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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Prospective Environmental and Economic Impacts of the Implementation of Free-Flow Open Road Tolling (ORT) in Greece</title>
      <link>https://trid.trb.org/View/2579497</link>
      <description><![CDATA[This paper investigates the differences in the environmental and economic impacts of applying a prospective free-flow Open Road Tolling (ORT) system in Greece, compared with conventional tolling focusing on Road Freight Transport (RFT). The sector of RFT highlights the negative impact of frequent stops for toll payment: increased travel time, reduced road safety, high traffic accident risk, fuel consumption, air pollutant emissions, etc. This research includes a comprehensive international comparison of relevant systems, providing a global perspective on tolling practices. The environmental and economic impacts on a local scale are estimated, and, in addition, there is a rough estimation of the annual cost of fuel consumption of RFT due to the non-application of ORT in Greece, aiming to highlight the need for implementation of ORT in Greece. Only using RFT fuel consumption cost during the year 2017, while the diesel cost was much lower than today, and the same goes for the number of toll stations in Greek motorways, it was found that each year 15 million euros more are spent due to the absence of free-flow ORT in Greek motorways. This amount probably has already tripled.]]></description>
      <pubDate>Wed, 12 Aug 2026 17:07:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579497</guid>
    </item>
    <item>
      <title>Real Time Road Performance Prediction Using Multilayer Perceptron Fusion of ETC and Connected Vehicle Data</title>
      <link>https://trid.trb.org/View/2752108</link>
      <description><![CDATA[Connected vehicles (CV) are key components of cooperative intelligent transportation systems. As CV penetration rates and roadside communication coverage gradually increase, government agencies face growing infrastructure investment needs. By fusing CV with existing electronic toll collection (ETC) data, road performance prediction accuracy can be enhanced, potentially enabling better resource allocation. This study employs multilayer perceptron neural networks to fuse CV and ETC data to predict real time average speeds over freeway segments. Statistical regression is adopted to analyze the effect of variables, including penetration rate, coverage rate, data transmission frequency, crash response time, and demand level, on prediction accuracy. Training, validation, and testing data for the data fusion model are synthesized using a calibrated simulation model. The results show that when CV communication coverage is below 100%, the fusion of CV and ETC data achieves higher prediction accuracy than using either data source alone. As CV penetration rates increase, improving communication coverage gradually becomes more critical. Finally, logistic regression is developed to provide guidance for management agencies to determine the appropriate timing for implementing the data fusion approach.]]></description>
      <pubDate>Tue, 11 Aug 2026 10:13:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752108</guid>
    </item>
    <item>
      <title>Electronic tolling-FASTag technology in transport is a game changer in climate action: A sustainable policy towards Net-Zero in India</title>
      <link>https://trid.trb.org/View/2694721</link>
      <description><![CDATA[On-road transport is a major contributor to the country's air pollution profile. Envisioned reaching Net-Zero by 2070 and curbing air pollution, Government of India's initiative to adopt FASTag (Fast and Secure Transmission of Highway Tags) technology is revolutionary in the toll collection system in transport ecosystem. This is the first-ever national-level study to investigate the impact of FASTag policy on mitigating vehicular emissions near toll plazas across India. The emission reduction is assessed and estimated using the Intergovernmental Panel on Climate Change (IPCC) defined bottom-up approach for 2025. The estimated emissions findings highlight that there is a significant reduction varying up to ∼7% for PM2.5, PM ₁₀, CO, NOₓ, VOC, SO₂, BC, OC and CO₂ emissions due to the FASTag as compared to traditional toll collection system, where the highest reduction comes from the regions like Indo-Gangetic plain, Western and South-Eastern India. With the implementation of advanced Global Positioning System (GPS) based toll collection systems in the forthcoming year, it will further reduce emissions by another ∼4%. Heavy commercial vehicles (HCVs) are the largest contributor to emissions and are likely to contribute the largest portion to the reduction of pollutants. This research sensitises the role of policy-induced advanced sustainable transport technology to achieve Net-Zero.]]></description>
      <pubDate>Fri, 31 Jul 2026 15:17:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694721</guid>
    </item>
    <item>
      <title>Integrating Real-Time Monitoring Information into Asset Failure Modeling: Application to ETC Facilities</title>
