and low toll operation efficiency caused by expressway toll collection. This high-technology system is utilized in many fields, such as radio communication and computer and automatic control in the transportation field. China’s expressway ETC systems have evident effects on congestion reduction at toll stations. Although the advantages of ETC lanes are significant, and China actively implements ETC-handling policies, some drivers are reluctant to install ETC on-board systems or they have negative experiences after handling them. According to a survey, existing literature lacks research on the factors affecting user satisfaction with ETC. On the basis of the questionnaire data of ETC user experience in Shanghai and Beijing, we comprehensively investigate the reasons of banking, ETC equipment, and drivers’ personal factors and travel characteristics to establish an ordinal logistics model. Through cluster analysis, drivers in the two cities are divided into three groups, and the significance of related variables is further analyzed. This research can help us understand the degree of influence of different factors on user satisfaction with ETC to a certain extent. Moreover, effective methods and measures can be used to promote the popularity of ETC vehicle equipment and the utilization rate of ETC-dedicated lanes.

Adoption of FasTrak on San Francisco Bay Area Bridges: Impact of Operations Research Models in Relieving Congestion

Bay Area toll bridges are the main transportation link across the nine-county San Francisco Bay Area. These bridges experience extreme congestion and become bottlenecks during peak hours with long backups at the toll plazas. A solution to ensure smooth vehicle throughput at toll plazas is the widespread adoption of the electronic toll collection system called FasTrak. However, the FasTrak system has experienced low usage rates since its inception relative to other toll collection systems in the country. Forecasting, marketing, and operations research models were utilized to make recommendations and collaborate with transportation authorities to increase FasTrak usage during peak hours (5–10 a.m. and 3–7 p.m.) to address traffic congestion. After these recommendations were implemented, FasTrak usage increased from 40% in 2006 to the long-term target of 70% by 2016. This paper presents a synthesis of the challenges and the implementation of the FasTrak Strategic Plan. Furthermore, econometric models are presented that capture the effect on traffic volumes of increased FasTrak usage achieved through congestion pricing. Saved travel time resulted in productivity gains of approximately $569 million per year. This study contributes to an understanding of the role of effective transportation policies in reducing congestion and improving productivity. History: This paper was refereed. Supplemental Material: The online appendix is available at https://doi.org/10.1287/inte.2022.1127 .