Toll Collection Research Articles

EdgeTC - a PBFT blockchain-based ETC scheme for smart cities

To improve the quality of mobility and transportation in a smart city, Electronic Toll Collection (ETC) system has been widely implemented along toll roads, which can automatically collect the usage fee from vehicles. There are many advantages of using ETC systems, such as no need to prepare cash, no need to waste time waiting in a queue, and be able to improve the air quality at tollgate. However, there are still some challenges remained for existing ETC systems, in relation to data storage, trustworthiness and transparency. To address this issue, there is a need for building a unified, decentralized, and trusted data storing platform. Motivated by this demand, in this work, we introduce a blockchain-based ETC system called EdgeTC. Unlike most other blockchain-based ETC platforms that use PoW or PoS, our system uses PBFT to achieve relatively faster performance. In the evaluation, we compare the system performance between Ethereum-based and Hyperledger-based ETC. The results indicate that Ethereum-based system suffers efficiency problems when the chain becomes long, while Hyperledger-based ETC can maintain a stable time consumption during the transactions.

WITHDRAWN: Alert message based automated toll collection and payment violation management system using smart road side units

Abstract In the current scenario, Automated Toll Collection system plays a significant role in smart transportation for traffic management. Now a days, vehicle drivers and passengers spend their valuable time at the toll plaza as it is required to stop at the toll plaza and pay the toll amount, eventually, causes the cumulative effect of queuing up vehicles near the toll plaza. This situation highly increases the traffic congestion, pollution, long delays and also increases travel time of the passengers. Aimed at addressing this difficulty, in this study, a novel approach is proposed for automatic toll collection and toll violation management system. This paper presents the complete methodology of alert message based automatic toll payment collection and payment violation management system using smart Road Side Units (RSUs) and RFID reader, and thereafter, it presents the operations to be carried out at the toll plaza and by RSUs and also calculates the time required to cross the toll plaza by the vehicles with successful/unsuccessful payment. As vehicle traffic would be heavy in peak hours, handling manual toll collection method may cause queuing up vehicles at toll plaza, and for addressing this issue, in our proposed system, a novel methodology of fully automatic with completely man-free and barricade-free system is discussed and also toll violation management process is presented to perform necessary action against the vehicle that violates the toll plaza. Furthermore, we evaluate the performance of our proposed methodology using network simulator platform and the results are presented.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles (IoV) in future smart cities could help solve many traffic-related challenges, such as reducing traffic congestion and traffic accidents. Various congestion pricing and electric vehicle charging policies have been introduced in recent years. Nonetheless, the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system. In this paper, we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment. The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations. We propose a token management system that serves as a virtual currency, where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees. The proposed system is designed for Vehicular Ad-hoc Networks (VANETs) in the context of a smart city environment without the need to set up any expensive toll collection stations. Through large-scale traffic simulation in different smart city scenarios, it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

Road infrastructure development under PPP model in India: a credit rating perspective

PurposeThe study analyzes significant parameters defining the credit worthiness, economic viability and managerial efficiency of special purpose vehicles (SPVs) of infrastructure development firms engaged in the execution of road projects under PPP model in India.Design/methodology/approachThe study is based on a comprehensive review of credit rating reports of major rating agencies. In particular, 18 special purpose vehicles (13 BOT-toll–based and 5 BOT-annuity–based road projects) during the period 2010–2019 were considered to conduct a comparative analysis of their rating progression. Considering both financial as well as nonfinancial parameters, their segregation was done on the basis of strengths, constraints and key rating sensitivities influencing the ratings of SPVs involved in road projects under PPP model.FindingsPromoters' credibility emerged as an important factor affecting PPP credit ratings. Other prominent factors included nature of stretch and regulatory terms and conditions and the project's potential to generate cash flows. Inability of PPP projects to generate the projected levels of toll collections was a major constraint and hampered ratings over time. Growth in traffic was a key sensitive area in a toll-based project. Interestingly, despite the fixed nature of revenues, BOT (annuity) projects were impacted by rating changes.Research limitations/implicationsFewer sample projects (for which the data were available) was a constraint. Future research could consider larger data sets to provide deeper insights. An examination of credit rating parameters using rating reports of projects in other developing nations could provide meaningful implications. The findings of this research however cannot be undermined as the study bridges a gap in existing literature pertaining to the examination of PPP model from a credit rating perspective.Practical implicationsThis study would guide project developers, government agencies and awarding agencies of PPP road projects to anticipate the challenges and take adequate steps to mitigate them.Originality/valueResearch in the area of PPP projects is skewed toward risk assessment with respect to financial parameters. The present study emphasizes the rating framework of SPVs. Comprehensive examination of factors affecting project ratings in the form of projects' strengths, constraints and sensitivities would provide inputs to academics and researchers.

