Toll Collection Research Articles

A Survey on Privacy-Preserving Electronic Toll Collection Schemes for Intelligent Transportation Systems

As part of Intelligent Transportation Systems (ITS), Electronic toll collection (ETC) is a type of toll collection system (TCS) which is getting more and more popular as it can not only help to finance the government’s road infrastructure but also it can play a crucial role in pollution reduction and congestion management. As most of the traditional ETC schemes (ETCS) require identifying their users, they enable location tracking. This violates user privacy and poses challenges regarding the compliance of such systems with privacy regulations such as the EU General Data Protection Regulation (GDPR). So far, several privacy-preserving ETC schemes have been proposed. To the best of our knowledge, this is the first survey that systematically reviews and compares various characteristics of these schemes, including components, technologies, security properties, privacy properties, and attacks on ETCS. This survey first categorizes the ETCS based on two technologies, GNSS and DSRC. Then under these categories, the schemes are classified based on whether they provide formal proof of security and support security analysis. We also demonstrate which schemes specifically are/are not resistant to collusion and physical attacks. Then, based on these classifications, several limitations and shortcomings in privacy-preserving ETCS are revealed. Finally, we identify several directions for future research.

Estimation of system delay based toll equivalency factors at toll plazas using simulation

The service time and time headway of the vehicles are used to define the equivalency factor of different vehicle classes at the toll plaza. Both the service time and time headway are point measures and do not account for the system delay (time difference when a vehicle enters the queue and leaves the tollbooth) caused to the vehicle. The present study aims to quantify the system delay incurred to the vehicles at electronic toll collection (ETC) lanes under mixed traffic conditions using a microsimulation approach. Field data collected from one toll plaza located on a National Highway are used for simulation model generation. A new terminology called system delay-based toll equivalency factor (DTEF) is introduced to convert the different vehicle classes into equivalent passenger cars. The DTEF variation for different approach volumes and heavy commercial vehicle (HCV) compositions were checked at different ETC penetration levels. A total of 288 scenarios have been worked out, and simulation runs have been made for all such scenarios to obtain the DTEF values. The average DTEF value of HCV was obtained as 2.20. Further, it is found that with an increase in approach volume and HCV share in the traffic stream, the DTEF value increases. The outcome of the present study will be useful to field practitioners and engineers to determine the capacity in equivalent DTEF/hr and level of service of a toll plaza in terms of system delay.

Caught on Camera

Using smart image processing and advanced back-office systems to enable free-flow electronic toll collection for light vehicles

Safety Evaluation of Toll Plaza Diverging Area Considering Different Vehicles’ Toll Collection Types

Different toll collection types of vehicles and different distribution of tollbooths lead to the toll plaza diverging area becoming a typical vehicle weaving area with frequent crossing behaviors and conflicts on highways. This study aims to identify contributing factors to conflict risks of four RP by developing random parameters ordered logit models with heterogeneity in means and variances. The model can flexibly capture the unobserved heterogeneity of the contributing factors in different vehicle-following patterns. Real-world vehicle trajectory data obtained from the toll plaza diverging area in Nanjing, China, are used for model estimation. The results show that vehicle-following patterns with the same toll collection types have a higher percentage of severe conflict risks. The average acceleration of the following vehicles, lane-marking indicator, the initial lanes and lane changes of vehicles are significantly associated with the collision risk levels. The standard deviation of surrogate safety measures of all vehicles in sub-segments are found to differ significantly between vehicle-following patterns. Furthermore, a series of likelihood ratio tests are adopted to test the spatial dependence in sub-segments of the diverging area. The findings of this study could provide valuable information for safety improvement in toll plazas.

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.

Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data

Passenger cars have emerged as a substantial segment of the vehicles traversing expressways, generating extensive traffic data on a daily basis. Accurately identifying individual vehicles and their travel patterns and characteristics is crucial in addressing the issues that impede the sustainable development of expressways, including traffic accidents, congestion, environmental pollution, and losses of both personnel and property. Regrettably, the utilization of electronic toll collection (ETC) data on expressways is currently not adequate, and data analysis and feature mining methods are underdeveloped, leading to the undervaluation of data potential. Focusing on ETC data from expressways, this study deeply analyzes the spatiotemporal characteristics of travel by passenger car users. Here, we propose an advanced user classification model by combining the traditional clustering algorithm with the feature grouping recognition model based on a back propagation neural network (BPNN) algorithm. Real-world data on expressway vehicle travel are used to validate our models. The results show a significant improvement in iteration efficiency of over 26.4% and a 23.17% accuracy improvement compared to traditional algorithms. The travel feature grouping recognition model yielded an accuracy of 95.23%. Furthermore, among the identified groups, such as “Public and commercial affairs” and “Commuting”, there is a notable characteristic of high travel frequency and concentrated travel periods. This indicates that these groups have placed significant pressure on the construction of a safe, efficient, and sustainable urban transportation system.

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.

ТЕХНИКО-ЭКОНОМИЧЕСКАЯ ОЦЕНКА ВНЕДРЕНИЯ ЭЛЕКТРОННОГО СБОРА ПЛАТЫ ЗА ПРОЕЗД КАК ПОДСИСТЕМА ИНТЕЛЛЕКТУАЛЬНОЙ ТРАНСПОРТНОЙ СИСТЕМЫ

. The article substantiates the relevance of introducing electronic toll collection in order to improve the quality of functioning of regional transport systems. The quality of service for road users affects both social and economic efficiency it allows you to reduce the time spent on traffic, to ensure «just in time» delivery. Based on the target and functional performance indicators of intelligent transport systems, a variant of a technical and economic assessment of the introduction of electronic toll collection is proposed, taking into account the ratio of benefits and costs

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.

A Fast Sand Dust Image Enhancement Algorithm and Number Plate Detection

Abstract: The color variation and limited visibility of images taken in sand-dust conditions have a significant negative impact on computer vision systems. We suggest a quick and efficient approach to improve photographs taken in sand-dust weather in order to address the aforementioned issues. First, we make amends for the blue channel's loss value. The color of the image that has been damaged by sand dust is then corrected using white balance technology. In order to improve the image contrast and edge accuracy, guided image filtering is utilized, and an adaptive approach is used to determine the detail layer's magnification factor in order to improve the image detail information. We also added an additional feature of number plate detection of the sandy images.The aim of this task is to develop an automated system that can accurately and efficiently detect and recognize number plates from sandy images captured by a camera. This technology has various applications, such as traffic control, toll collection, parking management, and law enforcement. The experimental results demonstrate that the approach can successfully restore the fading characteristics of a large number of sand-dust-damaged photos. The proposed method may considerably improve the photos acquired during sand-dust weather conditions, and the results are better than those of existing methods, according to experimental results via qualitative and quantitative assessments

The Intelligent Vehicle Number Plate Recognition System based on Arduino

Abstract: The thesis proposes the development of The Intelligent Vehicle Number Plate Recognition System (IVNPRS) using the Arduino platform. The system uses image processing techniques to automatically detect and recognize vehicle number plates, and it includes a camera module, an image processing unit, and an Arduino microcontroller. The proposed system has practical applications such as security monitoring, automatic toll collection, and traffic monitoring. The thesis discusses the different components of the system, image processing techniques used, and evaluates its performance by measuring accuracy, speed, and robustness under different lighting and weather conditions. The experimental results suggest that the VNPRS has the potential to improve vehicular traffic and safety in various locations.

Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China

Understanding equity and travelers’ behavior plays a key role in creating suitable strategies to promote the development of the expressway. Especially, finding clusters of expressway users could help managers provide targeted policies in order to enhance service quality. However, it is challenging to identify expressway travel behaviors, such as traffic flow distribution and users’ classification. Electronic toll collection (ETC) has been widely applied to improve expressway management, because it can record the origin–destination information of users. This paper proposes a framework to analyze the equity and travel behavior of expressway users with a large amount of ETC data. In the first stage, the Gini coefficient is adopted to analyze expressway equity. In the second stage, 12 kinds of indicators are extracted, including number of trips, car type, mean distance, etc. In the third stage, kmeans algorithm is adopted to cluster the users, based on the introduced indicators. Finally, we analyze the traffic flow distribution of each group by constructing a traffic flow network. The results show that the Gini coefficient is 0.4193, which demonstrates evident inequity in the expressway service. Moreover, statistical analysis shows that expressway flow is complicated and 70.77% of travelers do not make repeat trips. It is demonstrated that expressway users can be divided into six groups, and the flow networks of cluster 2 and cluster 3 are connected more closely and evenly than other clusters are.

