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Millimeter wave (mmWave)-based orthogonal frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission. To meet communication and sensing requirements, many works propose a static configuration where the wave's hyperparameters such as the number of symbols in a frame and the number of frames in a communication slot are already predefined. However, two facts oblige us to redefine the problem, 1) the environment is often dynamic and uncertain, and 2) mmWave is severely impacted by wireless environments. A striking example where this challenge is very prominent is autonomous vehicle (AV). Such a system leverages integrated sensing and communication (ISAC) using mmWave to manage data transmission and the dynamism of the environment. In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing. This enables the AV to achieve two primary objectives: establishing a stable communication link with other AVs and accurately estimating the velocities of surrounding objects with high resolution. The communication performance is therefore evaluated based on the queue state, the effective data rate, and the discarded packets rate. In contrast, the effectiveness of the sensing is assessed using the velocity resolution. In addition, we exploit adaptive OFDM techniques for dynamic modulation, and we suggest a reward function that leverages the age of updates to handle the communication buffer and improve sensing. The system is validated using advantage actor-critic (A2C) and proximal policy optimization (PPO). Furthermore, we compare our solution with the existing design and demonstrate its superior performance by computer simulations.

GNSS-independent localization is one of the most prominent research problems in aerial autonomous systems navigation, especially in certain applications where Simultaneous Localization and Mapping (SLAM) methods are inapplicable due to the complexity of the environment, or in open-air spaces where a flock of Autonomous Aerial Vehicles (AAVs) navigate in a GNSS-independent fashion. This paper introduces a filter through which AAVs form a multi-agent Cellular Vehicle-to-Everything (C-V2X) network to exchange their estimated positions, and eventually achieve a group consensus over the true position of each vehicle. The localization error correction takes place in the filter with reference to the AAV’s relative range from neighbouring vehicles, that is measured by onboard ranging devices. It is shown that in ideal situations where rangefinder errors can be neglected, cooperative localization yields perfect localization, if the network is sufficiently large and sufficiently connected. It is also shown that the accuracy of cooperative localization is superior to the existing least-mean-square-error based techniques, where a centralized controller augments the positioning accuracy of the flock. Cooperative localization is also favourable due to the fact that the process is computationally affordable and fully distributed. Theoretical derivations and results have been validated through case studies and Monte Carlo simulations, and suggest cooperative localization as a complementary navigation technique to odometery, and other advanced solutions that are available in the literature.

The evolution of Connected Vehicles (CVs) has introduced significant advancements in both in-vehicle and vehicle-edge platforms, creating a highly connected ecosystem. These advancements, however, have heightened exposure to cybersecurity risks. This work reviews emerging security challenges in the CV ecosystem from a new perspective, focusing on the integration of in-vehicle platforms such as the infotainment system and vehicle-edge platforms. By analyzing case studies such as Android Automotive, Message Queuing Telemetry Transport (MQTT), and the Robot Operating System (ROS), we identify the primary security threats, including malware attacks, data manipulation, and Denial of Service (DoS) attacks. The discussion extends to privacy concerns and the lack of trust-building mechanisms in CVs, highlighting how these gaps can be exploited. To mitigate these risks, we retrieve solutions drawn from the broader field of Internet of Things (IoT) security research, including Multi-Factor Authentication (MFA) and trust-based systems. The proposed framework aims to increase the trustworthiness of devices within the CV ecosystem. Finally, we identify future research directions in adaptive mechanisms and cross-domain security.

In autonomous vehicles (AV), sensor fusion methods have proven to be effective in merging data from multiple sensors and enhancing their perception capabilities. In the context of sensor fusion, the distinct strengths of multi-sensors, such as LiDAR, RGB, Thermal sensors, etc., can be leveraged to mitigate the impact of challenges imposed by extreme weather conditions. In this paper, we address multi-sensor fusion in AVs and present a comprehensive integration of a thermal sensor aimed at enhancing the cognitive robustness of AVs. Thermal sensors possess an impressive capability to detect objects and hazards that may be imperceptible to traditional visible light sensors. When integrated with RGB and LiDAR sensors, the thermal sensor becomes highly beneficial for detecting and locating objects in adverse weather conditions. The proposed deep learning-assisted multi-sensor fusion technique consists of two parts: (1) visual information fusion and (2) object detection using LiDAR, RGB, and Thermal sensors. The visual fusion framework employs a CNN (convolutional neural network) inspired by a domain image fusion algorithm. The object detection framework uses the modified version of the YoloV8 model, which exhibits high accuracy in real-time detection. In the YoloV8 model, we adjusted the network architecture to incorporate additional convolutional layers and altered the loss function to enhance detection accuracy in foggy and rainy conditions. The proposed technique is effective and adaptable in challenging conditions, such as night or dark mode, smoke, and heavy rain. The experimental results of the proposed method demonstrate enhanced efficiency and cognitive robustness compared to state-of-the-art fusion and detection techniques. This is evident from tests conducted on two public datasets (FLIR and TarDAL) and one private dataset (CUHK).

For decades, systems in the mobility sector have been maintained as discrete entities. It is imperative that they collaborate in order to enhance traffic safety and efficiency, while simultaneously reducing environmental pollution. The protocols employed in the formally separated ecosystems demonstrate disparate behaviors and data representations. In order to facilitate connectivity between these ecosystems, we propose the introduction of the Intelligent Transportation System Protocol Interoperability Evaluation (ITS-PIE). The evaluation method allows system architects and developers to analyze existing protocols in a six-step process. The initial step is the assessment of interdependencies, which identifies any formal or informal correlation between two protocol specifications. The second step is the formal classification of the protocols’ informal and formal descriptions, which emphasizes the specification’s similarities and differences. The formal comparison calculates the consensual parallelisms between the formal specifications. The data evaluation defines tooling to describe the specifications in a way that an automated transition can be conducted. The protocols’ architecture context evaluation displays the framework conditions. The final step is a situation-dependent interpretation. Two illustrative examples are provided herein to illustrate the advantages of the evaluation and to highlight the principal challenges that arise during the process.

Autonomous vehicles need to continuously analyse the driving context and establish a comprehensive understanding of the dynamic traffic environment. To ensure the safety and efficiency of their operations, it would be beneficial to have accurate predictions of surrounding vehicles’ future trajectories. AVs can adjust their motions proactively to improve road safety and comfort with such information. This paper proposes a novel approach to predict the future trajectories of interacting vehicles, through a model of potential spatial-temporal interactions. A unique kernel function that emphasises risk-awareness was developed to extract spatial dependencies. The established model was trained and evaluated with the publicly available Highway Drone Dataset and Intersection Drone Dataset. The performance of the developed model was assessed with eight state-of-the-art methods. An ablation study and safety analysis were also conducted to evaluate the proposed risk-awareness kernel function. Results show that the proposed model’s inference speed is over eight times faster than the commonly used LSTM-based models. It also achieves an improvement of over 8% in prediction accuracy when compared with the state-of-the-art model.

