Operational planning for electronic toll collection: a unique approach to computer modeling/analysis

Traffic simulation models are used to enhance planning, design, operation, and management of transportation facilities. A discrete-event stochastic object-oriented microscopic simulation model is presented that was specifically developed to evaluate the operational performance of toll plazas. Traffic behavior is represented using a set of mathematical and logic algorithms that control the conflicts among vehicles within the toll plaza area. Modified versions of car-following and lane-changing algorithms and a new toll-lane selection algorithm are integrated into this new model to simulate traffic operation at toll plazas. The model output includes measures of effectiveness (MOEs) that can be used to evaluate the performance of existing and future individual toll lanes and the entire toll plaza system. Real-world data collected at the busiest toll plaza in the Orlando–Orange County Expressway Authority system were used to validate the developed model. Statistical tests indicate that there is no significant difference at the 95 percent confidence level between MOEs obtained from the model and those collected in the real world. Sensitivity analysis of market penetration of the electronic toll collection (ETC) system indicates that an increase in ETC subscription rate improves the efficiency of toll plaza operation. The benefits of ETC depend on the specific plaza configuration. One of the most interesting results of this study is that for all plaza configurations simulated with manual payment lanes operating over capacity, total plaza delay can be reduced by half if only 10 percent of the users switch from manual payment lanes to ETC lanes.

Little research has been conducted to evaluate the traffic safety of toll plazas and the impact of electronic toll collection (ETC) systems on highway safety, but analyses indicate that toll plazas do contribute to traffic accidents. Traffic safety issues related to toll plazas and ETC systems were studied using the 1999 and 2000 toll plaza traffic accident reports of the Central Florida expressway system. The analysis focused on accident location with respect to the plaza structure (before, at, after plaza) and driver injury severity (no injury, possible, evident, severe injuries). Two well-known artificial neural network (ANN) paradigms were investigated: the Multi-Layer Perceptron and Radial Basis Functions neural networks. The performance of ANN was compared with calibrated logit models. Modeling results showed that vehicles equipped with ETC devices, especially medium/heavy-duty trucks, have higher risk of being involved in accidents at the toll plaza structure. Also, main-line toll plazas have a higher percentage of accident occurrence upstream of the toll plaza. In terms of driver injury severity, ETC users have a higher chance of being injured when involved in an accident. Older drivers tend to have higher risk of experiencing more severe injuries than younger drivers. Female drivers have a higher chance of experiencing a severe injury than do male drivers.

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.

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.

The present manual toll collection system of Sri Lankan expressways is expected to be inadequate to serve the forecasted traffic demand on most interchanges. High waiting times at the manual toll booths would directly affect the expressway user delay and the level of service. Therefore, an efficient tolling system will have to be introduced. This study is focused on developing a methodology to evaluate the operational improvements of using electronic toll collection incorporating the traffic characteristics of Expressways in Sri Lanka. A micro-simulation method using a commercially available software; PTV Vissim, was adopted to conduct a scenario analysis for a forecasted traffic demand at the Kottawa interchange as a case study. A calibrated sample model was developed and its simulation results show that the electronic toll collection system can reduce the average per vehicle delay and increase the toll plaza capacity up to 50% during peak hour if the proper toll plaza configuration is provided. The findings of the study are beneficial to toll station operators as well as for planning for toll station upgrading by assessing its optimum capacity.