IOT Based Automated Toll Collection System using NodeMcu and Blynk App

India has to be digitalized in every manner imaginable given its continuous development and economic expansion. To lessen the line at the toll plaza and the amount of staff needed, the toll collecting system in India has to be digitalized. In this project, the RFID-based Automated Toll Collection System is used to reduce traffic congestion and guarantee system transparency. With the help of the suggested system, traffic jams on toll roads, bridges, and tunnels might be eliminated without the need of cash or the requirement that cars stop. This tactic is based on an electronic toll collecting system that utilises radio frequency identification (RFID) technology to identify a vehicle specifically employed for toll collection. The proposed RFID system uses tags that are fastened to the automobiles' digital licence plates, giving RFID readers access to the data contained on the tags. It is possible to reduce the need for vehicle owners and toll collection companies to physically issue tickets and collect tolls using this method. Vehicle owners and the toll authority may easily exchange information about toll payments. As a result, toll payment transparency may be ensured with minimal manual labour and human error. It will be easier to develop a smart transportation system as a consequence.

Reproduction of spatial–temporal distribution of traffic loads on freeway bridges via fusion of camera video and ETC data

Understanding the spatial–temporal distribution of traffic loads on bridge decks is essential for managing traffic flow and assessing bridge load effects. While computer vision can already identify on-bridge vehicle parameters through traffic surveillance cameras, obtaining cost-effective vehicle load information remains a challenge, even though it is the most important service load for bridges. In this study, we present a methodology that applies computer vision algorithms to fuzzy match video-identified data with electronic toll collection (ETC) recorded data to reproduce the spatial–temporal distribution of traffic loads on freeway bridges. The proposed method involves three critical steps. First, multi-object detection and trajectory tracking of vehicle contours, vehicle heads, vehicle tires, and vehicle license plates are implemented using YOLOv5 and DeepSort algorithms. Second, the conversion of image coordinates to real-world coordinates is automatically calibrated using pre-measured roadside reflection points. Third, vehicle license plate characters are identified by the LPRnet algorithm and the results are fused with highway ETC records to determine vehicle weight information, while the KNN clustering algorithm is used to further infer vehicle axle weight. After these three steps, the vehicle load sequence in the video surveillance area can be reproduced. Finally, the effectiveness and feasibility of the proposed method are verified in an example of a highway bridge.

Ergodic secrecy capacity of backscatter communication based automatic toll collection systems

Ergodic secrecy capacity of backscatter communication based automatic toll collection systems

Fastag Fraud Detection: A Literature Survey

Abstract: National Payments Corporation of India (NPCI) has developed the National Electronic Toll Collection (NETC) program to meet the electronic tolling requirements of the Indian market.The use of FASTag is prevalent in modern society. However, it is clear that the number of FASTag frauds in the global integration and existing protection system is constantly increasing. This is why the issue of FASTag fraud detection is very important. Various FASTag frauds occur and there is no means to detect or prevent them. Among the many frauds, we will try to reduce the fraud where drivers of heavy vehicles issue and use FASTags that are intended for light vehicles. In this way, drivers of heavy vehicles are exempted from paying the higher tolls that are set for heavy vehicles. FASTag fraud detection is used to solve this problem. The system will be able to identify the type of vehicle and the FASTag amount paid. Using this system, FASTag fraud detects if any anomaly has occurred and alerts the system accordingly

Improved Preprocessing Strategy under Different Obscure Weather Conditions for Augmenting Automatic License Plate Recognition

Automatic license plate recognition (ALPR) systems are widely used for various applications, including traffic control, law enforcement, and toll collection. However, the performance of ALPR systems is often compromised in challenging weather and lighting conditions. This research aims to improve the effectiveness of ALPR systems in foggy, low-light, and rainy weather conditions using a hybrid preprocessing methodology. The research proposes the combination of dark channel prior (DCP), non-local means denoising (NMD) technique, and adaptive histogram equalization (AHE) algorithms in CIELAB color space. And used the Python programming language comparisons for SSIM and PSNR performance. The results showed that this hybrid approach is not merely robust to a variety of challenging conditions, including challenging weather and lighting conditions but significantly more accurate for existing ALPR systems.