With the increasing computational demands of Internet of Things (IoT) applications, air-ground integrated networks (AGIN), leveraging the capabilities of Unmanned Aerial Vehicles (UAVs) and High-Altitude Platform (HAP), provides an essential solution to these challenges. In this paper, we propose a framework that facilitates local computing at IoT devices and offers the flexibility to offload tasks to aerial platforms when necessary. Specifically, we formulate a multi-objective optimization model aiming at simultaneously minimizing energy consumption and reducing task latency by adjusting control variables such as transmit power, offloading decisions, and UAV placement in a distributed network of IoT devices. Our proposed framework employs Deep Deterministic Policy Gradient (DDPG) techniques to dynamically optimize network operations, allowing for efficient real-time adjustments to network conditions and task demands. The performance of the proposed algorithm is compared to traditional algorithms, including the Whale Optimization Algorithm (WOA), Gradient Search with Barrier, and Bayesian Optimization (BO). Simulation results show that this approach significantly minimizes energy consumption and latency, outperforming conventional optimization methods. Additionally, scalability tests confirm that our framework can efficiently integrate an increasing number of IoT devices and UAVs.

Radio access network optimization is a critical task in current cellular systems. For this purpose, Minimization of Drive Test (MDT) functionality provides mobile operators with georeferenced network performance statistics to tune radio propagation models in re-planning tools. However, some samples in MDT traces contain critical location errors due to the user equipment's energy-saving, thus making MDT data filtering vital to guarantee an adequate performance of MDT-driven algorithms. Supervised Learning (SL) allows to train automatic systems for detecting abnormal MDT measurements by using a labeled dataset. Unfortunately, labeling MDT data is a labor-intensive task, that can be alleviated by using Self-Supervised Learning (SSL). This work presents a novel SSL method to detect MDT measurements with abnormal position information in road scenarios. For this purpose, a dataset is first labeled by combining unlabeled MDT traces from high-mobility users and freely available land use maps, and then an SL classifier is trained. Model assessment is carried out using MDT data collected in a live Long-Term Evolution (LTE) network. Performance analysis includes the comparison of six well-known SL algorithms and 3 different sets of input features aiming to improve model accuracy, generalizability, and explainability, respectively. Results show that considering predictors regarding positioning error increases model accuracy, whereas omitting this information allows to cover a wider range of terminals. Likewise, Shapley Additive exPlanations (SHAP) analysis proves that the use of high-level predictors significantly improves model explainability.

Abstract The evolution of transport technologies, marked by integrating connectivity and automation, has led to innovative approaches such as truck platooning. This concept involves linking multiple trucks through automated driving and vehicle-to-vehicle communication, promising to revolutionize the freight industry by enhancing efficiency and reducing operational costs. This systematic review explores the current state of truck platooning testing literature, focusing on simulator and on-road tests. The objective is to identify key scenarios and requirements for successfully developing and implementing the truck platooning concept. Following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA) guidelines, we searched the Web of Science and Scopus databases, leading to the inclusion of thirty pertinent articles encompassing simulation-based, on-road, and mixed-environment experiments. In addition to the type of testing environment, these articles were assorted into three groups corresponding to their main thematic scope, human-centered, technology-centered, and energy efficiency studies, each providing unique insights into core themes for the development of truck platooning. The results reveal a commonly preferred platoon formation consisting of three trucks maintaining a constant speed of 80 km/h and a stable distance of 10 m between them. Simulator-based studies have predominantly concentrated on human factors, examining driver behavior and interaction within the platooning framework. In contrast, on-road trials have yielded tangible data, offering a more technology-driven perspective and contributing practical insights to the field. While the literature on truck platooning has grown considerably, this review recognizes some limitations in the existing literature and suggests paths for future research. Overall, this systematic review provides valuable insights to the ongoing development of robust and effective truck platooning systems.

Abstract The e-commerce sector’s rapid expansion has led to an increase in delivery activities both within and across cities, fuelling the growth of the courier, express, and parcel (CEP) services. CEP service providers are crucial for the distribution of goods across all types of cities, especially for last-mile delivery. However, CEP service providers need innovative approaches for their last-mile distribution in small- and medium-sized cities to reduce transport costs and negative environmental impacts. For this reason, this paper analyses the quantitative impacts of logistics measures of CEP service providers for last-mile delivery in small- and medium-sized cities, especially the resulting transport costs and environmental impacts, in the framework of a case study for the investigation area of the Aachen city region. A simulation-based analysis was conducted using the agent-based transport simulation MATSim and the linked route optimisation Jsprit. The results revealed that electric trucks are not cost-effective as a stand-alone logistics measure for last-mile delivery in small- and medium-sized cities. However, combining electric trucks with other sustainable logistics measures, such as parcel shops and parcel lockers, results in a viable logistics measure for last-mile delivery. It is possible to reduce total transport costs by at least 5.4% and CO2 emissions by at least 61.1%. Hence, CEP service providers should replace diesel trucks with a mix of sustainable logistics measures for last-mile delivery in small- and medium-sized cities to achieve better operational efficiency and lesser environmental impacts.

Deep neural networks (DNNs) have been widely used for learning various wireless communication policies. While DNNs have demonstrated the ability to reduce the time complexity of inference, their training often incurs a high computational cost. Since practical wireless systems require retraining due to operating in open and dynamic environments, it is crucial to analyze the factors affecting the training complexity, which can guide the DNN architecture selection and the hyper-parameter tuning for efficient policy learning. As a metric of time complexity, the number of floating-point operations (FLOPs) for inference has been analyzed in the literature. However, the time complexity of training DNNs for learning wireless communication policies has only been evaluated in terms of runtime. In this paper, we introduce the number of serial FLOPs (se-FLOPs) as a new metric of time complexity, accounting for the ability of parallel computing. The se-FLOPs metric is consistent with actual runtime, making it suitable for measuring the time complexity of training DNNs. Since graph neural networks (GNNs) can learn a multitude of wireless communication policies efficiently and their architectures depend on specific policies, no universal GNN architecture is available for analyzing complexities across different policies. Thus, we first use precoder learning as an example to demonstrate the derivation of the numbers of se-FLOPs required to train several DNNs. Then, we compare the results with the se-FLOPs for inference of the DNNs and for executing a popular numerical algorithm, and provide the scaling laws of these complexities with respect to the numbers of antennas and users. Finally, we extend the analyses to the learning of general wireless communication policies. We use simulations to validate the analyses and compare the time complexity of each DNN trained for achieving the best learning performance and achieving an expected performance.

Radar is an essential technology for Advanced Driving Assistance Systems (ADAS), used to accurately localize objects even in unfavorable weather conditions. Most radar systems that are now being produced for ADAS provide either 3D or 4D point clouds, containing range, Doppler, azimuth, and elevation information for every detected point target. Out of all dimensions, the azimuth and elevation are estimated using more advanced algorithms than the ones generally used for range and Doppler. This is due to the restricted size of the aperture that can be safely mounted on a vehicle, hence the resolution must be enhanced digitally. When using advanced algorithms challenges such as precise antenna manufacturing are of significant importance, to avoid phase and gain mismatch between the antenna elements along with their inherent coupling. These negative effects lead to a significant degradation in the Direction of Arrival estimation. Super-resolution techniques such as MUSIC and CAPON are widely referenced, however their performance throughout prior work is evaluated in ideal environments and generally with multiple available data acquisition snapshots. In this paper we address the issues faced when applying such algorithms in a radar application and offer a solution based on linear prediction and spatial smoothing to enhance the performance of such algorithms.

