Editorial

Comprehensive review on smart highway evolution and research 2025: Preface

Xiaojing Wang

Review

Comprehensive review on smart highway evolution and research 2025: Evolution process and key technologies of smart highway system

Jian Gao^1,2,3, Lin Wang^1,2, Shuo Zhao^1,2, Shuyun Niu^1,2, Sheng Yin^1,2, Yuqi Guo^1,2, Yeran Huang^1,2, Jierui Zhu^1,2,3

Abstract：Smart highway refers to a system that comprehensively applies new-generation information technologies to achieve digital, networked, and intelligent upgrades of highway infrastructure, thereby significantly enhancing efficiency, safety, and sustainability. Although the importance is increasingly prominent, current studies focus on specific technologies or regional development analyses, lacking a systematic review and comparison of the global evolutionary trajectory of smart highways. By synthesizing the trajectories of representative countries and regions, the evolution of smart highways can be understood as proceeding through several stages, ranging from the early emergence and exploration, to the rise of ITS, and further to stages characterized by cooperative vehicle-infrastructure systems and digital development. The conceptual characteristics and technological connotations of various stages are investigated. The evolutionary process of system architectures across countries is analyzed, through which a development trend is revealed toward a physical hierarchy structured around the cloud-edge-end paradigm and a logical hierarchy centered on sensing-communication-computing-application. On this basis, key enabling technologies are summarized in the domains of sensing, control, safety, and vehicle-infrastructure cooperation. The findings are expected to contribute to a more comprehensive understanding of the concepts, architectures, and technological evolution of smart highways, while providing the references for technical road mapping, standard publishing, and large-scale deployment.

Perspective

Innovation of digital transportation’s basic theories

Bingqi Zhang

Abstract：Humanity is entering a digital society, and transportation systems are undergoing a phase of transformation. This paper summarizes the developmental trajectory of transportation, analyzes the necessity of researching the basic theories of digital transportation, and proposes a preliminary definition of digital transportation from perspectives including productivity, production relations, transportation scope, traffic scope, management approach, system composition. It describes core characteristics and identifies key research directions for future focus, aiming to stimulate academic discourse and advance basic theoretical research in this field.

Research Article

New generation intelligent operation and control management system enhancing metro station operations

Jiabin Zhu

Abstract：This article discusses the need for digitalization, as well as the concept and features of a next-generation intelligent operation and control management system （IOCMS） based on digitalization. It highlights the differences between this new system and the existing standard architecture of rail transit stations, and summarizes the advantages of the new intelligent operation and control management system. The feasibility of digitally upgrading rail transit station operations is analyzed, and a system architecture plan for the intelligent management platform is proposed. Several key technologies of the intelligent operation and control management system are explored, and major innovative business aspects are listed. By conducting a comparative analysis before and after the digitalization upgrade of rail transit operations, a brief evaluation of the digitalization upgrade is presented. Finally, other aspects to consider in the construction of the new generation digitized intelligent operation and control management system are summarized.

GNTI: Gaussian noise-based trajectory imputation via self-supervised learning

Siqi Liu, Penghao Zhao, Lei Dong1, Na Dong, Liyuan Ding, Guangshi Pei

Abstract：Trajectory imputation aims to reconstruct complete movement sequences from noisy or incomplete GPS data, crucial for intelligent transportation systems (ITS). This study proposes GNTI (Gaussian Noise-based Trajectory Imputation), a self-supervised learning framework that introduces Gaussian noise during model training to simulate real-world GPS errors. By perturbing the input trajectories with probabilistic Gaussian noise, GNTI enables the model to learn robust trajectory representations without relying on labeled datasets. A transformer-based BERT encoder is employed to capture complex spatial-temporal dependencies, while a simple multilayer perceptron (MLP) decoder predicts corrected trajectory points based on contextualized embeddings. Extensive experiments were conducted on two large-scale real-world datasets, Chengdu and Porto. Comparative results show that GNTI outperforms traditional Seq2Seq-based models (gated recurrent unit (GRU), long short-term memory (LSTM)) and recent transformer-based models (Transformer, ST-BerImp), achieving the highest Micro-F1 scores across all settings. Specifically, GNTI improves Micro-F1 scores by 3%–5% over ST-BerImp. Ablation studies demonstrate that Gaussian noise augmentation improves model robustness by approximately 5% compared to models trained without augmentation. GNTI offers a practical and scalable solution for trajectory imputation tasks, enhancing robustness to GPS inaccuracies and reducing the need for complex multi-task objectives. Future work may explore extending the method to denser urban environments and optimizing it for real-time deployment.

Automatic identification of violations in driver training based on geofence and geospatial analysis

Xin Zhang, Shuo Xu, Keqi Wu, Hui Xiao^2,4, Huapeng Shen, Houyong Wang, Yuanmeng Zhang, Xiaoliang Zhang

Abstract：To further improve the regulatory efficiency of the driver training industry and promote the development of “Internet + supervision” in the driver training industry, an off-site supervision method for the driver training industry based on geofence technology and geospatial analysis methods is studied. This method aims to automatically identify and comprehensively supervise whether training vehicles operate in accordance with specified routes and times. Through spatiotemporal matching and spatial mapping of multi-source heterogeneous data such as trajectory data of training vehicles from driving training institutions and geofence data, a multi-source dataset for industry supervision is established. Using the Shapely geospatial analysis library, based on the DE-9IM model, and combined with the multi-source data infrastructure, real-time supervision of training vehicles and automatic identification of violations are realized. The results show that the off-site supervision method proposed in this study can achieve precise supervision of the driving training industry, with a supervision accuracy rate as high as 99.87%. The identification results can serve as an important basis for relevant industry regulatory and law enforcement departments to carry out off-site supervision and early warning in the industry, and promote the intelligent transformation of off-site supervision in the driving training industry.