      <link>https://trid.trb.org/View/2672631</link>
      <description><![CDATA[Infrastructure assets, such as expressway facilities, are sometimes monitored in real time to detect anomalies or failures. Although such monitoring systems are primarily intended to enable immediate responses by detecting anomalies or failures in real time, an additional potential use lies in accumulating information continuously obtained from the system and leveraging it to improve the accuracy of failure or deterioration estimation for the asset. In this study, we specifically consider scenarios in which real-time monitoring generates alerts upon detecting anomalies, and these anomalies are classified as failures if they do not spontaneously resolve. Using historical alert information from such systems, we aim to enhance the accuracy of failure-process modeling for infrastructure assets. Specifically, we propose a methodology that defines a novel lifetime indicator as a function of elapsed time since asset installation and alert-related statistics. By applying a Weibull hazard model to this lifetime indicator, the proposed approach enables a straightforward and analytically tractable quantification of differences in failure probabilities based on the alert history. The proposed model is applied to actual electronic toll collection (ETC) toll booth servers, demonstrating that an increase in cumulative alert duration significantly increases asset failure probabilities, in some cases approximately doubling them.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:02:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672631</guid>
    </item>
    <item>
      <title>Analysis of a simulation model of a toll plaza at the exit of a regional toll road</title>
      <link>https://trid.trb.org/View/2664457</link>
      <description><![CDATA[Toll road projects implemented on the basis of barrier-type toll collection systems remain relevant despite the availability of alternative toll collection technologies. Such infrastructure projects are implemented not only to connect large urban agglomerations, but also to solve local transportation problems in individual regions. The present study is devoted to the analysis of the developed simulation model of a toll plaza at the exit of a regional toll road section. The paper describes the results of the study of the updated simulation model, which takes into account additional parameters of traffic flow compared to the previously developed simulation models implemented by the authors at the previous stages of research. The analysis of the throughput capacity of the developed toll plaza simulation model is performed, the peculiarities of the toll plaza functioning and traffic flow under conditions of relatively low popularity of electronic toll collection in the region are noted. Modeling of conditions for increasing the share of electronic toll collection on-board units use in the traffic flow has been performed, and recommendations for changing the configuration of the toll collection system have been given. The work of simulation model in the mode of changing daily intensity is analyzed. Optimization of traffic flow parameters to ensure the passage of the maximum design values of daily intensity through the toll plaza is performed.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664457</guid>
    </item>
    <item>
      <title>Channel Measurement, Characterization, and Utilization of the ETC Systems in All-Metal Immersed Tunnel Scenario</title>
      <link>https://trid.trb.org/View/2591197</link>
      <description><![CDATA[The channel characteristics of immersed tunnels play a crucial role in the design and optimization of electronic toll collection (ETC) communication systems in such environments. Due to the lack of channel characteristics for all-metal immersed tunnel environments, this paper conducts an in-depth study on channel measurement, channel modeling, and optimization of ETC systems within all-metal immersed tunnels. Using a channel measurement system based on the universal software radio peripheral (USRP), this study extracts multidimensional channel characteristic parameters from the measured data, such as path loss (PL), shadow fading (SF), K-factor (KF), root mean square delay spread (RMS DS), power angular spectrum (PAS), and azimuth angle spread (ASA). The unique characteristics of the all-metal tunnel are analyzed in comparison to tunnels made of other materials. To develop a comprehensive all-metal immersed tunnel channel model meeting the quasi-deterministic radio channel generator (QuaDRiGa) requirements, we employed ray tracing (RT) methods to supplement angle domain parameters. Subsequently, we used this channel model to aid in the design and optimization of ETC systems, addressing transaction failures and erroneous transactions within all-metal immersed tunnels by establishing optimization targets and constraints, resulting in optimal height, tilt angle, transmission power, and beamforming. The findings of this research not only enrich the channel scenarios in QuaDRiGa but also provide practical guidance for improving the performance of ETC systems.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591197</guid>