Research into novel approaches like Machine Learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and highly actual misclassification case concerns gender. As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. There is a substantial amount of data on ML gender-bias in the literature. In these, the majority of the investigated cases are for ML branches like image or sound processing and text recognition. However, utilizing ML for driving style investigations is not an extensively researched area. In this work, a novel application for gender-based classification with bias-attenuation using anonymized driving data will be presented. Using data devoid of biometric and geographic information, the proposed pipeline distinguishes manifested binary genders with 80% accuracy for the drivers in the holdout data set. In addition, a method for sustainable algorithm selection and its extension to embedded applications, is proposed. An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden.

Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.

With the emergence of vehicles featuring advanced driver-assistance systems like adaptive cruise control (ACC) and additional automated driving functionalities, there has arisen a heightened potential for cyberattacks targeting these automated vehicles (AVs). While overt attacks that lead to collisions are more conspicuous, subtle attacks that slightly modify driving behaviors can cause widespread impacts, including increased congestion, fuel consumption, and crash risks without being easily detected. To address the detection of such attacks, we first present a traffic modeling framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, data poison attacks, and denial-of-service (DoS) attacks. Subsequently, we examine the consequences of these attacks on both singular vehicle dynamics (micro) and broader traffic flow patterns (macro). We introduce a new anomaly detection model based on generative adversarial networks (GAN) designed for the real-time pinpointing of such attacks using vehicle trajectory data. Numerical results are presented to show the effectiveness of our machine learning strategy in identifying cyberattacks on vehicles equipped with ACC. The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles.

Cell-free networks are expected to be a forthcoming (r)evolutionary step in the coming generation of mobile networks, the so called 6 G. While mobile infrastructure is often assumed to be deployed outdoor by the operators, reality is that most of the traffic has at least one of the communication ends located indoors. This paper introduces the problem of providing wireless service to a heterogeneous population made of indoor and outdoor users using an outdoor cell-free massive MIMO (CF-mMIMO) infrastructure. It is shown how the pervasive max-min criterion (in cell-free setups) that results in near-uniform quality-of-service to all users may lead to catastrophic consequences when some of the users happen to be indoor. This problem is analyzed in both communication directions, uplink and downlink, exposing the similarities and differences of these two scenarios. Direction-specific solutions are then provided that involve improving the channel estimation and connectivity of indoor users and modifying the power allocation so as to somehow compensate for the wall propagation indoor users have to endure. All the techniques introduced satisfy the scalability requirements thus making our proposal realistically implementable.

This research optimizes an electric vehicle (EV) sharing system for a university campus, focusing on different demand patterns and peak times within an Intelligent Transportation System (ITS) framework. The main objectives are to reduce the number of unserved demands and operational costs. A simulation model was developed in MATLAB, utilizing the Non-dominated Sorting Genetic Algorithm (NSGA-II), a powerful multi-objective optimization technique that balances conflicting objectives to achieve the best trade-offs for operational efficiency. In addition to conventional decision variables, dynamic dual relocation thresholds and charge levels are introduced as decision variables to enhance optimization. The study compares two scenarios: Equally Distributed Demand (EDD) and Non-Equally Distributed Demand (NEDD), customized for the University Putra Malaysia (UPM) campus. Findings indicate that the NEDD scenario, which concentrates on specific demand areas, effectively decreases unserved demands and operational costs. Additionally, a station-specific approach expanded the solution space, improving adaptability and resulting in notable reductions in operational costs and smaller but meaningful improvements in unserved demands, especially during peak periods. By setting station-specific relocation thresholds and charge levels, resources were deployed efficiently, minimizing unnecessary relocations. The use of dynamic values for dual relocation thresholds and charge-to-work levels further optimized the process, reducing operational costs significantly, with a lesser impact on unserved demands across both scenarios. This research offers valuable insights into the implementation of EV sharing systems in educational institutions, emphasizing the advantages of focused resource allocation and the integration of dynamic decision variables.

Abstract Recent applications of Mobility as a Service (MaaS) platforms have highlighted certain limitations in accurately identifying integrated mobility packages (MaaS bundles) that align with users’ needs and preferences while also supporting the financial viability of businesses. This study explores the interest of university community members in adopting and paying for MaaS bundles, aiming to determine whether their preferences differ from those of the general population commonly analyzed in existing research. The research method involved the design and administration of a survey instrument, resulting in 1949 completed computer-assisted web interviews collected between May and June 2023. The willingness to pay (WTP) for different MaaS bundles was estimated based on users’ responses to stated preference choice tree experiments. Results reveal that 45.2% of respondents showed no interest in any proposed MaaS bundle, 29.5% towards only one specific MaaS bundle. Conversely, 25.3% of respondents expressed interest in two or more MaaS bundles. Public transport (PT) pass holders showed a higher WTP for MaaS bundles compared to PT pass non-holders, with a WTP an additional 17.5–28.3% over what they currently pay for their PT pass. The findings reveal a positive attitude among university community members toward MaaS bundles, especially among PT pass holders. This underscores an opportunity for MaaS providers to target this group with tailored bundles that complement existing transport choices, thereby increasing user satisfaction and financial viability.

Abstract This paper presents the results of a demand forecasting study about the introduction of Urban Air Mobility (UAM) services in the metropolitan area of Milan (Italy). Demand forecasting is based on random utility mode choice models, which include factors related to the individuals’ perception of vertical take-off/landing and low altitude flying over urbanized areas. The use cases of UAM services include airport shuttles, intercity air connections, and “air-taxis”, i.e., UAM services for short-trips within the metropolitan area. Several scenarios have been considered based on the number of access points (“vertiports”) and UAM fare levels. The results indicate that airport shuttles have a modal share of trips to/from airports (for both business and leisure) in a range of 2-5%. They resulted to be more attractive than air-taxis, which have a modal share in a range of 1-3%. Furthermore, the probability of choosing UAM services for intercity travels decreases with the distance and the time required for access/egress to/from the vertiports, whose catchment areas are dependent on the level of service provided by competing modes (notably the presence of good railway and highway connections).

With the emergence of real-time applications, modern wireless networks have witnessed the use of Age of Information (AoI) as a critical metric for evaluating the timeliness of data delivery. This paper considers multi-user scheduling, extending beyond traditional single-user scheduling, to exploit the potential of Massive Multiple-Input Multiple-Output (mMIMO) systems for concurrent data delivery over imperfectly known channel state information (CSI). We propose a novel transmission scheduling framework that leverages the spatial multiplexing capabilities of mMIMO to minimize the AoI across multiple users. This results in a joint optimization of multi-user scheduling and power allocation problem for optimum data freshness in a wireless broadcast network. We handle the non-convexity of the resulting problem by utilizing successive convex approximation to specifically reformulate the binary/integer and non-convex constraints of the problem. Extensive simulations demonstrate superior performance of the proposed framework and its solution in terms of AoI compared to existing benchmarks.