Overview of Intelligent Transport System (ITS) and Its Applications

Since last decades world, predominantly urban areas, is experiencing huge voluminous road traffic growth, resulting in heavy congestion, air pollution, accidents, and poor efficiency. Many people every day are the victims of this poor management of tremendous traffic. Since many years, there had been some automation in managing the traffic namely Electronic Toll Collection (ETC), Electronic parking payment, normal traffic information etc. However, there are little efforts for making the system more advanced. Recently, several kinds of research are being launched by many countries to develop Intelligent Transport System (ITS), with the objectives to minimize congestion, ensure better safety, reduce air pollution etc. ITS are planned to establish robust communication between vehicle to vehicle (V2V), vehicle to pedestrian (V2P), vehicle to infrastructure (V2I), and vehicle to network (V2N). Initially, for communication links ITS, deploys Wi-Fi network, but because of limited capacity and huge requirement, some links use 5.8 GHz radio frequency for such purposes. IEEE, International Telecommunications Union (ITU) and other advanced research organisations are studying 700 MHz band and mm frequency bands for advanced ITS. ITS is poised to use Information & Communication Technology (ICT) networks for such purposes. ITU has established Study Groups/study questions for addressing ITS issues. The World Radio Conference (WRC-2019) has made a Recommendation 208 regarding harmonization of frequency bands for ITS applications. This paper presents a comprehensive overview of ITS, its applications and analysis etc. The radio frequency spectrum aspects and role of 5 G in ITS are also described in detail.

Optimization of Opening Scheme of ETC/MTC Toll Lane Based on Cost and Benefit Analysis

In recent years, with the popularization of electronic toll collection systems (ETC), expressway toll stations in China have developed into a mixed toll form jointly deployed by ETC toll lanes and MTC toll lanes, presenting a development trend based on ETC toll lanes. Since the toll collection system has the important function of “credit repayment“, the relationship between operation benefit and cost will become an important indicator for measuring the toll station system. Taking the minimum cost and the maximum benefit of the toll station under normal operating condition as the optimization goals, a multi-objective nonlinear optimization model is established within the seek for balance between cost and benefit, so as to obtain an open plan with better toll lanes. The Nanjing-Hangzhou toll station is used as an example to verify the model. Furthermore, based on the trend of the future traffic flow and the increase in the proportion of ETC vehicles, the distribution law of the optimal opening schemes of the toll lane under different traffic volumes and different traffic flow structures is analyzed, which provides experience support for the construction and operation of toll stations in the future.

Effects of the manual and electronic toll collection systems on the particulate pollutant levels on highways in Taiwan

Few studies have quantified the effects of particulate air pollution reductions on electronic toll collection (ETC) and manual toll collection (MTC). Our study evaluated the levels of particulate pollutants, including particulate polycyclic aromatic hydrocarbons (p-PAHs), PM1.0, PM2.5, and PM10, from MTC and ETC systems in booths at a manual/electric hybrid toll station on a highway in Taiwan. A two-week sampling campaign was conducted at the toll station on the first highway in the Taipei metropolitan area. Indoor tollbooth monitoring samples were collected for the ETC and MTC lanes to assess the hourly p-PAH, PM1.0, PM2.5, and PM10 concentrations. The hourly average particle concentration was calculated for different types of toll collection based on 100 vehicles per hour. The average traffic density in the truck and bus lanes or car and van lanes with ETC was significantly higher than that with MTC. The mean p-PAH, PM1.0, and PM2.5 concentrations in the car and van lanes with MTC were higher than those for ETC. The increases in PM1.0 and PM2.5 concentrations per 100 vehicles in the bus and truck lanes with MTC were approximately 2 times greater than those for ETC. ETC technologies serve as a solution to traffic delay problems at toll stations in Taiwan and may more homogeneously reduce the levels of p-PAHs, PM1.0, and PM2.5 particulate pollutants from traffic emissions than the MTC along highways.