    </item>
    <item>
      <title>Calculation Method and Case Verification of Expressway Actual Capacity Based on ETC Gantry Data</title>
      <link>https://trid.trb.org/View/2613132</link>
      <description><![CDATA[A generative adversarial network (GAN) is used to repair the missing expressway section flow data, and a method of calculating expressway capacity based on electronic toll collection (ETC) gantry data is proposed. Taking 27 expressways in the Guangdong-Hong Kong-Macao Greater Bay Area as examples, the results show that under the condition of speed limits, some expressways near the city have higher traffic efficiency, and the capacity can be improved by more than 15% in a more stable operating environment. The proportion of trucks will have an effect of first increasing and then decreasing on the actual capacity of expressways. Reasonable control of the proportion of trucks to less than 21.54% during peak hours can help improve the traffic efficiency of expressways.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613132</guid>
    </item>
    <item>
      <title>Real-Time Crash Risk Prediction of Highways Based on Electronic Toll Collection Big Data</title>
      <link>https://trid.trb.org/View/2613000</link>
      <description><![CDATA[Real-time crash risk prediction is crucial for proactive traffic safety management. Leveraging large-scale Electronic Toll Collection (ETC) data from highways, we developed a comprehensive traffic flow indicator system encompassing flow, speed, occupancy, and proportion, from which we extracted indicator features under pre-crash and non-crash traffic conditions. Subsequently, XGBoost was used to construct a section-based real-time crash risk prediction model. A case study was conducted on a highway ring road in Guangdong Province, China. To address data imbalance, SMOTE oversampling, NearMiss undersampling, and SMOTE-Tomek hybrid sampling were applied. The results show that (1) the constructed model demonstrates good performance; (2) under imbalanced data conditions, both SMOTE oversampling and SMOTE-Tomek hybrid sampling significantly improve model performance; and (3) the crash risk in traffic flow is primarily influenced by traffic flow in upstream and downstream sections, the proportion of truck flow in the downstream section, and the speed of upstream and downstream sections.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613000</guid>
    </item>
    <item>
      <title>Applying an improved calibration method in the safety evaluation framework for the open tolling system</title>
      <link>https://trid.trb.org/View/2614543</link>
      <description><![CDATA[This study proposed an improved calibration method in the safety evaluation framework and analysed the safety effect of installing the open road tolling (ORT) system. Although the ORT system in South Korea has recently installed in many tollgate sections, it is impossible to collect statistically significant crash data. Therefore, the safety benefits were estimated and compared by (1) applying the general calibration way suggested in the Highway Safety Manual, (2) the recent calibration research from Florida and (3) the improved calibration method suggested in this study. In the result, safety improvement effect of ORT installation was 43% to 51%. Additionally, the calibration function using the quadratic model showed the best performance. This study is meaningful in that the safety effectiveness was estimated using the calibration method when the sample size was insufficient, and it utilises new functions to predict nonlinear relationships that are difficult to calibrate with simple linear relationships.]]></description>
      <pubDate>Wed, 28 Jan 2026 14:41:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614543</guid>
    </item>
    <item>
      <title>Real-time highway crash prediction based on SHAP-RFECV and electronic toll collection data: A new feature selection strategy</title>
      <link>https://trid.trb.org/View/2644115</link>
      <description><![CDATA[Real-time crash prediction has emerged as a critical area of research in traffic safety, aiming to improve safety performance through proactive crash anticipation and management strategies. However, the accuracy and reliability of such prediction models largely depend on the selection of traffic features and prediction algorithms. Although numerous features have been investigated, they are often distributed across varying temporal and spatial dimensions and exhibit strong correlations, which can increase model variance and the risk of overfitting. Moreover, the selection and combination of features significantly affect both model performance and interpretability. To address these challenges, this study proposes a SHAP-RFECV feature selection algorithm that integrates SHapley Additive exPlanations (SHAP) values into the Recursive Feature Elimination with Cross-Validation (RFECV) framework. This method enhances feature selection by identifying the optimal subset of features most