Vehicular Ad Hoc Networks (VANET) represent an immense technological advancement enhancing connectivity among Vehicular Technology including vehicles and roadside infrastructure to ensure road safety and improve forthcoming transportation services. The effectiveness of safety applications depends on the reliability and consistency of periodically broadcasted real-time environmental and vehicle state information. However, insider threats arise when nodes with valid access credentials disseminate maliciously incorrect information. Existing misbehavior detection solutions are often static and lack the adaptability required for the dynamic nature of vehicular networks, leaving a gap in addressing sophisticated attacks such as Denial of Service (DoS), data replay, and Sybil attacks. To fill this gap, we propose a context-aware, data-driven misbehavior detection framework that allows each vehicle to perform plausibility and consistency checks on received messages. The Adaptive Misbehavior Detection Framework addresses critical security challenges within localized vehicles by incorporating dynamically computed parameters and confidence intervals to assess message integrity. To determine the presence of misbehavior, a weighted average approach effectively reduces the possibility of false positives. Simulation results demonstrate that our proposed mechanism significantly enhances detection performance against key misbehavior types, including false information dissemination, DoS, disruptive, and variants of Sybil attacks variants, outperforming existing benchmarks with the VeReMi extension dataset.

The very new evolution towards 6G networks necessitates a paradigm shift towards unified 3D network architectures, encompassing space, air, and ground segments. This paper outlines the conceptualization, challenges, and prospects of such a transformative architecture. We outline the foundational principles, drawn from standardization endeavors and cutting-edge research initiatives, to articulate the envisioned architecture poised to redefine network capabilities. Driven by the need to enhance capacity, increase data rates, support diverse mobility models, and facilitate heterogeneous connectivity, the conceptual framework of a unified 3D network is presented. The focus is on seamlessly integrating diverse network segments and fostering holistic network orchestration. In examining the technical challenges inherent to the realization of a unified 3D network, we outline our strategies to address mobility management, handover optimization, interference mitigation, and the integration of distributed physical layer concepts. Proposals encompass federated learning mechanisms, advanced beamforming techniques, and energy-efficient computational offloading strategies, aimed at enhancing network performance and resilience. Moreover, we outline compelling utilization scenarios and highlighted promising avenues for future research.

Cloud Radio Access Networks (CRAN) face a critical challenge due to the limited capacity of fronthaul links overwhelmed by massive data transmissions. This paper proposes a novel CRAN design that effectively tackles this challenge. Our approach combines three key elements: (1) Massive MIMO at the baseband unit to leverage large array gain and interference suppression; (2) a novel simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) that can both transmit and reflect signals concurrently, improving fronthaul capacity through energy splitting technique by enabling communication with remote radio heads serving multiple user equipments; and (3) a data compression technique by optimizing the quantization noise covariance matrix across remote radio heads, significantly reducing the fronthaul traffic load. We formulate a problem to maximize the overall network sum-rate by jointly optimizing transmit power, fronthaul capacity, beamforming vectors at RRHs, data compression, and STAR-RIS transmission-reflection coefficients. To address the nonconvexity of the resulting joint optimization problem, successive convexification along with alternating optimization technique are used to develop an iterative algorithm. Simulations demonstrate that our STAR-RIS-aided CRAN design surpasses conventional reflecting-only RIS aided CRAN by providing full-space coverage and thus offering more degrees-of-freedom compared to traditional RIS.

Smart charging for Electric Vehicles (EVs) is gaining traction as a key solution to alleviate grid congestion, delay the need for costly network upgrades, and capitalize on off-peak electricity rates. Governments are now enforcing the inclusion of smart charging capabilities in EV charging stations to facilitate this transition. While much of the current research focuses on managing voltage profiles, there is a growing need to examine harmonic emissions in greater detail. This study presents comprehensive data on harmonic distortion during the smart charging of eight popular EV models. We conducted an experimental analysis, measuring harmonic levels with charging current increments of 1A, ranging from the minimum to the maximum for each vehicle. The analysis compared harmonic emissions from both single and multiple EV charging scenarios against the thresholds for total harmonic distortion (THD) and individual harmonic limits outlined in power quality standards (e.g. IEC). Monte Carlo simulations were employed to further understand the behavior in multi-vehicle scenarios. The results reveal that harmonic distortion increases as the charging current decreases across both single and multiple vehicle charging instances. In case studies where several vehicles charge simultaneously, the findings show that as more EVs charge together, harmonic cancellation effects become more pronounced, leading to a gradual reduction in overall harmonic distortion. However, under worst-case conditions, the aggregate current THD can rise as high as 25%, with half of the tested vehicles surpassing the individual harmonic limits.

This paper presents a novel lower boundary for the coherence block (ChB) length in time-variant wireless channels. A rigorous estimation of the ChB length is important for the proper design of systems based on time division duplex-orthogonal frequency division multiplexing (TDD-OFDM). ChB length is especially relevant in the case of massive multiple input-multiple output (m-MIMO) systems, as it determines the overhead due to the massive channel estimation and, consequently, the spectral efficiency that can be achieved. The proposed boundary is based on a tractable propagation model, is related to easily obtainable channel parameters, and applicable to radio channels with temporal variation due to both the movement of the users and the movement of objects that surround them; including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and industrial Machine-to-Machine (M2M) communications.

The progression of low-carbon aviation policies and the maturation of electric vertical take-off and landing (eVTOL) technology have engendered considerable prospects for the advancement of short-haul intercity and intra-city transportation systems. To harness the potential of eVTOL travel in ameliorating transportation carbon emissions and alleviating ground transportation congestion, the judicious selection of optimal eVTOL stop sites emerges as a pivotal consideration. This study delineates a framework for the delineation of intra-city and short-distance inter-city eVTOL site selection predicated on comprehensive analysis of ground transportation system interconnections. The initial phase of the framework entails the identification of potential optimal take-off and landing sites through a multi-faceted assessment of factors encompassing vehicular and passenger traffic flows, regional economic dynamics, travel behavioral patterns, and prevailing eVTOL flight regulations across heterogeneous ground transportation networks. Employing an enhanced iteration of the K-means algorithm, this phase undertakes the clustering of optimal takeoff and landing locations, thereby discerning their spatial distribution to effectively alleviate ground traffic congestion while aligning with eVTOL vertical port requirements and airspace regulatory mandates. The second phase involves the establishment of a demand gravity model to validate the optimal take-off and landing coordinate sites of eVTOL and further assess a service index indicative of traffic flow optimization. The case shows that six optimal eVTOL take-off and landing locations have been discerned by our model within the Beijing-Tianjin-Xiong'an (Hebei) region. These locations are anticipated to yield a cumulative service index of 75,465 instances, thereby efficaciously mitigating travel pressure on ground transportation infrastructure.

