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Keywords = Electronic Toll Collection (ETC)

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36 pages, 314 KiB  
Review
Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
by Yizhe Wang, Ruifa Luo and Xiaoguang Yang
Appl. Sci. 2025, 15(12), 6863; https://doi.org/10.3390/app15126863 - 18 Jun 2025
Viewed by 635
Abstract
Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new opportunities to address these issues. The vast amount of “dormant” ETC data contains [...] Read more.
Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new opportunities to address these issues. The vast amount of “dormant” ETC data contains rich traffic information that urgently needs to be deeply mined and effectively utilized. This paper reviews the research status, key technologies, and development trends of urban traffic state sensing and analysis technologies based on ETC data. In terms of technological development, ETC systems have evolved from simple toll collection tools to comprehensive traffic management platforms, featuring unique advantages such as accurate vehicle identification, extensive spatiotemporal coverage, and stable data quality. ETC data-based traffic sensing technologies encompass traffic state representation at microscopic, mesoscopic, and macroscopic levels, enabling comprehensive sensing from individual vehicle behavior to overall network operations. The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy. In practical applications, ETC data has demonstrated enormous potential in real-time monitoring and signal control optimization, traffic prediction and artificial intelligence technologies, environmental impact assessment, and other fields. Meanwhile, ETC data-based urban traffic management is transitioning from passive responses to proactive prediction, from single functions to comprehensive services, and from isolated systems to integrated platforms. Looking toward the future, the deep integration of emerging technologies, such as vehicle–road networking, edge computing, and artificial intelligence, with ETC systems will further promote the intelligent, refined, and precise development of urban traffic management. Full article
20 pages, 1954 KiB  
Article
Analysis of Nitrogen Dioxide Concentration at Highway Toll Stations Based on fsQCA—Data Sourced from Sentinel-5P
by Shenghao Xu and Xinxiang Yang
Atmosphere 2025, 16(5), 517; https://doi.org/10.3390/atmos16050517 - 28 Apr 2025
Cited by 1 | Viewed by 351
Abstract
The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO2 concentration at key highway toll stations in Zhejiang Province, [...] Read more.
The Fuzzy-Set Qualitative Comparative Analysis (fsQCA) method is employed in this study to investigate the combined effects of region area, the number of COVID-19 infections, and the number of family cars on NO2 concentration at key highway toll stations in Zhejiang Province, China. By selecting and comparing typical cases, the analysis reveals differentiated characteristics in how various factor combinations influence NO2 concentration. The main findings are as follows: Under COVID-19 lockdown measures, prolonged vehicle waiting times and a shift towards family car usage among the public have led to a significant increase in NO2 concentration at highway toll stations. Promoting the Electronic Toll Collection (ETC) system and encouraging public transportation are of great importance. The synergistic effects of COVID-19 lockdown policies and urban land area, resulting in the reduction in the number of family cars and the excellent air circulation conditions in large cities, have contributed to the decrease in NO2 concentration at highway toll stations. Increasing urban green spaces and promoting clean energy vehicles are crucial for advancing urban sustainable development. The combined analysis of the region area and the number of family cars indicates that a higher proportion of large vehicles contributes to improving transportation efficiency, but also results in elevated NO2 concentration at highway toll stations due to diesel emissions. Optimizing the transportation structure and reducing reliance on large vehicles are of significant importance. Full article
(This article belongs to the Special Issue Recent Advances in Mobile Source Emissions (2nd Edition))
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22 pages, 16392 KiB  
Article
Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
by Zuoyu Chai, Tanghong Ran and Min Xu
Appl. Sci. 2025, 15(1), 364; https://doi.org/10.3390/app15010364 - 2 Jan 2025
Viewed by 1280
Abstract
Highway toll stations are equipped with electronic toll collection (ETC) lanes and manual toll collection (MTC) lanes. It is anticipated that connected autonomous vehicles (CAVs), MTC human-driven vehicles (MTC-HVs), and ETC human-driven vehicles (ETC-HVs) will coexist for a long time, sharing toll station [...] Read more.