relevant to real-time crash prediction. Using a comprehensive dataset derived from the electronic toll collection system in Guangzhou, China, various machine learning models including extreme gradient boosting (XGBoost), categorical boosting, and light gradient boosting were trained and evaluated to demonstrate the effectiveness of the proposed algorithm. The results show that the XGBoost outperformed in terms of out-of-sample prediction, yielding an area under the curve of 0.8350, a recall of 73.12%, a false alarm rate of 18.77%, and an accuracy of 79.54%. Compared with the RFECV strategy, the number of optimal features selected by the XGBoost was substantially reduced from 47 to 21 under the SHAP-RFECV. Four benchmark models, i.e., logistic regression, long short-term memory, convolutional neural network, and support vector machine, were developed to further validate the effectiveness of the feature selection method. The results demonstrate that the proposed SHAP-RFECV algorithm outperformed traditional feature selection methods such as RFECV, least absolute shrinkage and selection operator, Boruta, and permutation importance in terms of both predictive accuracy and interpretability. By applying SHAP, the authors systematically measured the importance and interaction effects of risk-related features. Notably, features related to the average speed in the 5 to 10 min prior to a crash were identified as the most critical. Features such as the proportion of truck flow and speed standard deviation were also found to have significant and nonlinear impacts on crash occurrence.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644115</guid>
    </item>
    <item>
      <title>Level of Service Thresholds for Electronic Toll Collection Lanes at Toll Plazas</title>
      <link>https://trid.trb.org/View/2601675</link>
      <description><![CDATA[Present study evaluates the Electronic Toll Collection System (ETC) commonly known as FASTag in India considering service headway as a measure of effectiveness under mixed traffic conditions. Service headway is composed of processing time (includes reading the FASTag electronic chip a vehicle) and clearance time of the vehicle. Video graphic data were collected at three different toll plazas located on National Highways to capture the variability in traffic and human behaviour. The results showed that as compared to the Manual Toll Collection (MTC) system, the average service headway in FASTag lanes is reduced by 48.43 percent. Service headway is found minimum for Small Car (SC) and the maximum for Trailer. The leader-follower pair wise service headway variation is also analysed (SC as a leader and a follower), and it is observed that the average service headway is lowest for SC-SC pair and the highest for SC-Trailer pair. The effect of the presence of boom barrier at tollbooth is studied for all car conditions based on observed service headway. It is found that service headway is reduced by 39.81% when the boom barrier is removed. Level of service (LOS) thresholds are developed LOS A to LOS F with service headway as a measure of effectiveness using k-means clustering technique. It has been observed that the LOS improved from LOS 'D' to LOS 'B' when boom barrier was removed from the toll lane.]]></description>
      <pubDate>Mon, 08 Dec 2025 11:39:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2601675</guid>
    </item>
    <item>
      <title>Study on the Remuneration of EETS (European Electronic Toll Service) Analysis of Existing Remuneration Schemes in the EU and Relevant Parameters for the Remuneration of EETS</title>
      <link>https://trid.trb.org/View/2556710</link>
      <description><![CDATA[The aim of this comprehensive study is to provide proposals and recommendations for the establishment of a fair remuneration model across various toll domains, considering the interests of toll chargers and service providers. Furthermore, the Study Team recommends that the EETS remuneration model shall adhere to the following principles: The EETS tolling service shall be interoperable and should be cost-efficient. There should be a consensus on a general business service structure for EETS services that can be applied within the existing toll domains. A transparent methodology must be established for the application of these principles across all toll domains.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556710</guid>
    </item>
    <item>
      <title>CAV-dedicated lane sharing strategy of expressway in mixed traffic environment: A novel dynamic lane-level tolling method</title>
      <link>https://trid.trb.org/View/2566089</link>