Massive multiple-input multiple-output (MIMO) systems are critical technologies for the next generation of networks. In this field of research, new forms of deployment are emerging, such as extremely large-scale MIMO (XL-MIMO), in which the antenna array at the base station (BS) is of extreme dimensions. As a result, spatial non-stationary features emerge as users view just a section of the antenna array, known as the visibility regions (VRs). The XL-MIMO systems can achieve higher spectral efficiency, improve cell coverage, and provide significantly higher data rates than standard MIMO systems. It is a promising technology for future sixth-generation (6G) networks. However, due to the large number of antennas, linear precoding algorithms such as Zero-Forcing (ZF) and regularized Zero-Forcing (RZF) methods suffer from unacceptable computational complexity, primarily due to the required matrix inversion. This work aims to develop low-complexity precoding techniques for the downlink XL-MIMO system. These low-complexity linear precoding methods are based on Gauss-Seidel (GS) and Successive Over-Relaxation (SOR) techniques, which avoid calculating the complex matrix inversion and lead to stable linear precoding performance. To further enhance linear precoding performance, we incorporate the Chebyshev acceleration method with the SOR and GS methods, referred to as the Cheby-SOR and Cheby-GS methods. As these proposed methods require optimizing parameters, we create a deep unfolded network (DUN) to optimize the algorithm parameters. Our performance results demonstrate that the proposed method significantly reduces computational complexity from to $\mathcal {O}(K^{2})$, where $K$ represents the number of users. Moreover, our approach outperforms the original algorithms, requiring only a few iterations to achieve the RZF bit error rate (BER) performance.

Abstract The global outbreak of the COVID19 pandemic dramatically changed people’s life’s and their travel behavior in 2020. Consequently, capturing these changes accurately and providing valid annual transport statistics constitutes a tremendous challenge all over the world. Against this background and the lack of a single comprehensive source of data revealing the ground truth, we present a data fusion approach to provide valid annual transportation statistics for Germany during the COVID19 pandemic. Therefore, we adapted our existing model approach at generating annual, national statistics for Germany on passenger transport. Unlike in the existing model, we do not model the whole year as one, but divide the year into pandemic stages in order to model passenger transport demand as adequately as possible within each stage. Three travel surveys capturing the altered travel behavior in the different stages of the pandemic were used in order to adapt our passenger kilometers travelled (PKT) model, which bases on a cross-sectional national household travel survey which in many countries serves as the data basis for providing annual transportation statistics. The main results show a decline in the overall number of trips in 2020 in Germany of around a quarter and around a third less kilometers travelled compared to 2019. These changes in travel behavior differ considerably between different modes of transport, trip purpose and the different stages of the pandemic in 2020. The results produced were validated on the basis of other studies and further sources of data such as floating car data and automated count stations for bicycle traffic and ensure reliable passenger transport statistics in the years of the COVID19 pandemic.

In this paper, a novel decoding kernel method based on Successive Parzen Windows Interference Cancellation (SPWIC) is proposed for the Non-Orthogonal Multiple Access (NOMA) uplink. The procedure leverages on the diversity in both angle and received power at a 3D antenna combined with a Parzen Windows based decoding to achieve better interference cancellation, providing the decoding process with robustness against multi-user interference and user discrimination. This is specially convenient in vehicular scenarios in crowded cities. We have evaluated SPWIC in various scenarios and concluded that it outperforms the standard Successive Interference Cancellation (SIC) approach even in Multiple-Input Multiple-Output (MIMO) cases such that up to 9 users can be allocated on the same resources -as long as they are not too close to each other-. Although it is proposed for mmWave, it can be directly adapted to lower frequencies.

This paper presents a comprehensive hazard analysis, risk assessment, and loss evaluation for an Evasive Minimum Risk Maneuvering (EMRM) system designed for autonomous vehicles. The EMRM system is designed to improve collision avoidance and mitigate loss severity by drawing inspiration from professional drivers who perform aggressive maneuvers while maintaining stability for effective risk mitigation. Recent advances in autonomous vehicle technology demonstrate a growing capability for high-performance maneuvers. This paper discusses a comprehensive safety verification process and establishes a clear safety goal to enhance the validation of the testing. The study systematically identifies potential hazards and assesses their risks to overall safety and the protection of vulnerable road users. A novel loss evaluation approach is introduced that focuses on the impact of mitigation maneuvers on loss severity. In addition, the proposed mitigation integrity level can be used to verify the minimum-risk maneuver feature. This paper applies a verification method to evasive maneuvering, contributing to the development of more reliable active safety features in autonomous driving systems.

［Objective］ Under the integrated station-city-people development, the railway passenger transport hub is upgraded to a new type of urban space that provides emotional connection and sustenance while undertaking transportation and urban development functions. The service attributes and the passenger flow groups of the hubs have undergone significant changes. Therefore, it is necessary to study the functional characteristics and the passenger flow demands of the hubs under the integrated station-city-people development based on changes in the demand side. ［Method］ Based on the characteristics of the integrated station-city-people development in each stage and its influence on passenger flow of the hubs, the functional changes and the passenger flow composition of the new railway passenger hubs are analyzed, specifically including several parts such as the trend passenger flow based on transportation services, the derivative passenger flow induced by urban development, and the resilient passenger flow with dyna-mically adjusting supply. Based on the traditional four-stage method of traffic prediction, the prediction ideas and methods for passenger flow demand in each scenario are proposed, then verified and analyzed with Xi′an East Station as the study case. ［Result & Conclusion］ The research results show that the passenger flow at hubs presents a development trend, where the traffic function passenger flow is dominant in the early stage, the urban function passenger flow gradually increases, and the resilient passenger flow tends to be stable in the short term. With a comprehensive analysis of the above three parts of passenger flow, the total passenger flow of Xi’an East Hub at peak hours in the long term is tentatively predicted about 30 000 to 32 000 person-times per hour. Refining the prediction method of the passenger flow demands for each part of the hub can provide a scientific and reliable basis for the functional configuration, facility layout, and passenger transportation organization for the new-type hubs.

［Objective］ As an important yardstick for measuring technological innovation capabilities, patent is of great significance to the innovative development of rail transit equipment. Currently, the systematic evaluation of patent value in the field of rail transit equipment in China is still insufficient, and it is necessary to make an in-depth analysis of its innovation trends and value. ［Method］ The required patent data information is retrieved from the Dawell Innojoy Global Patent Database. Statistical methods are utilized to conduct an analysis of technological innovation in general and an analysis of the technological composition and evolution of patents in the field of rail transit equipment in China. Four patent indicators are adopted to evaluate the patent value of major applicants and technological fields based on the principal component analysis method. ［Result & Conclusion］ The technological innovation in China′s rail transit equipment presents a growing trend, but the collaboration needs to be improved. The direction of computational methods among the major technology categories is booming along with the wave of digitalization. High-value patents are distributed in the major technological fields, where universities hold patents of basically higher value as a whole than those enterprises do. The development trend of technological innovation in the field of rail transit equipment in China is steadily positive. In the future, the direction of high-value technological innovation can be the focus, and the cooperation among universities, enterprises and research institutions in production, education and research can be further deepened.