Highway vehicle emissions can result in adverse health problems to nearby residents and workers, especially during traffic congestion. In response, the policy to implement electronic toll collection (ETC) has helped alleviate traffic congestion, as compared to manual toll collection (MTC) and has led to reduced air pollution and improved public health. However, the effect of ETC in reducing particulate matter polluting the air is not well understood, especially in the ultrafine particle (UFP) range (particle diameter <100 nm). To the best of our knowledge, this is the first study to investigate how ETC affects the traffic pattern and air quality, especially UFP and PM2.5. We selected a site in Tainan, Taiwan, and measured UFP and PM2.5 concentrations before and after the construction of the ETC system. The computed traffic volumes during peak travel periods (7:00 AM to 9:00 AM and 4:00 PM to 6:00 PM) respectively, accounted for approximately 23-25% and 14-18% before and after the implementation of ETC, indicating that peak traffic volumes were more homogeneous after ETC. Moreover, the results indicate that the full implementation of ETC can help reduce UFP number concentrations and PM2.5 mass concentrations in the highway downwind area by 4 × 103 #/cm3 and 20.5 μg/m3, respectively. After the full implementation of the ETC, significant reductions in both the UFP number concentration and PM2.5 mass concentration were seen. Furthermore, excessive lifetime cancer risks (ELCR) from exposure to PM2.5 and UFP together were reduced 49.3% after the implementation of the ETC. Accordingly, ETC not only helps alleviate traffic congestion but also reduces traffic emissions and lifetime cancer risk for people living or working near highways.

Exploratory Study on Approaches for Traffic count Prediction; Using Toll-Way Traffic Count

The environmental outcome of the Electronic Toll Collection (ETC) system is an important aspect in evaluating the impacts of the ETC system, which is influenced by various factors including the vehicle type, travel speed, traffic volume, and average queue length of Manual Toll Collection (MTC) lanes. The primary objective of this paper is to develop a field data-based practical model for evaluating the effects of ETC system on the fuel efficiency and vehicle emission. First, laboratory experiments of seven types of vehicles under various scenarios for toll collection were conducted based on the Vehicle Emissions Testing System (VETS). The indicator calculation models were then established to estimate the comprehensive benefit of ETC system by comparing the test results of MTC lane and ETC lane. Finally, taking Beijing as a case study, the paper calibrated the model parameters, and estimated the monetization value of environmental benefit of the ETC system in terms of vehicle emissions reduction and fuel consumption decrease. The results shows that the applications of ETC system are expected to save fuel consumption of 4.1 million liters and reduce pollution emissions by 730.89 tons in 2013 in Beijing.

This paper examines the deployment of electronic toll collection (ETC) and develops a model to maximize social welfare associated with the toll plaza.

Influences of traffic volumes and wind speeds on ambient ultrafine particle levels—Observations at a highway electronic toll collection (ETC) lane

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.

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

Methodology for determining ecological benefits of advanced tolling systems

Electronic Toll Collection (ETC) serves a major function in an Intelligent Transportation System (ITS). Recently, ETC has been widespread and prevalent within our national ITS infrastructure construction. However, with the advanced application of new technologies in ETC, people have to face lots of privacy issues. Based on privacy concerns and issues, the paper first analyzes privacy risks in ETC system and then conducts research on technical solutions to and legal strategies for privacy protection. Finally, the paper proposes suitable suggestions for solving privacy issues and risks in our domestic ETC system.

An electronic toll collection (ETC) system is one of the most important parts of an intelligent transportation system (ITS), but existing ETC systems are not efficient and have vehicle fee evasion complications. Owing to the traceability and tamper resistance of a blockchain, the combination of a blockchain and ETC system is a feasible way to solve the above problems. However, traditional blockchain (e.g., Bitcoin and Ethereum) has high power consumption and low efficiency. Hence, it cannot be used in ETC systems with high throughput and is not suitable for power-constrained Internet of Things (IoT) devices. In this study, therefore, we propose a blockchain architecture for the ETC system. To curb vehicle fee evasion behavior, thereby reducing the economic loss caused, we propose a vehicle behavior management mechanism based on credit value. Furthermore, to reduce the burden of storage and build a chain of evidence for auditing purposes, we propose an evidence chain framework. To protect the data security in the transaction process, we designed a data protection method to encrypt the transaction data. Additionally, this system is based on an open-source alliance blockchain framework called Hyperledger Fabric, which is more in line with the current application scenarios of ETC systems. We implemented the system on a Raspberry Pi and carried out simulations. The comprehensive evaluation results and analysis show that the system effectively reduces the number of illegal acts, completes the evidence inspection, and improves the security of the data.