      <description><![CDATA[In a mixed network environment on expressways, designating dedicated lanes for connected and autonomous vehicles (CAVs) can effectively enhance road capacity. However, at low CAV penetration rates, setting dedicated lanes may cause inefficient use of road resources and potential congestion. To improve the utilization efficiency of dedicated lanes, this paper develops a dynamic tolling strategy for connected human-driven vehicles (CHVs) to use CAV-dedicated lanes through a pay-and-share mechanism. This dynamic tolling strategy involves two primary components. First, an optimized lane-level cell transmission model (CTM) is developed to incorporate the proportion of CHVs willing to pay for CAV-dedicated lanes access, combined with a user equilibrium (UE) model based on heterogeneous time values to allocate traffic flow. Second, a model predictive control tolling (MPCT) strategy is proposed, using the refined lane-level CTM as the predictive model and the dung beetle optimizer (DBO) algorithm to calculate the optimal toll rate sequence. Finally, the effectiveness of the proposed MPCT strategy is evaluated through a comparative analysis against with a feedback control tolling (FCT) strategy and a no control (NC) baseline. Simulation results indicate that the proposed MPCT strategy can significantly improve traffic efficiency and safety while also contributing to reduced fuel consumption.]]></description>
      <pubDate>Thu, 21 Aug 2025 09:19:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2566089</guid>
    </item>
    <item>
      <title>Traffic simulation optimization considering driving styles</title>
      <link>https://trid.trb.org/View/2548021</link>
      <description><![CDATA[Parameter calibration is essential for ensuring the accuracy of microscopic traffic simulations. The expected speed is a critical parameter that characterizes behaviors of vehicles in most simulation models, which is influenced by road traffic conditions and the driving characteristics of different drivers. Most existing parameter calibration methods typically concentrate on micro-level parameters such as time headway and lane change motivation, while overlooking the calibration of vehicle expected speeds in consideration of driver behavior habits. This study combines data from highway electronic toll collection (ETC), gantries, and 100-m mileage average speed data, and proposes a method for calibrating vehicle expected speed that considers driving style clustering. The Gaussian mixture model (GMM) algorithm is used to develop driver models with three distinct driving styles: aggressive, moderate, and conservative. To ensure driving diversity and enhance parameter calibration efficiency, the authors rebuild vehicle driving models and representative parameters based on the classification results. Moreover, the Bayesian optimization algorithm is modified in conjunction with a microscopic traffic simulation model to perform automatic calibration of expected speeds. Experiments conducted on the Shanghai-Hangzhou-Ningbo highway demonstrate that the proposed method significantly reduces the mean absolute percentage error (MAPE) from 20.2% (using default parameters) to 3.1%. Additionally, in the model robustness test, the MAPE reaches 5.01%, indicating a certain level of stability and scalability. This method proposes a tailored calibration method accounting for the heterogeneous driving behaviors of micro-traffic simulation models, achieving satisfactory calibration results for simulation models in highway scenarios.]]></description>
      <pubDate>Thu, 26 Jun 2025 11:42:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548021</guid>
    </item>
    <item>
      <title>Design of Electronic Road Pricing in Jakarta Based on Willingness and Ability to Pay: Addressing Traffic Congestion with Pricing Effectiveness</title>
      <link>https://trid.trb.org/View/2559778</link>
      <description><![CDATA[Congestion in Jakarta is a significant problem that impacts socio-economic aspects. The odd-even policy implemented by the government is considered ineffective because it encourages people to have two vehicles with odd and even license plates so as not to reduce the number of cars on the road significantly. Therefore, the government plans to implement Electronic Road Pricing (ERP), which promotes a more equitable distribution of transportation modes and the reduction of congestion. This research aims to design ERP tariffs that are effective in reducing congestion. This study uses a quantitative approach in data collection, which is then analyzed using a combination of Ability to Pay (ATP), Willingness to Pay (WTP), and Analytical Hierarchy Process (AHP). The results of the study show that the optimal fare is in the range of Rp 18,000 – Rp 22,000 for cars, while for motorcycles, it is in the range of Rp 7,000 – 9,500. The study also showed that the value of ATP > WTP. This imbalance can lead to ineffectiveness, so a pricing strategy must be made so that rates are fairer and more effective for different levels of society. This study highlights the importance of incorporating ATP and WTP analyses in determining fair rate structures. In addition, AHP is important for determining the optimal ERP rate by giving weight to the WTP value factor so that the analysis results are more accurate and objective. With a well-designed tariff, ERP can potentially serve as a viable solution to Jakarta’s congestion problem. Copyright:]]></description>
      <pubDate>Tue, 24 Jun 2025 09:11:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559778</guid>
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