［Objective］ In order to effectively reduce the hazards from the collision of urban rail transit trains and thereby improve the safety of train operation, it is necessary to conduct research on the collision performance of urban rail transit trains during the operation process. ［Method］ Based on urban rail transit train collision scenarios and standards，a train collision energy management scheme is proposed. The finite element software is utilized to conduct simulation calculation of train collision scenarios, the speed, vehicle interface force, coupler force, vehicle deformation amount, stress and strain in trains collision are analyzed. ［Result & Conclusion］ During the collision with the train speed of 25 km/h, the maximum deformation in the passenger area of the vehicle would occur in Car 6. This elastic deformation could reach 12.2 mm, far less than that specified in EN 15227:2008+A1:2010 Railway applications-Crashworthiness requirements for urban rail transit vehicle bodies . The maximum plastic strain of the vehicle occurring at the ends of Car 6 is 4.7%, less than 10% as specified in EN 15227:2008+A1:2010. Therefore, the vehicle meets the requirements for the survival space specified in EN 15227:2008+A1:2010 during the collision. The maximum lifting height of the wheelset at the rear bogie of Car 5 reaches 10.5 mm, not exceeding 75% of the flange height, thereby without the risk of vehicle climbing. The proposed collision energy management scheme satisfies the requirements for vehicle crashworthiness in EN 15227:2008+A1:2010.

［Objective］ Currently, frequent underground engineering flooding accidents tend to cause serious economic losses and casualties. In case of flooding, manual sealing methods such as temporarily stacking sandbags or manual installation of waterproof baffles are usually used, with problems such as a longer time duty for staff, inconvenient use and delayed response, etc. Therefore, it is necessary to study safe, reliable and responsive waterproof and back-flow preventing measures for different scenarios such as underground engineering entrances and exits, underground connecting passages, in order to improve the overall waterproof capacity of underground engineering. ［Method］ Based on liquid level alarm control technology, a series of intelligent anti-flood equipment for underground engineering are proposed, mainly including full-section intelligent anti-flood door, intelligent drop type anti-flood door, intelligent uplifting anti-flood baffle, to establish a comprehensive protection system of underground engineering against flooding and waterlogging. ［Result & Conclusion］ The above-mentioned series of equipment are adopted in the metro projects of cities like Luoyang and Xiamen. Based on the progress of the current design projects in hand, it is foreseeable that the series of equipment will be promoted in more underground engineering projects, providing a safeguard for the safe development of cities.

［Objective］ Air compressor is one of the main sources for metro vehicle vibration excitation. Reasonable modal matching design for air compressor is conducive to reducing the vehicle body vibration and noise, thereby improving the riding comfort of the vehicle. ［Method］ A finite element model of air compressor and its frame is established by using ANSYS finite element software. The influence of the two-stage vibration reduction suspension method for metro vehicle air compressor on its modal parameters is calculated and analyzed, and the above-mentioned suspension method between air compressor and its frame is optimized in the design. ［Result & Conclusion］ For the air compressor with a two-stage vibration reduction system, unreasonable suspension method is likely to cause the modal frequencies of air compressor and its frame close to the excitation frequency of the compressor, thus resulting in resonance. When rubber is adopted for the first-stage vibration reduction between the motor end, the compressor end and the frame, and rigid connection adopted for the second-stage vibration reduction between the air compressor frame and the vehicle body mounting seat, the modal frequencies of air compressor and its frame can effectively avoid excitation frequency of the air compressor, preventing the violent vibration of air compressor and its frame caused by the coincidence of the excitation frequency and the modal frequency.

［Objective］ Research is conducted on the influence and significance of the excitation frequency of the dynamic stabilization device and the mechanical characteristics of rail fasteners on the operation effect of the dynamic track stabilizer in order to provide some reference for the operation of the dynamic stabilizer. ［Method］ Based on the multi-body system dynamics theory, a dynamic stabilization device-track dynamics model considering the mechanical characteristics of rail fasteners is established. A rigid-flexible coupling dynamics simulation model of the dynamic stabilization device-ballast bed is established by using the finite element method. With the excitation frequency of the dynamic stabilization device, the lateral stiffness and the lateral damping of the fastener as research factors, and the lateral acceleration response of the sleeper as the evaluation index of stable operation effect, a numerical simulation analysis is carried out to obtain the significance of the influencing factors for the dynamic stabilization operation effect. ［Result & Conclusion］ With the increase of the excitation force amplitude of the dynamic stabilization device, both the lateral displacement and the acceleration of the sleeper increase monotonically. When the excitation frequency gradually increases, the lateral displacement of the sleeper decreases monotonically, while the lateral acceleration first decreases and then increases. The obtained optimal excitation frequency range of the dynamic stabilization device is from 30 to 36 Hz. Increasing the lateral stiffness of the rail fastener leads to an increase of the sleeper lateral acceleration. Increasing the lateral damping of the rail fastener will reduce the sleeper lateral acceleration. Based on the response surface method, three factors influencing the sleeper lateral acceleration are excitation frequency, fastener lateral stiffness and fastener lateral damping in order of significance.

［Objective］ In view of the inconsistency in the construction schedule of the large underground integrated transportation hub in Xiong’an New Area, it is necessary to conduct in-depth research on the key design points of the reserved rail transit projects in the construction of above-mentioned transportation hub, in order to reduce the impact of secondary construction and lower the difficulties, risks and investments in later stages. ［Method］ The basic profiles of hubs such as Xiong’an Intercity Station, Xiaoli Station and Financial Island Station in Xiong’an New Area are introduced. The necessity of reserving rail transit projects in underground integrated transportation hub is analyzed. The design concepts for the above projects are summarized, and their key design points are extracted. ［Result & Conclusion］ Key design points for the reserved rail transit project in the transportation hub are as follows: The system types and vehicle formations shall be determined based on the planning conditions; Reasonable station locations and station types shall be decided with the idea of overall optimization; Station and city integration shall be used as a strategy to coordinate urban and traffic functions；The layout plan of the hub shall be improved with the concept of integrated design; Following the principle of intensive and appropriate development, the phased construction of the hub shall be studied; The reserved and pre-embedded measures shall be coordinated for detailed implementation.

［Objective］ The possible impact of the development of driverless car technology on future urban rail transit is explored, and the role of driverless cars in urban transportation systems as well as their impact on the existing urban transportation structure are analyzed. ［Method］ The traffic characteristics of driverless cars are outlined, the impact of driverless cars on urban transportation structure is analyzed, and the specific impact of driverless cars on urban rail transit is discussed in detail. ［Result & Conclusion］ Driverless cars can improve traffic efficiency and passenger experience, which may lead to a decrease in short-distance passenger flow in urban rail transit, but long-distance passenger flow still is still highly dependent on it. The popularization of driverless cars will prompt urban rail transit planning to pay more attention to speed and efficiency, optimization of transportation connection between the central city and satellite cities, and promotion of the interoperability and information interaction of various transportation modes’information systems, thereby enhancing the intensity of urban transportation integration. The development of driverless cars will trigger major changes in urban transportation, requiring urban planners and transportation managers to take corresponding measures to adapt to technological development.