Machine-learning prediction model for bond strength evolution of corroded rebar-concrete interface

Meng Wang^1,3, Yichen Lian, Fei Xu^1,3, Qingyuan Meng, Guoqing Wang^1,5, Tong Shen^6,7, Xu Sun^1,5, Xinyu Zheng^1,3, Xuefeng Duan^1,3

Abstract：The mechanical performance of concrete structures under corrosive environments largely depends on the bond behavior between rebar and concrete. Existing studies primarily focus on predicting the peak bond stress, paying limited attention to the complete degradation process of bond strength. To address the deterioration of bond strength caused by internal rebar corrosion in concrete structures, a comprehensive bond–slip dataset was constructed based on extensive pull-out test data from existing literature. Nine input features were selected: corrosion rate, bond length, rebar diameter, concrete compressive strength (both cube and cylinder), concrete cover thickness, rebar yield strength, rebar type, and slip. This dataset captures the full evolution of bond strength at the corroded rebar–concrete interface as a function of slip. A bond–slip prediction model for corroded rebar was developed using a stacking GBDT–SVR (Gradient Boosting Decision Tree–Support Vector Regression) machine learning approach. Feature importance analysis was conducted using the SHAP method. The results showed a strong agreement between predicted and actual values. Performance metrics such as R2, σ, η, and γ confirmed the high accuracy of the model, with prediction outside 0.8–1.2 confidence band only at low bond stress values. Compared to traditional empirical formulas, the proposed model demonstrates superior precision.

Consolidation characteristics of permafrost foundational soil in Yellow River source along G214

Jianhua Yu, Lei Quan, Bo Tian, Lihui Li, Panpan Zhang, Sili Li

Abstract：This study conducted indoor consolidation tests on the aging road and natural ground foundational soils from the G214 permafrost section in the Yellow River source area to compare their consolidation characteristics. The results revealed that the consolidation deformation of the aging road foundation soil was significantly lower than that of the natural foundation soil, with a flatter consolidation curve. In the aging road foundation, the 2.5-4.3 m soil layer affected by annual temperature fluctuations exhibited void ratio variations 2.05 times greater than other layers. However, the variation in void ratio in multiple layers of the natural foundation was greater than that in the aging road foundation. The maximum settlement of the aging road foundation originated from the 2.5-4.3 m layer with smaller shallow settlement, while the natural foundation showed dominant shallow settlement followed by the 2.5-7.5 m layer, reaching a total settlement 1.37 times that of the aging road foundation. A layered treatment strategy is recommended: applying techniques adapted from soft foundation treatment to shallow layers while implementing thermal stability protection for permafrost in deeper layers, achieving a coordinated approach between deformation control and permafrost environmental protection.

Highway roadside unit deployment under uneven distribution evolution of intelligent connected vehicles

Jiyuan Zhou, Xiaolin Yu, Jian Geng, Ying Zhang, Luyu Zhang, Zhenhua Mou

Abstract：As the penetration rate of connected and autonomous vehicles (CAVs) increases on highways, their role as network nodes for communication tasks raises significant challenges. Spatial heterogeneity in node distribution creates density discrepancies that substantially impact network lifetime and stability, thereby constraining optimal deployment strategies for road side units (RSUs). This study introduces an enhanced low-energy adaptive clustering hierarchy (LEACH) clustering algorithm tailored for vehicular networks. By identifying dense and sparse regions through dynamic clustering, the algorithm categorizes node functions according to regional characteristics to balance energy consumption and improve network connectivity. MATLAB simulations validate the algorithm’s performance under non-uniform vehicle distributions.The research further analyzes how vehicle node distribution patterns and CAV penetration rates affect optimal RSU deployment intervals. Key findings reveal that with consistent RSU-vehicle communication ranges: At a CAV traffic density of 0.01 and relative spatial density of 0.5 (uniform distribution), RSUs should be deployed at 641 m intervals. At a relative density of 0.9 (concentrated distribution), deployment intervals can expand to 1,887 m while maintaining high network connectivity. This adaptive strategy reduces communication blind spots by 32%, lowers deployment costs by 18%, and enhances vehicle-road coordination efficiency and traffic safety. The results provide critical technical support for intelligent vehicle-road collaboration systems.

Journal Introduction

HTRD

The Journal of Highway and Transportation Research and Development (English Edition) (HTRD) was established in 2006 as a leading academic journal under the sponsor of the Research Institute of Highway, Ministry of Transport, published quarterly by the Journal of Highway and Transportation Research and Development Co., Ltd. As of 2024, the journal is co-published by Tsinghua University Press and has transitioned to an Open Access Journal. Articles co-published between 2006 and 2023 are available for access at https://ascelibrary.org/journal/jhtrcq.

HTRD is an international Englishscientific journal. HTRD’s topics include civil engineering (road, bridge,tunnel), traffic engineering, intelligent transportation, environmentalengineering, transport economics, automotive engineering, logistics engineering,disaster prevention, etc.

Sponsor: Research Institute of Highway, Ministry of Transport.

Co-publisher: Tsinghua University Press (2024-).

American Society of Civil Engineers (ASCE) Library (2012-2023).

Indexed: DOAJ, ProQuest, INSPEC, Ulrich, CA, AJ VINITI, WJCI, COPE.

期刊官網(wǎng)：

https://www.sciopen.com/journal/2095-6215

投稿網(wǎng)址：

https://mc03.manuscriptcentral.com/htrd

公路交通科技

中文刊：https://www.gljtkj.com/CN/home

英文刊：https://www.sciopen.com/journal/2095-6215

來(lái)源：雜志社

編發(fā)：辦公室

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