Highway toll stations are equipped with electronic toll collection (ETC) lanes and manual toll collection (MTC) lanes. It is anticipated that connected autonomous vehicles (CAVs), MTC human-driven vehicles (MTC-HVs), and ETC human-driven vehicles (ETC-HVs) will coexist for a long time, sharing toll station infrastructure. To fully leverage the congestion reduction potential of ETC, this paper addresses the problem of ETC lane allocation at toll stations under heterogeneous traffic flows, modeling it as a mixed-integer nonlinear bilevel programming problem (MINLBP). The objective is to minimize total toll station travel time by optimizing the number of ETC lanes at station entrances and exits while considering ETC-HVs’ lane selection behavior based on the user equilibrium principle. As both upper-level and lower-level problems are convex, the bilevel problem is transformed into an equivalent single-level optimization using the Karush–Kuhn–Tucker (KKT) conditions of the lower-level problem, and numerical solutions are obtained using the commercial solver Gurobi. Based on surveillance video data from the Liulin toll station (Lianhuo Expressway) in Zhengzhou, China, numerical experiments were conducted. The results illustrate that the proposed method reduces total vehicle travel time by 90.44% compared to the current lane allocation scheme or the proportional lane allocation method. Increasing the proportion of CAVs or ETC-HVs helps accommodate high traffic demand. Dynamically adjusting lane allocation in response to variations in traffic arrival rates is proven to be a more effective supply strategy than static allocation. Moreover, regarding the interesting conclusion that all ETC-HVs choose the ETC lanes, we derived the relaxed analytical solution of MINLBP using a parameter iteration method. The analytical solution confirmed the validity of the numerical experiment results. The findings of this study can effectively and conveniently guide lane allocation at highway toll stations to improve traffic efficiency. Full article
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23 pages, 1663 KiB  
Article
Research on the Construction of an Evaluation Index System for Onboard Equipment Status Based on Real-Time Data Analysis
by Fumin Zou, Ting Ye and Feng Guo
Information 2025, 16(1), 15; https://doi.org/10.3390/info16010015 - 31 Dec 2024
Viewed by 888
Abstract
The research explores methods for onboard real-time data analysis and health status assessment, focusing on the working principles and status assessment theory of onboard equipment in the electronic toll collection (ETC) system. By applying AHP, fuzzy comprehensive evaluation method, entropy method, and Delphi [...] Read more.
The research explores methods for onboard real-time data analysis and health status assessment, focusing on the working principles and status assessment theory of onboard equipment in the electronic toll collection (ETC) system. By applying AHP, fuzzy comprehensive evaluation method, entropy method, and Delphi method, a practical health status assessment model for onboard equipment has been established, providing an innovative path for dynamic real-time monitoring of onboard devices. The research first constructs an indicator system and assessment model, combining expert scoring with fuzzy judgment to objectively quantify the status of onboard equipment, ultimately resulting in a health status assessment indicator system for the objects under evaluation. During the assessment process, expert scoring is used to determine the subjective weights via AHP, and the combined weights are derived by incorporating objective weights, with instance results showing that the comprehensive weights of various indicators lie between the subjective and objective weights. The research also utilizes the Weibull distribution to simulate the failure rate of onboard equipment, indicating that the failure modes of the onboard devices are closely related to their operational status, providing a theoretical basis for future technological improvements. Through empirical analysis of the assessment model, the research verifies the superiority of the comprehensive weights and emphasizes its new approach in health status assessment of onboard devices, opening up new avenues for research in related fields. Full article
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19 pages, 6962 KiB  
Article
Impacts of a Toll Information Sign and Toll Lane Configuration on Queue Length and Collision Risk at a Toll Plaza with a High Percentage of Heavy Vehicles
by Farnaz Zahedieh and Chris Lee
Vehicles 2024, 6(3), 1249-1267; https://doi.org/10.3390/vehicles6030059 - 23 Jul 2024
Cited by 2 | Viewed by 1552
Abstract
This study assessed the impacts of a toll information sign with different toll lane configurations on queue length and collision risk at a toll plaza with an estimated high percentage of heavy vehicles (HVs). The toll information sign displays information about different toll [...] Read more.
This study assessed the impacts of a toll information sign with different toll lane configurations on queue length and collision risk at a toll plaza with an estimated high percentage of heavy vehicles (HVs). The toll information sign displays information about different toll payment methods for cars and HVs upstream of the toll booth. The impacts were assessed for the toll plaza of the Gordie Howe International Bridge under construction at the Windsor–Detroit international border crossing using a traffic simulation model. Results show that the toll information sign upstream of the toll plaza and converting the toll lanes with multiple toll payment methods to electronic toll collection (ETC)-only lanes reduced queue length and collision risk. However, increasing the number of HV-only lanes for a higher percentage of HVs increased lane-change collision risk. Thus, it is recommended that toll lane configurations be changed based on the percentage of HVs to reduce collision risk at a toll plaza. Full article
(This article belongs to the Special Issue Emerging Transportation Safety and Operations: Practical Perspectives)
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30 pages, 7464 KiB  
Article
Expressway Vehicle Arrival Time Estimation Algorithm Based on Electronic Toll Collection Data
by Shukun Lai, Hongke Xu, Yongyu Luo, Fumin Zou, Zerong Hu and Huan Zhong
Sustainability 2024, 16(13), 5581; https://doi.org/10.3390/su16135581 - 29 Jun 2024
Cited by 1 | Viewed by 1750
Abstract
Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time [...] Read more.
Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time state perception of vehicles on expressways, we propose an algorithm to estimate the arrival time of vehicles in the next segment using Electronic Toll Collection (ETC) data. Firstly, the characteristics of ETC data and GPS data are meticulously described. We devise algorithms for data cleaning and fusion, subsequently segmenting the vehicle journey into multiple sub-segments. In the following step, feature vectors are constructed from the fused data to detect service areas and analyze the expressway segment characteristics, vehicle traits, and the influence of service areas. Finally, an algorithm utilizing LightGBM is introduced for estimating the arrival time of vehicles at various segments, corroborated by empirical tests using authentic traffic data. The MAE of the algorithm is recorded as 20.1 s, with an RMSE of 32.6 s, affirming its efficacy. The method proposed in this paper can help optimize transportation systems for improving efficiency, alleviating congestion, reducing emissions, and enhancing safety. Full article
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23 pages, 4899 KiB  
Article
Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
by Yikang Rui, Yan Zhao, Wenqi Lu and Can Wang
Sensors 2024, 24(1), 86; https://doi.org/10.3390/s24010086 - 23 Dec 2023
Viewed by 1501
Abstract
The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased [...] Read more.
The deployment of Electronic Toll Collection (ETC) gantry systems marks a transformative advancement in the journey toward an interconnected and intelligent highway traffic infrastructure. The integration of these systems signifies a leap forward in streamlining toll collection and minimizing environmental impact through decreased idle times. To solve the problems of missing sensor data in an ETC gantry system with large volumes and insufficient traffic detection among ETC gantries, this study constructs a high-order tensor model based on the analysis of the high-dimensional, sparse, large-volume, and heterogeneous characteristics of ETC gantry data. In addition, a missing data completion method for the ETC gantry data is proposed based on an improved dynamic tensor flow model. This study approximates the decomposition of neighboring tensor blocks in the high-order tensor model of the ETC gantry data based on tensor Tucker decomposition and the Laplacian matrix. This method captures the correlations among space, time, and user information in the ETC gantry data. Case studies demonstrate that our method enhances ETC gantry data quality across various rates of missing data while also reducing computational complexity. For instance, at a less than 5% missing data rate, our approach reduced the RMSE for time vehicle distance by 0.0051, for traffic volume by 0.0056, and for interval speed by 0.0049 compared to the MATRIX method. These improvements not only indicate a potential for more precise traffic data analysis but also add value to the application of ETC systems and contribute to theoretical and practical advancements in the field. Full article
(This article belongs to the Special Issue Regeneration Control, Sensing and Digital Twin of Eco-Environment)
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37 pages, 5728 KiB  
Article
Dynamic Identification Method for Potential Threat Vehicles beyond Line of Sight in Expressway Scenarios
by Fumin Zou, Chenxi Xia, Feng Guo, Xinjian Cai, Qiqin Cai, Guanghao Luo and Ting Ye
Appl. Sci. 2023, 13(23), 12899; https://doi.org/10.3390/app132312899 - 1 Dec 2023
Viewed by 1659
Abstract
Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to [...] Read more.