［Objective］ With the proposal of a green and low-carbon development goal for urban rail transit in China, metro is facing a daily increasing pressure of energy saving and emission reduction. Since traction consumes the main energy in metro operation, it is necessary to conduct a study on the optimization of traction energy consumption. ［Method］ In consi-deration of the low train braking rate in the process of metro design and operation, an energy-saving operation strategy is proposed by appropriately increasing the train braking rate and lowering the maximum cruising speed under fixed interval running time condition, that is, under the timing condition. Based on the establishment of the train traction simulation calculation model, a calculation method under the timing condition is proposed. The feasibility of the model is verified through case simulation, and the impact of the braking rate on traction energy consumption is mainly analyzed. ［Result & Conclusion］ Simulation calculation is conducted on a flat and straight interval, results showing that under the timing condition, the larger the maximum cruising speed of the train is, the more sensitive it is to the change of the braking rate. In the initial period, the increase of train braking rate can obviously bring down the maximum cruising speed of the train, achieving the effect of reducing the traction energy consumption. The feasibility of the energy-saving strategy and its effect are further verified on a specific interval of Guangzhou Urban Rail Line 22. The results show that under the timing condition, when the braking rate on the interval is increased from 0.6 to 0.85, the maximum cruising speed of the train can be reduced from 141 km/h to 117 km/h, saving 30% of the energy consumption with a significant energy-saving effect. As a conclusion, the train braking rate can be appropriately increased in the metro design and operation to save traction energy consumption.

［Objective］ The rotary drilling process of bored cast-in-place piles will cause vibration to the existing underground buildings and structures. Therefore, it is necessary to study the vibration response of the strata and structures caused by the rotary drilling process. ［Method］ Taking a station foundation pit excavation project of Jinan Metro Line 4 as the background, through field tests and numerical simulation methods based on three-dimensional finite element models, the influence of soil layers and power pipe trenches vibration caused by rotary drilling construction is analyzed considering factors such as vibration source distance, burial depth, excavation depth, different power pipe trench positions, and the number of piles under simultaneous construction. ［Result & Conclusion］ The closer the soil and existing power pipe trench are to the vibration source, the greater the vibration effect they receive. The vibration effect on the soil and existing power pipe trenches can be ignored when the distance from the vibration source exceeds 6 m. The vibration effect on the soil shows a decreasing trend with the increase of burial depth. After the excavation depth reaches 12 m, the vibration impact on the shallow buried power pipe trench can be ignored. When the measuring point and the vibration source are at the same depth, the vibration impact at the measuring point is the greatest. With the decrease of the clearance between the two simultaneously-constructed piles, the vibration velocity peak value shows an increasing trend. When the net distance between two simultaneously-constructed piles is 10 m, the vibration coupling effect can be ignored.

［Objective］ In order to strengthen the status ma-nagement of urban rail transit line sections, it is necessary to predict the overall deterioration trend of the gauge in space. ［Method］ CEEMD (complementary ensemble empirical mode decomposition) theory is introduced to extract the IMF (intrinsic mode function) of the geometric alignment of the track section. The PSO (particle swarm optimization) algorithm is utilized to optimize the SVR (support vector regression machine) to train and test the extracted data after calibrating the optimal parameters of the prediction model. Thus, the CEEMD-PSO-SVR prediction model is constructed. The prediction model is tested with 1,128 sets of track inspection sample data within the upward track section from K12+134 to K15+743 on Shanghai Metro Line 16. ［Result & Conclusion］ Compared with the PSO-SVR model and the ARIMA (autoregressive integrated moving average) model, the CEEMD-PSO-SVR prediction model has advantages in three performance evaluation indicators, namely root mean square error, mean absolute error, and absolute value of mean relative error.

［Objective］ At present, the lack of monitoring means for the operation status of key components such as axle boxes, gearboxes, and traction motors in the daily operation of urban rail transit trains, makes it impossible to realize the fault early warning and tracking of the key components, and meet the requirements of precise maintenance of the trains. To adapt to the demands of intelligent operation and maintenance technology development, it is necessary to study and solve problems of the operation status monitoring and the fault early warning of key components. ［Method］ In view of the current application status of comprehensive monitoring technology for railway vehicles, it is proposed to set up a track side comprehensive monitoring equipment of the line based on photothermal imaging technology and acoustic diagnosis to collect the temperature and noise information of the key components du-ring train operation. Dynamic monitoring and fault diagnosis of the train key components is realized by establishing a fault judgment model. ［Result & Conclusion］ Photothermal imaging technology characterizes its detection as multi-points, large coverage, strong vehicle model adaptability, accuracy, and visual results. When combined with acoustic diagnosis and other technologies, it can realize real-time monitoring and fault diagnosis for the train key component operation status, providing technical support for precise maintenance of trains.

［Objective］ Drawing on the successful experiences of Japanese government in promoting the rail transit through subsidies can provide relevant cases of practical guiding significance for the establishment of an effective governmental subsidy mechanism in China at the stage of urban rail transit renewal and renovation. ［Method］ The rail transit-related subsidy systems successively introduced by Japan after the 1960s are taken as the research objects, and according to different business forms of Japan′s rail transit undertakings, Japan′s current subsidy systems are divided into four categories for urban railways, trunk railways, safety, disaster prevention and technology development, which have constituted a complete and systematic auxiliary mechanism. The subsidy purpose, subsidy objects and subsidy contents are studied and analyzed, and the subsidy management institutions and the subsidy procedures are introduced. ［Result & Conclusion］ Finally, combined with the realities in China, it is suggested that a financial subsidy system should be established for the renovation of existing lines, the management system be improved, and dynamic evaluation and adjustment be implemented.

［Objective］ Improving the turn-back capacity, a key indicator for evaluating the transportation efficiency of urban rail transit lines, is an important issue currently being faced. Therefore, it is of particular importance to conduct an in-depth analysis of the influence of urban rail transit turn-back forms and the related factors on turn-back capacity. ［Method］ In the case study of an actual operating urban rail transit line, a set of urban rail transit train operation simulation systems independently developed by the laboratory is adopted to calculate and analyze the turn-back capacity under various turn-back forms. According to the detailed process of turn-back operations, various factors affecting the turn-back interval are qualitatively analyzed, and the influence of the related factors on the turn-back capacity is studied. On this basis, optimization measures are put forward and quantitatively verified in specific types of station. The improvement capabilities and limiting conditions of each optimization measure are calculated under different turn-back forms. ［Result & Conclusion］ Under normal circumstances, the turn-back capacity of the train turning back behind the station is stronger than that in front of the station. The proposed optimization measures such as shortening the stop time, increasing the turnout lateral speed limit, lowering the requirements for train control comfort and parking accuracy, and shortening the train end-switching time have different improvement effects under different turn-back forms, and need to be flexibly selected according to the actual situation. The turn-back capacity is significantly improved with the train turn-back form based on vehicle-to-vehicle communication compared with the traditional one based on vehicle-to-ground communication.