Novel Battery-Less Sustainable Energy Harvester Scheme for On-board Electronic Units

With the rapid development in smart vehicles, the on-board unit is established for communication between the vehicle and toll collection booth for toll collection. However, providing a continuous power supply to such units has been a challenge. While features like compactness, low cost and simplicity are to be maintained, the accuracy and performance of the device are to be maintained intact. This paper proposes a battery-less energy harvester scheme as a solution to this power issue. Maximum energy transmission Several researches are conducted on obtaining maximum energy transmission through performance optimization in this domain. This paper provides maximum performance while minimizing the energy harvester transponder dimensions. The energy transmitted to the transponder is maximized considering the optimal source impedance. This provides power to the battery-less structure. The radio frequency to DC rectifier is provided with an optimal source impedance with the help of a small patch antenna. Utilization of a lossy matching network can be avoided through this technique. Inter-stage matching network is used for comparison of the function. The energy efficiency using the proposed scheme is increased by a factor of 10% when compared to the existing schemes.

Robust license plate signatures matching based on multi-task learning approach

Identifying a car by its License Plate (LP) is a critical task in many applications, such as travel time estimation, vehicle re-identification, automatic toll collection, etc. Therefore, matching them must be as accurate as possible. This research proposes a novel deep neural network based method and its learning strategy for LP matching (LPM), which is originally from the cognitive-psychology-inspired unified objective function. The proposed method uses a deep Convolutional Neural Network (CNN) model to extract effective visual signature of the LP image. It exploits the multi-task learning approach to optimize the model, which combines two different tasks: (a) parallel letters recognition to transcribe the image-text contents and (b) image classification to classify the distinct LPs. Moreover, it takes profit from the use of image augmentation techniques. The proposed method is evaluated on three datasets of different characteristics. One of them was collected in this research and will be released publicly. The obtained results show that the proposed method performs better than the state-of-the art methods based on the commonly used evaluation metrics and computation time.

Developing Policy Framework of Dynamic Toll Pricing in India

There are 543 toll plazas across the National Highways and State Highways in India and most of them are currently operating under Manual Toll Collection (MTC) system. The Government of India has recently adopted Electronic Toll Collection (ETC) system over MTC system, but, due to the technical constraints of toll plazas, they are either operating with both ETC and MTC systems or with MTC system only. The motive of switching from MTC to ETC is to reduce congestion and delay time. However, due to the fix toll charges for a vehicle type and heterogeneity in traffic mix, the peak hour traffic causes congestion that ultimately returns to enormous delay to users. The present study proposes the policy framework of Dynamic Toll Pricing (DTP) that alleviate the congestion at toll plazas by consisting of shifting traffic volume from peak/congested hours to off-peak/non-congested hours. This type of pricing proved to be one of the methods to control the traffic by elevating the toll in peak hours and thus giving discounts in off-peak hours. Such type of study about dynamic toll is not yet carried out for Indian scenario where traffic is highly heterogeneous. Hence, the main goal of the present study is to evaluate the DTP for the Indian condition and thus to examine the effect of discount and time saving on the shift in peak hour traffic demand. The observations revealed that most of the respondents are willing to shift in the morning peak hour than evening peak hour. The maximum shift was observed for small cars and for 25 % discount in toll charges. Further, the price elasticities of demand are calculated and are found to vary between -0.03 and -1.19 i.e. with 1 percent of discount the traffic volume changes between 0.03 % to 1.19 %. The output of the present study may set up a framework for DTP in India and may be used for analysing the travel choice behaviour models in future studies.

Expressway Exit Traffic Flow Prediction for ETC and MTC Charging System Based on Entry Traffic Flows and LSTM Model

The Expressway (controlled-access highways) of China is the longest in the world and plays an important role in people’s daily life. Accurate short-term traffic prediction is essential for travel schedule and active traffic management. There are two coexisting charging systems for expressway in China, Electronic Toll Collection (ETC) and Manual Toll Collection (MTC), which have different passing capacity and variation pattern. In this work, we demonstrate that the exit traffic flow prediction at Shanghai Xinqiao toll station using entry traffic flows from multiple close-related stations with Long Short-Term Memory (LSTM) model. Based on the origin-destination (OD) traffic data of a month, we present a new method to predict the exit station’s traffic flow in the future 5 minutes. After deleting abnormal data, we select 12 of the 109 entry toll stations for the experiment. The traffic flow of these 12 entry stations account for 86% of the total exit traffic flow. This method uses the spatial-temporal matrix to deal with different three scenes that are ETC and MTC charging systems individually, the mix of ETC and MTC. We use the LSTM model with various lengths of flow sequence and amounts of hidden layer neurons for three different scenes. Lastly, we validate our model and carefully select the hyperparameters for better prediction accuracy by three evaluation metrics. The experimental results demonstrate that predicting the ETC is the best in the three scenes.