Due to the challenge of limited line of sight in the perception system of intelligent driving vehicles (cameras, radar, body sensors, etc.), which can only perceive threats within a limited range, potential threats outside the line of sight cannot be fed back to the driver. Therefore, this article proposes a safety perception detection method for beyond the line of sight for intelligent driving. This method can improve driving safety, enabling drivers to perceive potential threats to vehicles in the rear areas beyond the line of sight earlier and make decisions in advance. Firstly, the electronic toll collection (ETC) transaction data are preprocessed to construct the vehicle trajectory speed dataset; then, wavelet transform (WT) is used to decompose and reconstruct the speed dataset, and lightweight gradient noosting machine learning (LightGBM) is adopted to train and learn the features of the vehicle section speed. On this basis, we also consider the features of vehicle type, traffic flow, and other characteristics, and construct a quantitative method to identify potential threat vehicles (PTVs) based on a fuzzy set to realize the dynamic safety assessment of vehicles, so as to effectively detect PTVs within the over-the-horizon range behind the driver. We simulated an expressway scenario using an ETC simulation platform to evaluate the detection of over-the-horizon PTVs. The simulation results indicate that the method can accurately detect PTVs of different types and under different road scenarios with an identification accuracy of 97.66%, which verifies the effectiveness of the method in this study. This result provides important theoretical and practical support for intelligent driving safety assistance in vehicle–road collaboration scenarios. Full article
(This article belongs to the Special Issue Vehicle Safety and Crash Avoidance)
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14 pages, 5909 KiB  
Communication
The Design of a Circularly Polarized Antenna Array with Flat-Top Beam for an Electronic Toll Collection System
by Tianfan Xu, Mengchi Xu and Xiao Cai
Sensors 2023, 23(23), 9388; https://doi.org/10.3390/s23239388 - 24 Nov 2023
Cited by 2 | Viewed by 1570
Abstract
Electronic toll collection (ETC), known as a non-stop toll collection system which can automatically realize payment by setting the identification antenna at the entrance, is always suffering from information exchange interruption caused by beam switching. A circularly polarized sector beam antenna array operating [...] Read more.
Electronic toll collection (ETC), known as a non-stop toll collection system which can automatically realize payment by setting the identification antenna at the entrance, is always suffering from information exchange interruption caused by beam switching. A circularly polarized sector beam antenna array operating at 5.8 GHz with flat-top coverage is proposed, based on the weighted constrained method of the maximum power transmission efficiency (WCMMPTE). By setting the test receiving antennas at the specific angles of the ETC antenna array to be designed, constraints on the received power are introduced to control the radiation pattern and obtain the optimized distribution of excitations for antenna elements. A 1-to-16 feeding network, based on the microstrip transmission line theory is designed to feed a 4 × 4 antenna array. Simulation results show that the half-power beamwidth covers an angular range of −30° to 30° while the axial ratio is below 3dB, which meets the ETC requirements. Furthermore, the gain fluctuation among the needed range of −30° to 30° is lower than 0.7 dB, which is suitable for the ETC system to achieve a stable signal strength and uninterrupted communication. Full article
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26 pages, 3501 KiB  
Article
An Expressway ETC Missing Data Restoration Model Considering Multi-Attribute Features
by Fumin Zou, Zhaoyi Zhou, Qiqin Cai, Feng Guo and Xinyi Zhang
Sensors 2023, 23(21), 8745; https://doi.org/10.3390/s23218745 - 26 Oct 2023
Cited by 2 | Viewed by 1678
Abstract
Electronic toll collection (ETC) data mining has become one of the hotspots in the research of intelligent expressway extension applications. Ensuring the integrity of ETC data stands as a critical measure in upholding data quality. ETC data are typical structured data, and although [...] Read more.
Electronic toll collection (ETC) data mining has become one of the hotspots in the research of intelligent expressway extension applications. Ensuring the integrity of ETC data stands as a critical measure in upholding data quality. ETC data are typical structured data, and although deep learning holds great potential in the ETC data restoration field, its applications in structured data are still in the early stages. To address these issues, we propose an expressway ETC missing transaction data restoration model considering multi-attribute features (MAF). Initially, we employ an entity embedding neural network (EENN) to automatically learn the representation of categorical features in multi-dimensional space, subsequently obtaining embedding vectors from networks that have been adequately trained. Then, we use long short-term memory (LSTM) neural networks to extract the changing patterns of vehicle speeds across several continuous sections. Ultimately, we merge the processed features with other features as input, using a three-layer multilayer perceptron (MLP) to complete the ETC data restoration. To validate the effectiveness of the proposed method, we conducted extensive tests using real ETC datasets and compared it with methods commonly used for structured data restoration. The experimental results demonstrate that the proposed method significantly outperforms others in restoration accuracy on two different datasets. Specifically, our sample data size reached around 400,000 entries. Compared to the currently best method, our method improved the restoration accuracy by 19.06% on non-holiday ETC datasets. The MAE and RMSE values reached optimal levels of 12.394 and 23.815, respectively. The fitting degree of the model to the dataset also reached its peak (R2 = 0.993). Meanwhile, the restoration stability of our method on holiday datasets increased by 5.82%. An ablation experiment showed that the EENN and LSTM modules contributed 7.60% and 9% to the restoration accuracy, as well as 4.68% and 7.29% to the restoration stability. This study indicates that the proposed method not only significantly improves the quality of ETC data but also meets the timeliness requirements of big data mining analysis. Full article
(This article belongs to the Special Issue Advanced Sensing Technology in Intelligent Transportation Systems)
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21 pages, 3633 KiB  
Article
Dynamic Anomaly Detection in Gantry Transactions Using Graph Convolutional Network-Gate Recurrent Unit with Adaptive Attention
by Fumin Zou, Yue Xing, Qiang Ren, Feng Guo, Zhaoyi Zhou and Zihan Ye
Appl. Sci. 2023, 13(19), 11068; https://doi.org/10.3390/app131911068 - 8 Oct 2023
Viewed by 1660
Abstract
With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph [...] Read more.