［Objective］ Despite the current established rail transit networking operation in cities like Beijing, Shanghai and Guangzhou, usually the independent operation of each line leads to high pressure and uneven distribution of resources at transfer stations, it is necessary to draw on the experience of foreign cities in typical rail transit interconnection to provide references for the development mode of urban rail transit interconnection in Chinese cities. ［Method］ With the urban rail transit lines in cities like Tokyo, Paris and Karlsruhe as the research objects, the background and development modes of their interconnection are investigated and analyzed, the basic characteristics are summarized. ［Result & Conclusion］ As the typical urban rail transit interconnection modes in the above-mentioned cities emerge in different contexts, whether to adopt these modes or not should be decided based on the actual situation in China. The typical interconnection in foreign cities mainly exists among lines on the same passenger flow corridor, and basically not at the level of urban rail transit network. Therefore, in the implementation of interconnection, renovation of the existing operating lines should not be considered unless the renovation cost is low and the impact is small. The newly-built lines should use the technical standards that are consistent with those of the existing lines. In the interconnection of urban rail transit, dual-system trains are only suitable among existing rail transit lines with different systems in order to save costs.

［Objective］ The purpose of health status evaluation of the traction power supply equipment is to provide references for the operation, repair and maintenance of this equipment in urban rail transit. ［Method］ A health status evaluation model for urban rail transit traction transformer is established based on AHP (analytic hierarchy process) and EWM (entropy weight method). The hierarchical structure for evaluating the health status of the traction transformer is constructed based on the operation indicator data of the traction transformer. The subjective weights of the evaluation indicators are calculated by using AHP through the expert judgment matrix, and the weights of the test indicators are solved by using EWM and then combined with the subjective weights. The health status score of the traction transformer is calculated according to the evaluation indicators to determine its overall health status, thereby predict its remaining service life and evaluate the impact of different maintenance methods on the service life. With the monitoring data of the traction transformer in a certain traction substation as an example, the health status of the traction transformer is evaluated. ［Result & Conclusion］ The effectiveness of the proposed health status evaluation method for traction transformer is verified through examples. The evaluation method realizes unification of the subjective and objective factors of equipment status detection, and can be popularized and applied to the health status evaluation of other urban rail transit DC power supply equipment.

［Objective］ In the context of the current urban rail transit towards intelligent，green and low-carbon development, upgrading the traction power supply system, the "heart" of urban rail transit, is of crucial importance for realizing the green and smart development of urban rail transit. Therefore, it is necessary to conduct research on the future development path of the traction power supply system so as to promote its technological innovation and industrial upgrading. ［Method］ The cutting-edge technologies and innovative achievements of typical foreign countries, such as Germany, Japan and France in optimizing the configuration of rail transit traction power supply system and utilizing new energy and digital informatization are summarized. The innovative development path of urban rail transit traction power supply system in China is explored from three dimensions, namely promoting the application of innovative achievements, increasing the application of new energy, and establishing an intelligent management platform. ［Result & Conclusion］ The proposed innovative development path for urban rail transit traction power supply system can promote the upgrading of traction power supply system and further contribute to the green and intelligent development of urban rail transit.

［Objective］ Acceleration of the urbanization process and frequent occurrence of urban flood disasters impose higher requirements on the rainwater system of urban rail transit depots. It is necessary to conduct a detailed study on the schemes of the depot rainwater system. ［Method］ Based on the rainwater management concept by combining gray infrastructure and green infrastructure, the hydrological simulation software SWMM (storm water management model) is used to simulate the hydrological processes under various combination schemes for Yuhuazhai Depot of Xi’an Metro Line 3. A multi-objective optimization method is designed through the response surface method. Combined with the particle swarm optimization algorithm, the Matlab software is used to optimize the configuration of the green infrastructure within the depot in order to achieve effective control over the total runoff volume, peak flood, TSS (total suspended solids) and costs. ［Result & Conclusion］ Through multi-objective optimization and comparison, the optimal gray-green combination rainwater system scheme for Yuhuazhai Depot of Xi’an Metro is determined. In particular, the combined layout of permeable pavements and retention ponds should be given priority within the depot. This scheme not only meets the requirements of GB 51345—2018 Assessment Standard for Sponge City Construction Effect, but also maximizes the economic benefits.

［Objective］ In water-rich soft soil areas, it is difficult to control the deformation of the existing operating metro tunnel structure caused by the construction of the shield tunneling closely under-passing. Therefore, it is necessary to conduct research on corresponding protection measures. ［Method］ Based on a project of Nanjing Metro Line 5 shield tunneling closely under-passing the existing section of Nanjing Metro Line 1, the MIDAS/GTS NX finite element software is used to simulate the construction of the shield tunneling. Then, active protection measures for the existing tunnel and control measures for the shield tunneling of the newly-built tunnel are put forward. The vertical displacement and horizontal convergence of the existing tunnel during the shield under-passing are monitored with construction monitoring methods. ［Result & Conclusion］ The specific construction protection measures are as follows. Before the construction of shield tunneling under-passing, the affected sections of the existing tunnel are reinforced with measures such as micro-disturbance grouting, permanent circumferential supports, and longitudinal tension strips to effectively reduce the tunnel deformation and make it conform to the deformation control standards. During the construction, the earth pressure and the amount of excavated soil are adjusted in real time based on the settlement amount of the existing tunnel, and methods are adopted including synchronous grouting and the distribution of grouting amounts in the upper and lower holes to further control the tunnel deformation. After the completion of construction, against the post-construction settlement problem of the stratum, sufficient grouting material and equipment for reinforcing grouting are prepared inside the tunnel section where the shield tunneling underpasses, and subsequent grouting reinforcement is carried out according to the monitored settlement. The above measures can effectively reduce the impact of the shield tunneling under-passing construction on the existing tunnel.

［Objective］ Solve the problem of excavation surface stability during shield tunneling with pressure entry in shallow-buried sandy stratum.To solve the problem of the stability of tunnel face of shield tunnel with pressure into the chamber in shallow buried sandy soil stratum. ［Method］ The limit support pressure during the shield tunneling pressurized chamber entry operation is studied by combining numerical simulation, theoretical analysis and engineering verification methods. Firstly, the active failure phenomenon of shallow-buried sandy soil stratum is studied by numerical simulation, and the corresponding limit support pressure and failure zone shape are obtained. Then, based on the numerical simulation results, the failure mode applicable to shallow-buried sandy soil stratum is constructed, and the corresponding ultimate support pressure is solved. Finally, the model is applied in the Hangzhou Metro Line 5 pressurized inlet project, and the ground settlement is monitored. ［Result & Conclusion］ The limit support pressure increases approximately linearly with the increase of burial depth, and decreases approximately exponentially with the increase of internal friction angle; the instability mode of the shallow-buried stratum presents the shape of lower wedge and upper inverted cone/platform, in which the upper failure zone is further split into the damage core zone and disturbance zone. By comparing with the numerical model and the literature model, it is found that the proposed wedge + inverted cone/platform model can better describe the phenomenon that the damage zone can reach the ground surface due to the inability of the shallow-buried sandy stratum to form an arch; during the shield pressure entry construction of Hangzhou Metro Line 5 project, the maximum ground settlement value caused is 9.5 mm, and the influence range is within 10 m on both sides of the tunnel axis.