Vehicle License Plate Recognition

One of the most significant parts of integrating computer technologies into intelligent transportation systems (ITS) is vehicle license plate recognition (VLPR). In most cases, however, to recognize a license plate successfully, the location of the license plate is to be determined first. Vehicle License Plate Recognition systems are used by law enforcement agencies, traffic management agencies, control agencies, and various government and non-government agencies. VLPR is used in various commercial applications, including electronic toll collecting, personal security, visitor management systems, parking management, and other corporate applications. As a result, calculating the correct positioning of a license plate from a vehicle image is an essential stage of a VLPR system, which substantially impacts the recognition rate and speed of the entire system. In the fields of intelligent transportation systems and image recognition, VLPR is a popular topic. In this research paper, we address the problem of license plate detection using a You Only Look Once (YOLO)-PyTorch deep learning architecture. In this research, we use YOLO version 5 to recognize a single class in an image dataset.

Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems

In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition‐based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real‐time road traffic conditions. Deep learning‐based classification systems have been proposed to incorporate the above‐mentioned issues in traditional methods. However, convolutional neural networks require piles of data including noise, weather, and illumination factors to ensure robustness in real‐time applications. Moreover, there is no generalized dataset available to validate the efficacy of vehicle classification systems. To overcome these issues, we propose a convolutional neural network‐based vehicle classification system to improve robustness of vehicle classification in real‐time applications. We present a vehicle dataset comprising of 10,000 images categorized into six‐common vehicle classes considering adverse illuminous conditions to achieve robustness in real‐time vehicle classification systems. Initially, pretrained AlexNet, GoogleNet, Inception‐v3, VGG, and ResNet are fine‐tuned on self‐constructed vehicle dataset to evaluate their performance in terms of accuracy and convergence. Based on better performance, ResNet architecture is further improved by adding a new classification block in the network. To ensure generalization, we fine‐tuned the network on the public VeRi dataset containing 50,000 images, which have been categorized into six vehicle classes. Finally, a comparison study has been carried out between the proposed and existing vehicle classification methods to evaluate the effectiveness of the proposed vehicle classification system. Consequently, our proposed system achieved 99.68%, 99.65%, and 99.56% accuracy, precision, and F1‐score on our self‐constructed dataset.

Cryptographic Solution-Based Secure Elliptic Curve Cryptography Enabled Radio Frequency Identification Mutual Authentication Protocol for Internet of Vehicles

Internet of Vehicles (IoV) is one of the most active research disciplines in Intelligent Transportation Systems (ITS), intending to improve Vehicular Ad-hoc Network (VANET) capabilities. The main objective of IoV is to enhance the safety of passengers by incorporating various advanced information and communication technologies thus, easing the driving experience of passengers and enhancing traffic efficiency. The IoV has numerous key technologies, and one of them is Radio-Frequency Identification Technology (RFID) which has a plethora of applications in IoV like automatic toll collection, intelligent parking, data dissemination, tracking the location of the vehicle, etc. which enhances the overall performance of IoV networks. Along with this, RFID devices are resource-constrained, thus security and privacy are a major concern and also IoV is a real-time sensitive network where security is of utmost importance. Keeping in mind the security perspective, the concept of Elliptic-Curve Cryptography (ECC) is taken into consideration. So, in this paper, we have proposed a Cryptographic solution-based secure ECC-enabled RFID mutual authentication protocol for IoV. The proposed protocol is comprised of three phases: Setup Phase, Tag Authentication Phase, and Server Authentication Phase. Security evaluation of the proposed protocol is performed by taking into consideration the analysis of security requirements as well as security attacks. Also, the simulation of the proposed protocol is done using the AVISPA tool and the results indicate that the proposed protocol is safe against various malevolent attacks. Performance evaluation of the proposed protocol is computed based on parameters i.e., storage requirements, communication cost, and computational cost. Results indicate that the proposed protocol contributes to high performance and security and has low computational cost than other existing authentication protocols. A novel Blockchain-based security framework for RFID-enabled IoV has also been proposed to further enhance the security of the IoV network.