ОБҐРУНТУВАННЯ МЕТОДУ РОЗПОДІЛУ ТРАНСПОРТНИХ ПОТОКІВ ПРИ МІКРОМОДЕЛЮВАННІ ПУНКТІВ ЗБОРУ ОПЛАТИ ЗА ПРОЇЗД

Introduction. The practice of charging road tolls to compensate for the costs of the road construction and operation has been known since ancient times. The traditional approach to toll collection on motorways is that vehicles must stop and pay at the appropriate collection point (Toll Collection Plaza — TCP). At the same time, two payment systems are traditionally distinguished: an open one, in which the driver pays for traveling the entire toll section, and a closed one, in which, upon entering the toll motorway, the driver is obliged to take a ticket at the first point (station) of payment and return it upon exiting the motorway at the final payment point, paying for the actual distance traveled. In both systems, there are bound to be vehicle delays in TCP. In this regard, transport engineers and road design specialists face several urgent tasks, such as determining the impact of toll booths on the level of service, capacity and other traffic indicators on motorways, assessing the impact of TCP configuration and toll collection technologies on of the traffic flows indicators in the TCP zone and in the transport network. Problem statement. One of the modern methods of research and assessment of the state of traffic at toll collection points is microsimulation of the road network or individual TCPs. At the same time, the reliability of this analysis tool will be determined not only by the calibration parameters of the simulation model, but also by the quality of the adopted distribution of traffic flows. Purpose. The purpose of the work is to substantiate the method of traffic flows distribution when developing a microsimulation model of toll collection plazas via PTV Vissim software. Materials and methods. Single TCP scenarios are simulated in Vissim, which differ in the number of operation lanes, approaches to network reproduction, and applied traffic distribution methods. The results. The actual scientific and applied problem of justifying the method of traffic flows distribution in the microsimulation of toll collection plazas in PTV Vissim has been solved. An analysis of the possibilities of traffic reproduction methods in TCP using static routes, dynamic assignment, the PTV Vissim COM interface for dynamic assignment, “Managed lanes” routes was performed. In TCP models with the number of lanes from 3 to 6 with static routes and dynamic assignment, the realistic behavior of drivers when choosing a traffic lane in TCP is verified. An analysis of transport delays in TCP models was performed to compare the simulation results when using static routes and dynamic assignment.

CSFPre: Expressway key sections based on CEEMDAN-STSGCN-FCM during the holidays for traffic flow prediction.

The implementation of the toll free during holidays makes a large number of traffic jams on the expressway. Real-time and accurate holiday traffic flow forecasts can assist the traffic management department to guide the diversion and reduce the expressway's congestion. However, most of the current prediction methods focus on predicting traffic flow on ordinary working days or weekends. There are fewer studies for festivals and holidays traffic flow prediction, it is challenging to predict holiday traffic flow accurately because of its sudden and irregular characteristics. Therefore, we put forward a data-driven expressway traffic flow prediction model based on holidays. Firstly, Electronic Toll Collection (ETC) gantry data and toll data are preprocessed to realize data integrity and accuracy. Secondly, after Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) processing, the preprocessed traffic flow is sorted into trend terms and random terms, and the spatial-temporal correlation and heterogeneity of each component are captured simultaneously using the Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN) model. Finally, the fluctuating traffic flow of holidays is predicted using Fluctuation Coefficient Method (FCM). Through experiments of real ETC gantry data and toll data in Fujian Province, this method is superior to all baseline methods and has achieved good results. It can provide reference for future public travel choices and further road network operation.