With the wide application of Electronic Toll Collection (ETC) systems, the effectiveness of the operation and maintenance of gantry equipment still need to be improved. This paper proposes a dynamic anomaly detection method for gantry transactions, utilizing the contextual attention mechanism and Graph Convolutional Network-Gate Recurrent Unit (GCN-GRU) dynamic anomaly detection method for gantry transactions. In this paper, four different classes of gantry anomalies are defined and modeled, representing gantries as nodes and the connectivity between gantries as edges. First, the spatial distribution of highway ETC gantries is modeled using the GCN model to extract gantry node features. Then, the contextual attention mechanism is utilized to capture the recent patterns of the dynamic transaction graph of the gantries, and the GRU model is used to extract the time-series characteristics of the gantry nodes to dynamically update the gantry leakage. Our model is evaluated on several experimental datasets and compared with other commonly used anomaly detection methods. The experimental results show that our model outperforms other anomaly detection models in terms of accuracy, precision, and other evaluation values of 99%, proving its effectiveness and robustness. This model has a wide application potential in real gantry detection and management. Full article
(This article belongs to the Special Issue Intelligent Manufacturing and Medical-Engineering Integration)
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30 pages, 3307 KiB  
Article
Dynamic Generation Method of Highway ETC Gantry Topology Based on LightGBM
by Fumin Zou, Weihai Wang, Qiqin Cai, Feng Guo and Rouyue Shi
Mathematics 2023, 11(15), 3413; https://doi.org/10.3390/math11153413 - 4 Aug 2023
Cited by 5 | Viewed by 2062
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Applied Statistical Modeling and Data Mining)
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28 pages, 12094 KiB  
Article
Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data
by Xiaoyu Cai, Yihan Zhang, Xin Zhang and Bo Peng
Sustainability 2023, 15(15), 11619; https://doi.org/10.3390/su151511619 - 27 Jul 2023
Cited by 1 | Viewed by 1606
Abstract
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 [...] Read more.
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. Full article
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19 pages, 8913 KiB  
Article
Uncovering Equity and Travelers’ Behavior on the Expressway: A Case Study of Shandong, China
by Rong Cao, Xuehui Chen, Jianmin Jia and Hui Zhang
Sustainability 2023, 15(11), 8688; https://doi.org/10.3390/su15118688 - 27 May 2023
Cited by 2 | Viewed by 1864
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Promotion and Optimization toward Sustainable Urban Logistics Systems)
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13 pages, 2094 KiB  
Article
A Traffic Crash Warning Model for BOT E-Tolling Operations Based on Predictions Using a Data Association Framework
by Sheng-Chih Ho, Kuo-Chi Yen, Chung-Yung Wang and Yu Sun
Appl. Sci. 2023, 13(10), 5973; https://doi.org/10.3390/app13105973 - 12 May 2023
Cited by 1 | Viewed by 1961
Abstract
As a result of the increasing use of artificial intelligence technology in transportation, numerous real-time crash prediction techniques have been developed. In the context of highway traffic management, machine learning models and classifiers are used to analyze electronic toll collection (ETC) and vehicle [...] Read more.
As a result of the increasing use of artificial intelligence technology in transportation, numerous real-time crash prediction techniques have been developed. In the context of highway traffic management, machine learning models and classifiers are used to analyze electronic toll collection (ETC) and vehicle detector (VD) data to predict crash occurrences. However, traffic accidents are influenced by multiple factors, such as traffic speed differences, traffic density, and weather conditions, and direct associations may not exist between sensor data and crash incidents. Therefore, data integration and association methods must be used to examine ETC and VD data through traffic flow theories, to extract key data from datasets and to facilitate model training. In this study, a data association method and framework combined with deep learning was proposed to construct a crash prediction and warning model for national highways in Taiwan. The results revealed a model accuracy of 94%, indicating that the model had a low error rate and was suitable for the prediction of traffic accidents. Overall, this study provides referential data for the Freeway Bureau of Taiwan to conduct comprehensive assessments and develop strategies for crash prevention. Full article
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