Automatic Number plate detection for law enforcement using optical recognition system

Automatic Number Plate Recognition (ANPR) is a specialized type of Optical Character Recognition System (OCR). It is a method of reading a vehicle's license plate using OCR to create vehicle registry or location data. ANPR is utilized by a variety of agencies around the world to enforce the law, including determining whether a vehicle is registered or not. Government entities, such as highways agencies, can categorize traffic movements for computerized toll collection. Images of the text from the license plate can be stored and processed using the ANPR system. Infrared cameras are often employed to take photographs in any lighting condition, whether it is day or night. To be more accurate ANPR technology should also consider plate variations from place to place.

Research of ETC with GPS/BDS High-speed Charging and Navigation System

Aiming at the disadvantages of ETC electronic charging compared with traditional manual charging, a ETC charging method based on GPS/BDS combined system is proposed. In view of the vehicle in the process of driving will be due to a variety of reasons in the road through the gantry record omission and other circumstances, resulting in the exit billing failed to charge according to the actual driving path; Because ETC electronic toll collection system is implemented by network technology under the environment of information technology, in the case of network technology problems, ETC electronic toll collection system will stop working phenomenon, thus affecting the efficiency of the highway; For vehicles on business trips, the ETC channel is cumbersome to charge and reimburse. At present, manual toll channels are still used for high-speed bus trips. At present, there are still some technical loopholes in the ETC electronic charging system. Criminals steal or rub others' ETC cards. When the vehicle enters the ETC sensor area and opens the railing, they will rub others' ETC by following closely or changing lanes instantly, which will affect the interests of the owners and lead to safety accidents and other problems. The GPS/BDS chip is installed on the ETC device. According to the real-time path information and preset navigation route of the charging vehicle, the precise positioning of the charging vehicle is realized to achieve accurate charging. According to the observed vehicle density of road conditions, select the path that is not easy to jam vehicles to achieve accurate navigation; Judge whether the real-time location information of the vehicle is in the target navigation route, so that when the vehicle takes a wrong route, it can quickly return to the correct route and reduce the high-speed charge.

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Optimized Design of Multilines Center of Subway AFC System via Distributed File System and Bayesian Network Model

Automatic fare collection system (AFCS) is a modern, automatic, networked toll collection system for rail transit ticket sales, collection, billing, charging, statistics, sorting, and management. To realize the subway transit networking operation, this paper designs the subway AFCS based on a distributed file system (DFS), namely, Gluster File System (GlusterFS). Firstly, the multiline center (MLC) in the subway AFCS is designed to analyze the status and current situation of distributed file processing in subway MLC system; secondly, the relevant technical theories are summarized, the Bayesian Network (BN) theoretical model and DFS are explored, and the principles of four DFS are comparatively analyzed; thirdly, the architecture and cluster mode of GlusterFS is expounded, and then based on GlusterFS, the architecture of subway AFCS is discussed. This paper presents several innovation points: first, the subway AFCS is designed based on GlusterFS by analyzing and aiming at the functional requirements, performance requirements, and safety requirements of the MLC subway system; second, the safety risk analysis (SRA) of AFCS is conducted from six security requirements, and a Web scanning system is designed to ensure the system data security. Finally, the design scheme is used to analyze the subway passenger flow and power consumption. The results demonstrate that the design scheme can competently adapt to three different application scenarios. Through comparison of two deployment modes of the Web scanning system, the data security Web scanning system can ensure the safe operation of the AFCS. Furthermore, the statistical analysis of subway passenger flow and power supply data shows that the proposed scheme can support the smooth operation of the subway system, which has significant practical value.

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Discrete-event modelling of the capacity of the toll collection exit point and the formation of congestion

Modeling Time-Varying Variability and Reliability of Freeway Travel Time Using Functional Principal Component Analysis

This paper presents an aproach for modeling travel time variability and reliability that accounts for time-of-day effects on travel time variations. The conditional probability density functions of travel time in each specific period play a fundamental role in the modeling of time-varying variability and reliability. Nonparametric kernel density estimation is applied to travel time data, which enhances the flexibility and accuracy in accommodating travel time distributions under various complex traffic conditions. The proposed functional travel time density model (FTTDM) uses functional principal component analysis in kernel density function estimates for modeling and depicting time-varying variability through the dependence on the departure time of day. The resulting quantile estimates are used to obtain time-varying reliability, which considers the addition of extra time to the average or the median for an on-time arrival. This study uses linked travel time data of an electronic toll collection system to estimate route travel times in the Taiwan Freeway system. As illustrated in the data applications, the FTTDM effectively captures the time-varying feature of travel time variability and reliability, and the reliability indicators identify the unreliable departure time of day of a route.